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Ding L, Zhao J, Yang Y, Bhuva MS, Dipendra P, Sun X. Prognostic implications of CT-defined ground glass opacity in clinical stage I-IIA grade 3 invasive non-mucinous pulmonary adenocarcinoma. Clin Radiol 2024; 79:e353-e360. [PMID: 38123396 DOI: 10.1016/j.crad.2023.10.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 09/19/2023] [Accepted: 10/24/2023] [Indexed: 12/23/2023]
Abstract
AIM To investigate the prognostic impact of computed tomography (CT)-defined ground glass opacity (GGO) in patients with clinical stage I-IIA grade 3 invasive non-mucinous pulmonary adenocarcinoma (INPA). MATERIALS AND METHODS The present study retrospectively enrolled 187 patients diagnosed with stage I-IIA grade 3 INPA. Their clinicopathological, radiological, and genetic information was evaluated systematically, and a 5-year follow-up was conducted to monitor disease recurrence and mortality. Patients were stratified based on the presence of a GGO component, and the Cox proportional hazard model was employed to assess the influence of clinicopathological factors and genetic variables on tumour outcomes. Recurrence-free survival (RFS) and overall survival (OS) were estimated using the Kaplan-Meier method and compared using the log-rank test. RESULTS Significant differences were observed in both OS and RFS based on the presence of a GGO component. The group with GGO exhibited superior OS (p=0.002) and RFS (p=0.029). Multivariate analysis revealed that the presence of a GGO component (hazard ratio [HR] = 0.412, 95% confidence interval [CI]: 0.177-0.959, p=0.040), clinical T2 stage (HR=2.473, 95% CI: 1.498-4.083, p<0.001), pathological N2 stage (HR=3.049, 95% CI: 1.800-5.167, p<0.001), and mixed high-grade patterns (HR=2.392, 95% CI: 1.418-4.036, p=0.001) were predictors of RFS. CONCLUSION The presence of a GGO component is strongly associated with a favourable prognosis in grade 3 INPA.
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Affiliation(s)
- L Ding
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, 507 Zheng Min Road, Shanghai 200433, China
| | - J Zhao
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, 507 Zheng Min Road, Shanghai 200433, China
| | - Y Yang
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, 507 Zheng Min Road, Shanghai 200433, China
| | - M S Bhuva
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, 507 Zheng Min Road, Shanghai 200433, China
| | - P Dipendra
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, 507 Zheng Min Road, Shanghai 200433, China
| | - X Sun
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, 507 Zheng Min Road, Shanghai 200433, China.
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Liu L, Cai S, Chen A, Dong Y, Zhou L, Li L, Zhang Z, Hu Z, Zhang Z, Xiong Y, Hu Z, Li Y, Lu M, Wu L, Zheng L, Ding L, Fan X, Yao Y. Long-term prognostic value of thyroid hormones in left ventricular noncompaction. J Endocrinol Invest 2024:10.1007/s40618-024-02311-8. [PMID: 38358462 DOI: 10.1007/s40618-024-02311-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 01/11/2024] [Indexed: 02/16/2024]
Abstract
PURPOSE Thyroid function is closely related to the prognosis of cardiovascular diseases. This study aimed to explore the predictive value of thyroid hormones for adverse cardiovascular outcomes in left ventricular noncompaction (LVNC). METHODS This longitudinal cohort study enrolled 388 consecutive LVNC patients with complete thyroid function profiles and comprehensive cardiovascular assessment. Potential predictors for adverse outcomes were thoroughly evaluated. RESULTS Over a median follow-up of 5.22 years, primary outcome (the combination of cardiovascular mortality and heart transplantation) occurred in 98 (25.3%) patients. For secondary outcomes, 75 (19.3%) patients died and 130 (33.5%) patients experienced major adverse cardiovascular events (MACE). Multivariable Cox analysis identified that free triiodothyronine (FT3) was independently associated with both primary (HR 0.455, 95%CI 0.313-0.664) and secondary (HR 0.547, 95%CI 0.349-0.858; HR 0.663, 95%CI 0.475-0.925) outcomes. Restricted cubic spline analysis illustrated that the risk for adverse outcomes increased significantly with the decline of serum FT3. The LVNC cohort was further stratified according to tertiles of FT3 levels. Individuals with lower FT3 levels in the tertile 1 group suffered from severe cardiac dysfunction and remodeling, resulting in higher incidence of mortality and MACE (Log-rank P < 0.001). Subgroup analysis revealed that lower concentration of FT3 was linked to worse prognosis, particularly for patients with left atrial diameter ≥ 40 mm or left ventricular ejection fraction ≤ 35%. Adding FT3 to the pre-existing risk score for MACE in LVNC improved its predictive performance. CONCLUSION Through the long-term investigation on a large LVNC cohort, we demonstrated that low FT3 level was an independent predictor for adverse cardiovascular outcomes.
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Affiliation(s)
- L Liu
- Cardiac Arrhythmia Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, National Clinical Research Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Road, Xicheng District, Beijing, 100037, China
| | - S Cai
- Cardiac Arrhythmia Center, Heart Center, The People's Hospital of Zhengzhou University, Henan Provincial People's Hospital, Huazhong Fuwai Hospital, Zhengzhou, Henan, China
| | - A Chen
- Cardiac Arrhythmia Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, National Clinical Research Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Road, Xicheng District, Beijing, 100037, China
| | - Y Dong
- Cardiac Arrhythmia Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, National Clinical Research Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Road, Xicheng District, Beijing, 100037, China
| | - L Zhou
- Cardiac Arrhythmia Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, National Clinical Research Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Road, Xicheng District, Beijing, 100037, China
| | - L Li
- Cardiac Arrhythmia Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, National Clinical Research Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Road, Xicheng District, Beijing, 100037, China
| | - Z Zhang
- Cardiac Arrhythmia Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, National Clinical Research Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Road, Xicheng District, Beijing, 100037, China
| | - Z Hu
- Cardiac Arrhythmia Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, National Clinical Research Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Road, Xicheng District, Beijing, 100037, China
| | - Z Zhang
- Cardiac Arrhythmia Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, National Clinical Research Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Road, Xicheng District, Beijing, 100037, China
| | - Y Xiong
- Cardiac Arrhythmia Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, National Clinical Research Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Road, Xicheng District, Beijing, 100037, China
| | - Z Hu
- Cardiac Arrhythmia Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, National Clinical Research Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Road, Xicheng District, Beijing, 100037, China
| | - Y Li
- Department of Echocardiography, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - M Lu
- Department of Magnetic Resonance Imaging, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - L Wu
- Cardiac Arrhythmia Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, National Clinical Research Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Road, Xicheng District, Beijing, 100037, China
| | - L Zheng
- Cardiac Arrhythmia Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, National Clinical Research Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Road, Xicheng District, Beijing, 100037, China
| | - L Ding
- Cardiac Arrhythmia Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, National Clinical Research Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Road, Xicheng District, Beijing, 100037, China
| | - X Fan
- Cardiac Arrhythmia Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, National Clinical Research Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Road, Xicheng District, Beijing, 100037, China
| | - Y Yao
- Cardiac Arrhythmia Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, National Clinical Research Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishi Road, Xicheng District, Beijing, 100037, China.
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Ma Y, Zhang X, Yi Z, Ding L, Cai B, Jiang Z, Liu W, Zou H, Wang X, Fu G. A study of machine learning models for rapid intraoperative diagnosis of thyroid nodules for clinical practice in China. Cancer Med 2024; 13:e6854. [PMID: 38189547 PMCID: PMC10904961 DOI: 10.1002/cam4.6854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 11/06/2023] [Accepted: 12/10/2023] [Indexed: 01/09/2024] Open
Abstract
BACKGROUND In China, rapid intraoperative diagnosis of frozen sections of thyroid nodules is used to guide surgery. However, the lack of subspecialty pathologists and delayed diagnoses are challenges in clinical treatment. This study aimed to develop novel diagnostic approaches to increase diagnostic effectiveness. METHODS Artificial intelligence and machine learning techniques were used to automatically diagnose histopathological slides. AI-based models were trained with annotations and selected as efficientnetV2-b0 from multi-set experiments. RESULTS On 191 test slides, the proposed method predicted benign and malignant categories with a sensitivity of 72.65%, specificity of 100.0%, and AUC of 86.32%. For the subtype diagnosis, the best AUC was 99.46% for medullary thyroid cancer with an average of 237.6 s per slide. CONCLUSIONS Within our testing dataset, the proposed method accurately diagnosed the thyroid nodules during surgery.
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Affiliation(s)
- Yan Ma
- Department of Pathology, Sir Run Run Shaw HospitalZhejiang University School of MedicineHangzhouZhejiangChina
| | - Xiuming Zhang
- Department of PathologyThe First Affiliated Hospital, School of Medicine, Zhejiang UniversityHangzhouZhejiangChina
| | - Zhongliang Yi
- Department of PathologyHang Zhou Dian Medical LaboratoryHangzhouZhejiangP. R. China
| | - Liya Ding
- Department of Pathology, Sir Run Run Shaw HospitalZhejiang University School of MedicineHangzhouZhejiangChina
| | - Bojun Cai
- Hangzhou PathoAI Technology Co., LtdHangzhouZhejiangChina
| | - Zhinong Jiang
- Department of Pathology, Sir Run Run Shaw HospitalZhejiang University School of MedicineHangzhouZhejiangChina
| | - Wangwang Liu
- Department of Pathology, Sir Run Run Shaw HospitalZhejiang University School of MedicineHangzhouZhejiangChina
| | - Hong Zou
- Department of PathologyThe Second Affiliated Hospital of Zhejiang University School of MedicineHangzhouZhejiangChina
| | - Xiaomei Wang
- Hangzhou PathoAI Technology Co., LtdHangzhouZhejiangChina
| | - Guoxiang Fu
- Department of Pathology, Sir Run Run Shaw HospitalZhejiang University School of MedicineHangzhouZhejiangChina
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Ding L, Huang L. THE EFFECT OF CHILDHOOD SUBJECTIVE SOCIOECONOMIC STATUS ON MENTAL HEALTH: THE MEDIATING ROLES OF PERCEIVED DISCRIMINATION AND STATUS ANXIETY. Georgian Med News 2024:56-62. [PMID: 38501622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Abstract
This study explored the effect of childhood subjective socioeconomic status on mental health and the chain-mediating mechanism of perceived discrimination and status anxiety. A random survey was conducted via an online survey platform with 999 college students in east China. Participants completed the Childhood Subjective Socioeconomic Status Scale, General Health Questionnaire, Status Anxiety Scale, and the Perceived Personal Discrimination Scale. The sample comprised 323 men and 676 women. The mean age was 20.49±2.70 years. Mediation analysis using Model 6 and 5,000 bootstrap samples was employed to explore the mediating role of perceived discrimination and status anxiety in the relationship between childhood subjective socioeconomic status and mental health. Mental health was significantly positively correlated with childhood socioeconomic status, and significantly negatively correlated with perceived discrimination and status anxiety. Perceived discrimination and status anxiety played a partial chain mediating role between childhood socioeconomic status and mental health. The mediation model accounted for 31% of the variance in mental health. Moreover, the results indicated that the significant mediating effect of perceived discrimination between childhood subjective SES and mental health had a value of 0.029 and a 95% confidence interval of [0.019, 0.041]. Furthermore, the significant mediating effect of status anxiety between childhood subjective SES and mental health had a value of 0.010 and a 95% confidence interval of [0.006, 0.014]. The results provide an explanation of how childhood subjective socioeconomic status influences their mental health. Interventions to address perceived discrimination and status anxiety can improve the mental health status of children who experience childhood adversity. The study's findings contribute to understanding mental health in childhood and inform potential interventions to improve the well-being of individuals who have experienced childhood adversity. The limitations of the study were self-report scales and potential biases in the sample population. Addressing these limitations will enhance the credibility of the research and pave the way for future studies.
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Affiliation(s)
- L Ding
- School of Humanities and Management, Wannan Medical College, Wuhu, China
| | - L Huang
- School of Humanities and Management, Wannan Medical College, Wuhu, China
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5
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Han X, Guo S, Ji N, Li T, Liu J, Ye X, Wang Y, Yun Z, Xiong F, Rong J, Liu D, Ma H, Wang Y, Huang Y, Zhang P, Wu W, Ding L, Hawrylycz M, Lein E, Ascoli GA, Xie W, Liu L, Zhang L, Peng H. Whole human-brain mapping of single cortical neurons for profiling morphological diversity and stereotypy. Sci Adv 2023; 9:eadf3771. [PMID: 37824619 PMCID: PMC10569712 DOI: 10.1126/sciadv.adf3771] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 04/18/2023] [Indexed: 10/14/2023]
Abstract
Quantifying neuron morphology and distribution at the whole-brain scale is essential to understand the structure and diversity of cell types. It is exceedingly challenging to reuse recent technologies of single-cell labeling and whole-brain imaging to study human brains. We propose adaptive cell tomography (ACTomography), a low-cost, high-throughput, and high-efficacy tomography approach, based on adaptive targeting of individual cells. We established a platform to inject dyes into cortical neurons in surgical tissues of 18 patients with brain tumors or other conditions and one donated fresh postmortem brain. We collected three-dimensional images of 1746 cortical neurons, of which 852 neurons were reconstructed to quantify local dendritic morphology, and mapped to standard atlases. In our data, human neurons are more diverse across brain regions than by subject age or gender. The strong stereotypy within cohorts of brain regions allows generating a statistical tensor field of neuron morphology to characterize anatomical modularity of a human brain.
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Affiliation(s)
- Xiaofeng Han
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Shuxia Guo
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Nan Ji
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Beijing Key Laboratory of Brain Tumor, Beijing, China
| | - Tian Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jian Liu
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Xiangqiao Ye
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Yi Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhixi Yun
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Feng Xiong
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Jing Rong
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Di Liu
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Hui Ma
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Yujin Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yue Huang
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Peng Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wenhao Wu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Liya Ding
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | | | - Ed Lein
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Giorgio A. Ascoli
- Center for Neural Informatics, Krasnow Institute for Advanced Studies and Bioengineering Department, College of Engineering and Computing, George Mason University, Fairfax, VA, USA
| | - Wei Xie
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
- The Key Laboratory of Developmental Genes and Human Disease, Ministry of Education, School of Life Science and Technology, Southeast University, Nanjing, China
| | - Lijuan Liu
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Liwei Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Beijing Key Laboratory of Brain Tumor, Beijing, China
| | - Hanchuan Peng
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
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Lv C, Wang R, Li S, Yan S, Wang Y, Chen J, Wang L, Liu Y, Guo Z, Wang J, Pei Y, Yu L, Wu N, Lu F, Gao F, Chen J, Liu Y, Wang X, Li S, Han B, Zhang L, Ma Y, Ding L, Wang Y, Yuan X, Yang Y. Randomized phase II adjuvant trial to compare two treatment durations of icotinib (2 years versus 1 year) for stage II-IIIA EGFR-positive lung adenocarcinoma patients (ICOMPARE study). ESMO Open 2023; 8:101565. [PMID: 37348348 PMCID: PMC10515286 DOI: 10.1016/j.esmoop.2023.101565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/30/2023] [Accepted: 04/24/2023] [Indexed: 06/24/2023] Open
Abstract
BACKGROUND Despite the prolonged median disease-free survival (DFS) by adjuvant targeted therapy in non-small-cell lung cancer patients with epidermal growth factor receptor (EGFR) mutations, the relationship between the treatment duration and the survival benefits in patients remains unknown. PATIENTS AND METHODS In this multicenter, randomized, open-label, phase II trial, eligible patients aged 18-75 years with EGFR-mutant, stage II-IIIA lung adenocarcinoma and who had not received adjuvant chemotherapy after complete tumor resection were enrolled from eight centers in China. Patients were randomly assigned (1 : 1) to receive either 1-year or 2-year icotinib (125 mg thrice daily). The primary endpoint was DFS assessed by investigator. The secondary endpoints were overall survival (OS) and safety. This study was registered at ClinicalTrials.gov (NCT01929200). RESULTS Between September 2013 and October 2018, 109 patients were enrolled (1-year group, n = 55; 2-year group, n = 54). Median DFS was 48.9 months [95% confidence interval (CI) 33.1-70.1 months] in the 2-year group and 32.9 months (95% CI 26.6-44.8 months) in the 1-year group [hazard ratio (HR) 0.51; 95% CI 0.28-0.94; P = 0.0290]. Median OS for patients was 75.8 months [95% CI 64.4 months-not evaluable (NE)] in the 2-year group and NE (95% CI 66.3 months-NE) in the 1-year group (HR 0.34; 95% CI 0.13-0.95; P = 0.0317). Treatment-related adverse events (TRAEs) were observed in 41 of 55 (75%) patients in the 1-year group and in 36 of 54 (67%) patients in the 2-year group. Grade 3-4 TRAEs occurred in 4 of 55 (7%) patients in the 1-year group and in 3 of 54 (6%) patients in the 2-year group. No treatment-related deaths or interstitial lung disease was reported. CONCLUSIONS Two-year adjuvant icotinib was shown to significantly improve DFS and provide an OS benefit in EGFR-mutant, stage II-IIIA lung adenocarcinoma patients compared with 1-year treatment in this exploratory phase II study.
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Affiliation(s)
- C Lv
- Department of Thoracic Surgery II, Beijing Cancer Hospital, Beijing
| | - R Wang
- Department of Thoracic Surgery, The Fourth Hospital of Hebei Medical University, Hebi
| | - S Li
- Department of Thoracic Surgery II, Beijing Cancer Hospital, Beijing
| | - S Yan
- Department of Thoracic Surgery II, Beijing Cancer Hospital, Beijing
| | - Y Wang
- Department of Thoracic Surgery II, Beijing Cancer Hospital, Beijing
| | - J Chen
- Department of Thoracic Surgery II, Beijing Cancer Hospital, Beijing
| | - L Wang
- Department of Thoracic Surgery II, Beijing Cancer Hospital, Beijing
| | - Y Liu
- Department of Thoracic Surgery II, Beijing Cancer Hospital, Beijing
| | - Z Guo
- Department of Thoracic Surgery, The Affiliated Hospital of Inner Mongolia Medical University, Inner Mongolia
| | - J Wang
- Department of Thoracic Surgery II, Beijing Cancer Hospital, Beijing
| | - Y Pei
- Department of Thoracic Surgery II, Beijing Cancer Hospital, Beijing
| | - L Yu
- Department of Thoracic Surgery, Beijing Tongren Hospital, CMU, Beijing
| | - N Wu
- Department of Thoracic Surgery II, Beijing Cancer Hospital, Beijing
| | - F Lu
- Department of Thoracic Surgery II, Beijing Cancer Hospital, Beijing
| | - F Gao
- Department of Thoracic Surgery, The Fourth Hospital of Hebei Medical University, Hebi
| | - J Chen
- Thoracic Neoplasms Surgical Department, Tianjing Medical University General Hospital, Tianjing
| | - Y Liu
- Thoracic Neoplasms Surgical Department, Inner Mongolia People's Hospital, Inner Mongolia
| | - X Wang
- Department of Thoracic Surgery II, Beijing Cancer Hospital, Beijing
| | - S Li
- Department of Thoracic Surgery, Peking Union Medical College Hospital, Beijing
| | - B Han
- Department of Thoracic Surgery, PLA Pocket Force Characteristic Medical Center, Beijing
| | - L Zhang
- Department of Thoracic Surgery II, Beijing Cancer Hospital, Beijing
| | - Y Ma
- Department of Thoracic Surgery II, Beijing Cancer Hospital, Beijing
| | - L Ding
- Betta Pharmaceuticals Co., Ltd, Hangzhou, China
| | - Y Wang
- Betta Pharmaceuticals Co., Ltd, Hangzhou, China
| | - X Yuan
- Betta Pharmaceuticals Co., Ltd, Hangzhou, China
| | - Y Yang
- Department of Thoracic Surgery II, Beijing Cancer Hospital, Beijing.
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Abdulameer NJ, Acharya U, Adare A, Aidala C, Ajitanand NN, Akiba Y, Akimoto R, Alfred M, Apadula N, Aramaki Y, Asano H, Atomssa ET, Awes TC, Azmoun B, Babintsev V, Bai M, Bandara NS, Bannier B, Barish KN, Bathe S, Bazilevsky A, Beaumier M, Beckman S, Belmont R, Berdnikov A, Berdnikov Y, Bichon L, Black D, Blankenship B, Bok JS, Borisov V, Boyle K, Brooks ML, Bryslawskyj J, Buesching H, Bumazhnov V, Campbell S, Canoa Roman V, Chen CH, Chiu M, Chi CY, Choi IJ, Choi JB, Chujo T, Citron Z, Connors M, Corliss R, Corrales Morales Y, Csanád M, Csörgő T, Datta A, Daugherity MS, David G, Dean CT, DeBlasio K, Dehmelt K, Denisov A, Deshpande A, Desmond EJ, Ding L, Dion A, Doomra V, Do JH, Drees A, Drees KA, Durham JM, Durum A, En'yo H, Enokizono A, Esha R, Fadem B, Fan W, Feege N, Fields DE, Finger M, Finger M, Firak D, Fitzgerald D, Fokin SL, Frantz JE, Franz A, Frawley AD, Gallus P, Gal C, Garg P, Ge H, Giles M, Giordano F, Glenn A, Goto Y, Grau N, Greene SV, Grosse Perdekamp M, Gunji T, Guragain H, Gu Y, Hachiya T, Haggerty JS, Hahn KI, Hamagaki H, Hanks J, Han SY, Harvey M, Hasegawa S, Hemmick TK, He X, Hill JC, Hodges A, Hollis RS, Homma K, Hong B, Hoshino T, Huang J, Ikeda Y, Imai K, Imazu Y, Inaba M, Iordanova A, Isenhower D, Ivanishchev D, Jacak BV, Jeon SJ, Jezghani M, Jiang X, Ji Z, Johnson BM, Joo E, Joo KS, Jouan D, Jumper DS, Kang JH, Kang JS, Kawall D, Kazantsev AV, Key JA, Khachatryan V, Khanzadeev A, Khatiwada A, Kihara K, Kim C, Kim DH, Kim DJ, Kim EJ, Kim HJ, Kim M, Kim T, Kim YK, Kincses D, Kingan A, Kistenev E, Klatsky J, Kleinjan D, Kline P, Koblesky T, Kofarago M, Koster J, Kotov D, Kovacs L, Kurgyis B, Kurita K, Kurosawa M, Kwon Y, Lajoie JG, Larionova D, Lebedev A, Lee KB, Lee SH, Leitch MJ, Leitgab M, Lewis NA, Lim SH, Liu MX, Li X, Loomis DA, Lynch D, Lökös S, Majoros T, Makdisi YI, Makek M, Manion A, Manko VI, Mannel E, McCumber M, McGaughey PL, McGlinchey D, McKinney C, Meles A, Mendoza M, Meredith B, Miake Y, Mignerey AC, Miller AJ, Milov A, Mishra DK, Mitchell JT, Mitrankova M, Mitrankov I, Miyasaka S, Mizuno S, Mondal MM, Montuenga P, Moon T, Morrison DP, Moukhanova TV, Muhammad A, Mulilo B, Murakami T, Murata J, Mwai A, Nagamiya S, Nagle JL, Nagy MI, Nakagawa I, Nakagomi H, Nakano K, Nattrass C, Nelson S, Netrakanti PK, Nihashi M, Niida T, Nouicer R, Novitzky N, Nukazuka G, Nyanin AS, O'Brien E, Ogilvie CA, Oh J, Orjuela Koop JD, Orosz M, Osborn JD, Oskarsson A, Ozawa K, Pak R, Pantuev V, Papavassiliou V, Park JS, Park S, Patel L, Patel M, Pate SF, Peng JC, Peng W, Perepelitsa DV, Perera GDN, Peressounko DY, PerezLara CE, Perry J, Petti R, Pinkenburg C, Pinson R, Pisani RP, Potekhin M, Pun A, Purschke ML, Radzevich PV, Rak J, Ramasubramanian N, Ravinovich I, Read KF, Reynolds D, Riabov V, Riabov Y, Richford D, Riveli N, Roach D, Rolnick SD, Rosati M, Rowan Z, Rubin JG, Runchey J, Saito N, Sakaguchi T, Sako H, Samsonov V, Sarsour M, Sato S, Sawada S, Schaefer B, Schmoll BK, Sedgwick K, Seele J, Seidl R, Sen A, Seto R, Sett P, Sexton A, Sharma D, Shein I, Shibata M, Shibata TA, Shigaki K, Shimomura M, Shi Z, Shukla P, Sickles A, Silva CL, Silvermyr D, Singh BK, Singh CP, Singh V, Slunečka M, Smith KL, Soltz RA, Sondheim WE, Sorensen SP, Sourikova IV, Stankus PW, Stepanov M, Stoll SP, Sugitate T, Sukhanov A, Sumita T, Sun J, Sun Z, Sziklai J, Takahama R, Takahara A, Taketani A, Tanida K, Tannenbaum MJ, Tarafdar S, Taranenko A, Timilsina A, Todoroki T, Tomášek M, Torii H, Towell M, Towell R, Towell RS, Tserruya I, Ueda Y, Ujvari B, van Hecke HW, Vargyas M, Velkovska J, Virius M, Vrba V, Vznuzdaev E, Wang XR, Wang Z, Watanabe D, Watanabe Y, Watanabe YS, Wei F, Whitaker S, Wolin S, Wong CP, Woody CL, Wysocki M, Xia B, Xue L, Yalcin S, Yamaguchi YL, Yanovich A, Yoon I, Younus I, Yushmanov IE, Zajc WA, Zelenski A, Zou L. Measurement of Direct-Photon Cross Section and Double-Helicity Asymmetry at sqrt[s]=510 GeV in p[over →]+p[over →] Collisions. Phys Rev Lett 2023; 130:251901. [PMID: 37418716 DOI: 10.1103/physrevlett.130.251901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 11/04/2022] [Accepted: 04/28/2023] [Indexed: 07/09/2023]
Abstract
We present measurements of the cross section and double-helicity asymmetry A_{LL} of direct-photon production in p[over →]+p[over →] collisions at sqrt[s]=510 GeV. The measurements have been performed at midrapidity (|η|<0.25) with the PHENIX detector at the Relativistic Heavy Ion Collider. At relativistic energies, direct photons are dominantly produced from the initial quark-gluon hard scattering and do not interact via the strong force at leading order. Therefore, at sqrt[s]=510 GeV, where leading-order-effects dominate, these measurements provide clean and direct access to the gluon helicity in the polarized proton in the gluon-momentum-fraction range 0.02<x<0.08, with direct sensitivity to the sign of the gluon contribution.
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Affiliation(s)
- N J Abdulameer
- Debrecen University, H-4010 Debrecen, Egyetem tér 1, Hungary
| | - U Acharya
- Georgia State University, Atlanta, Georgia 30303, USA
| | - A Adare
- University of Colorado, Boulder, Colorado 80309, USA
| | - C Aidala
- Department of Physics, University of Michigan, Ann Arbor, Michigan 48109-1040, USA
| | - N N Ajitanand
- Chemistry Department, Stony Brook University, SUNY, Stony Brook, New York 11794-3400, USA
| | - Y Akiba
- RIKEN Nishina Center for Accelerator-Based Science, Wako, Saitama 351-0198, Japan
- RIKEN BNL Research Center, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - R Akimoto
- Center for Nuclear Study, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo 113-0033, Japan
| | - M Alfred
- Department of Physics and Astronomy, Howard University, Washington, D.C. 20059, USA
| | - N Apadula
- Iowa State University, Ames, Iowa 50011, USA
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - Y Aramaki
- RIKEN Nishina Center for Accelerator-Based Science, Wako, Saitama 351-0198, Japan
| | - H Asano
- Kyoto University, Kyoto 606-8502, Japan
- RIKEN Nishina Center for Accelerator-Based Science, Wako, Saitama 351-0198, Japan
| | - E T Atomssa
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - T C Awes
- Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
| | - B Azmoun
- Physics Department, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - V Babintsev
- IHEP Protvino, State Research Center of Russian Federation, Institute for High Energy Physics, Protvino 142281, Russia
| | - M Bai
- Collider-Accelerator Department, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - N S Bandara
- Department of Physics, University of Massachusetts, Amherst, Massachusetts 01003-9337, USA
| | - B Bannier
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - K N Barish
- University of California-Riverside, Riverside, California 92521, USA
| | - S Bathe
- Baruch College, City University of New York, New York, New York 10010, USA
- RIKEN BNL Research Center, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - A Bazilevsky
- Physics Department, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - M Beaumier
- University of California-Riverside, Riverside, California 92521, USA
| | - S Beckman
- University of Colorado, Boulder, Colorado 80309, USA
| | - R Belmont
- University of Colorado, Boulder, Colorado 80309, USA
- Physics and Astronomy Department, University of North Carolina at Greensboro, Greensboro, North Carolina 27412, USA
| | - A Berdnikov
- Saint Petersburg State Polytechnic University, St. Petersburg 195251 Russia
| | - Y Berdnikov
- Saint Petersburg State Polytechnic University, St. Petersburg 195251 Russia
| | - L Bichon
- Vanderbilt University, Nashville, Tennessee 37235, USA
| | - D Black
- University of California-Riverside, Riverside, California 92521, USA
| | - B Blankenship
- Vanderbilt University, Nashville, Tennessee 37235, USA
| | - J S Bok
- New Mexico State University, Las Cruces, New Mexico 88003, USA
| | - V Borisov
- Saint Petersburg State Polytechnic University, St. Petersburg 195251 Russia
| | - K Boyle
- RIKEN BNL Research Center, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - M L Brooks
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - J Bryslawskyj
- Baruch College, City University of New York, New York, New York 10010, USA
- University of California-Riverside, Riverside, California 92521, USA
| | - H Buesching
- Physics Department, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - V Bumazhnov
- IHEP Protvino, State Research Center of Russian Federation, Institute for High Energy Physics, Protvino 142281, Russia
| | - S Campbell
- Columbia University, New York, New York 10027 and Nevis Laboratories, Irvington, New York 10533, USA
- Iowa State University, Ames, Iowa 50011, USA
| | - V Canoa Roman
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - C-H Chen
- RIKEN BNL Research Center, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - M Chiu
- Physics Department, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - C Y Chi
- Columbia University, New York, New York 10027 and Nevis Laboratories, Irvington, New York 10533, USA
| | - I J Choi
- University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - J B Choi
- Jeonbuk National University, Jeonju, 54896, Korea
| | - T Chujo
- Tomonaga Center for the History of the Universe, University of Tsukuba, Tsukuba, Ibaraki 305, Japan
| | - Z Citron
- Weizmann Institute, Rehovot 76100, Israel
| | - M Connors
- Georgia State University, Atlanta, Georgia 30303, USA
| | - R Corliss
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | | | - M Csanád
- ELTE, Eötvös Loránd University, H-1117 Budapest, Pázmány P. s. 1/A, Hungary
| | - T Csörgő
- MATE, Laboratory of Femtoscopy, Károly Róbert Campus, H-3200 Gyöngyös, Mátraiút 36, Hungary
- Institute for Particle and Nuclear Physics, Wigner Research Centre for Physics, Hungarian Academy of Sciences (Wigner RCP, RMKI) H-1525 Budapest 114, P.O. Box 49, Budapest, Hungary
| | - A Datta
- University of New Mexico, Albuquerque, New Mexico 87131, USA
| | | | - G David
- Physics Department, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - C T Dean
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - K DeBlasio
- University of New Mexico, Albuquerque, New Mexico 87131, USA
| | - K Dehmelt
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - A Denisov
- IHEP Protvino, State Research Center of Russian Federation, Institute for High Energy Physics, Protvino 142281, Russia
| | - A Deshpande
- RIKEN BNL Research Center, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - E J Desmond
- Physics Department, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - L Ding
- Iowa State University, Ames, Iowa 50011, USA
| | - A Dion
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - V Doomra
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - J H Do
- Yonsei University, IPAP, Seoul 120-749, Korea
| | - A Drees
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - K A Drees
- Collider-Accelerator Department, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - J M Durham
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - A Durum
- IHEP Protvino, State Research Center of Russian Federation, Institute for High Energy Physics, Protvino 142281, Russia
| | - H En'yo
- RIKEN Nishina Center for Accelerator-Based Science, Wako, Saitama 351-0198, Japan
| | - A Enokizono
- RIKEN Nishina Center for Accelerator-Based Science, Wako, Saitama 351-0198, Japan
- Physics Department, Rikkyo University, 3-34-1 Nishi-Ikebukuro, Toshima, Tokyo 171-8501, Japan
| | - R Esha
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - B Fadem
- Muhlenberg College, Allentown, Pennsylvania 18104-5586, USA
| | - W Fan
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - N Feege
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - D E Fields
- University of New Mexico, Albuquerque, New Mexico 87131, USA
| | - M Finger
- Charles University, Faculty of Mathematics and Physics, 180 00 Troja, Prague, Czech Republic
| | - M Finger
- Charles University, Faculty of Mathematics and Physics, 180 00 Troja, Prague, Czech Republic
| | - D Firak
- Debrecen University, H-4010 Debrecen, Egyetem tér 1, Hungary
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - D Fitzgerald
- Department of Physics, University of Michigan, Ann Arbor, Michigan 48109-1040, USA
| | - S L Fokin
- National Research Center "Kurchatov Institute," Moscow 123098, Russia
| | - J E Frantz
- Department of Physics and Astronomy, Ohio University, Athens, Ohio 45701, USA
| | - A Franz
- Physics Department, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - A D Frawley
- Florida State University, Tallahassee, Florida 32306, USA
| | - P Gallus
- Czech Technical University, Zikova 4, 166 36 Prague 6, Czech Republic
| | - C Gal
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - P Garg
- Department of Physics, Banaras Hindu University, Varanasi 221005, India
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - H Ge
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - M Giles
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - F Giordano
- University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - A Glenn
- Lawrence Livermore National Laboratory, Livermore, California 94550, USA
| | - Y Goto
- RIKEN Nishina Center for Accelerator-Based Science, Wako, Saitama 351-0198, Japan
- RIKEN BNL Research Center, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - N Grau
- Department of Physics, Augustana University, Sioux Falls, South Dakota 57197, USA
| | - S V Greene
- Vanderbilt University, Nashville, Tennessee 37235, USA
| | | | - T Gunji
- Center for Nuclear Study, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo 113-0033, Japan
| | - H Guragain
- Georgia State University, Atlanta, Georgia 30303, USA
| | - Y Gu
- Chemistry Department, Stony Brook University, SUNY, Stony Brook, New York 11794-3400, USA
| | - T Hachiya
- Nara Women's University, Kita-uoya Nishi-machi Nara 630-8506, Japan
- RIKEN Nishina Center for Accelerator-Based Science, Wako, Saitama 351-0198, Japan
- RIKEN BNL Research Center, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - J S Haggerty
- Physics Department, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - K I Hahn
- Ewha Womans University, Seoul 120-750, Korea
| | - H Hamagaki
- Center for Nuclear Study, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo 113-0033, Japan
| | - J Hanks
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - S Y Han
- Ewha Womans University, Seoul 120-750, Korea
- Korea University, Seoul 02841, Korea
| | - M Harvey
- Texas Southern University, Houston, Texas 77004, USA
| | - S Hasegawa
- Advanced Science Research Center, Japan Atomic Energy Agency, 2-4 Shirakata Shirane, Tokai-mura, Naka-gun, Ibaraki-ken 319-1195, Japan
| | - T K Hemmick
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - X He
- Georgia State University, Atlanta, Georgia 30303, USA
| | - J C Hill
- Iowa State University, Ames, Iowa 50011, USA
| | - A Hodges
- Georgia State University, Atlanta, Georgia 30303, USA
- University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - R S Hollis
- University of California-Riverside, Riverside, California 92521, USA
| | - K Homma
- Hiroshima University, Kagamiyama, Higashi-Hiroshima 739-8526, Japan
| | - B Hong
- Korea University, Seoul 02841, Korea
| | - T Hoshino
- Hiroshima University, Kagamiyama, Higashi-Hiroshima 739-8526, Japan
| | - J Huang
- Physics Department, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - Y Ikeda
- RIKEN Nishina Center for Accelerator-Based Science, Wako, Saitama 351-0198, Japan
| | - K Imai
- Advanced Science Research Center, Japan Atomic Energy Agency, 2-4 Shirakata Shirane, Tokai-mura, Naka-gun, Ibaraki-ken 319-1195, Japan
| | - Y Imazu
- RIKEN Nishina Center for Accelerator-Based Science, Wako, Saitama 351-0198, Japan
| | - M Inaba
- Tomonaga Center for the History of the Universe, University of Tsukuba, Tsukuba, Ibaraki 305, Japan
| | - A Iordanova
- University of California-Riverside, Riverside, California 92521, USA
| | - D Isenhower
- Abilene Christian University, Abilene, Texas 79699, USA
| | - D Ivanishchev
- PNPI, Petersburg Nuclear Physics Institute, Gatchina, Leningrad region 188300, Russia
| | - B V Jacak
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - S J Jeon
- Myongji University, Yongin, Kyonggido 449-728, Korea
| | - M Jezghani
- Georgia State University, Atlanta, Georgia 30303, USA
| | - X Jiang
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - Z Ji
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - B M Johnson
- Physics Department, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
- Georgia State University, Atlanta, Georgia 30303, USA
| | - E Joo
- Korea University, Seoul 02841, Korea
| | - K S Joo
- Myongji University, Yongin, Kyonggido 449-728, Korea
| | - D Jouan
- IPN-Orsay, Univ. Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, BP1, F-91406 Orsay, France
| | - D S Jumper
- University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - J H Kang
- Yonsei University, IPAP, Seoul 120-749, Korea
| | - J S Kang
- Hanyang University, Seoul 133-792, Korea
| | - D Kawall
- Department of Physics, University of Massachusetts, Amherst, Massachusetts 01003-9337, USA
| | - A V Kazantsev
- National Research Center "Kurchatov Institute," Moscow 123098, Russia
| | - J A Key
- University of New Mexico, Albuquerque, New Mexico 87131, USA
| | - V Khachatryan
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - A Khanzadeev
- PNPI, Petersburg Nuclear Physics Institute, Gatchina, Leningrad region 188300, Russia
| | - A Khatiwada
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - K Kihara
- Tomonaga Center for the History of the Universe, University of Tsukuba, Tsukuba, Ibaraki 305, Japan
| | - C Kim
- Korea University, Seoul 02841, Korea
| | - D H Kim
- Ewha Womans University, Seoul 120-750, Korea
| | - D J Kim
- Helsinki Institute of Physics and University of Jyväskylä, P.O.Box 35, FI-40014 Jyväskylä, Finland
| | - E-J Kim
- Jeonbuk National University, Jeonju, 54896, Korea
| | - H-J Kim
- Yonsei University, IPAP, Seoul 120-749, Korea
| | - M Kim
- Department of Physics and Astronomy, Seoul National University, Seoul 151-742, Korea
| | - T Kim
- Ewha Womans University, Seoul 120-750, Korea
| | - Y K Kim
- Hanyang University, Seoul 133-792, Korea
| | - D Kincses
- ELTE, Eötvös Loránd University, H-1117 Budapest, Pázmány P. s. 1/A, Hungary
| | - A Kingan
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - E Kistenev
- Physics Department, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - J Klatsky
- Florida State University, Tallahassee, Florida 32306, USA
| | - D Kleinjan
- University of California-Riverside, Riverside, California 92521, USA
| | - P Kline
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - T Koblesky
- University of Colorado, Boulder, Colorado 80309, USA
| | - M Kofarago
- ELTE, Eötvös Loránd University, H-1117 Budapest, Pázmány P. s. 1/A, Hungary
- Institute for Particle and Nuclear Physics, Wigner Research Centre for Physics, Hungarian Academy of Sciences (Wigner RCP, RMKI) H-1525 Budapest 114, P.O. Box 49, Budapest, Hungary
| | - J Koster
- RIKEN BNL Research Center, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - D Kotov
- PNPI, Petersburg Nuclear Physics Institute, Gatchina, Leningrad region 188300, Russia
- Saint Petersburg State Polytechnic University, St. Petersburg 195251 Russia
| | - L Kovacs
- ELTE, Eötvös Loránd University, H-1117 Budapest, Pázmány P. s. 1/A, Hungary
| | - B Kurgyis
- ELTE, Eötvös Loránd University, H-1117 Budapest, Pázmány P. s. 1/A, Hungary
| | - K Kurita
- Physics Department, Rikkyo University, 3-34-1 Nishi-Ikebukuro, Toshima, Tokyo 171-8501, Japan
| | - M Kurosawa
- RIKEN Nishina Center for Accelerator-Based Science, Wako, Saitama 351-0198, Japan
- RIKEN BNL Research Center, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - Y Kwon
- Yonsei University, IPAP, Seoul 120-749, Korea
| | - J G Lajoie
- Iowa State University, Ames, Iowa 50011, USA
| | - D Larionova
- Saint Petersburg State Polytechnic University, St. Petersburg 195251 Russia
| | - A Lebedev
- Iowa State University, Ames, Iowa 50011, USA
| | - K B Lee
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - S H Lee
- Iowa State University, Ames, Iowa 50011, USA
- Department of Physics, University of Michigan, Ann Arbor, Michigan 48109-1040, USA
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - M J Leitch
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - M Leitgab
- University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - N A Lewis
- Department of Physics, University of Michigan, Ann Arbor, Michigan 48109-1040, USA
| | - S H Lim
- Pusan National University, Pusan 46241, Korea
- Yonsei University, IPAP, Seoul 120-749, Korea
| | - M X Liu
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - X Li
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - D A Loomis
- Department of Physics, University of Michigan, Ann Arbor, Michigan 48109-1040, USA
| | - D Lynch
- Physics Department, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - S Lökös
- ELTE, Eötvös Loránd University, H-1117 Budapest, Pázmány P. s. 1/A, Hungary
| | - T Majoros
- Debrecen University, H-4010 Debrecen, Egyetem tér 1, Hungary
| | - Y I Makdisi
- Collider-Accelerator Department, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - M Makek
- Weizmann Institute, Rehovot 76100, Israel
- Department of Physics, Faculty of Science, University of Zagreb, Bijenička c. 32 HR-10002 Zagreb, Croatia
| | - A Manion
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - V I Manko
- National Research Center "Kurchatov Institute," Moscow 123098, Russia
| | - E Mannel
- Physics Department, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - M McCumber
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - P L McGaughey
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - D McGlinchey
- University of Colorado, Boulder, Colorado 80309, USA
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - C McKinney
- University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - A Meles
- New Mexico State University, Las Cruces, New Mexico 88003, USA
| | - M Mendoza
- University of California-Riverside, Riverside, California 92521, USA
| | - B Meredith
- Columbia University, New York, New York 10027 and Nevis Laboratories, Irvington, New York 10533, USA
| | - Y Miake
- Tomonaga Center for the History of the Universe, University of Tsukuba, Tsukuba, Ibaraki 305, Japan
| | - A C Mignerey
- University of Maryland, College Park, Maryland 20742, USA
| | - A J Miller
- Abilene Christian University, Abilene, Texas 79699, USA
| | - A Milov
- Weizmann Institute, Rehovot 76100, Israel
| | - D K Mishra
- Bhabha Atomic Research Centre, Bombay 400 085, India
| | - J T Mitchell
- Physics Department, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - M Mitrankova
- Saint Petersburg State Polytechnic University, St. Petersburg 195251 Russia
| | - Iu Mitrankov
- Saint Petersburg State Polytechnic University, St. Petersburg 195251 Russia
| | - S Miyasaka
- RIKEN Nishina Center for Accelerator-Based Science, Wako, Saitama 351-0198, Japan
- Department of Physics, Tokyo Institute of Technology, Oh-okayama, Meguro, Tokyo 152-8551, Japan
| | - S Mizuno
- RIKEN Nishina Center for Accelerator-Based Science, Wako, Saitama 351-0198, Japan
- Tomonaga Center for the History of the Universe, University of Tsukuba, Tsukuba, Ibaraki 305, Japan
| | - M M Mondal
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - P Montuenga
- University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - T Moon
- Korea University, Seoul 02841, Korea
- Yonsei University, IPAP, Seoul 120-749, Korea
| | - D P Morrison
- Physics Department, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - T V Moukhanova
- National Research Center "Kurchatov Institute," Moscow 123098, Russia
| | - A Muhammad
- Mississippi State University, Mississippi State, Mississippi 39762, USA
| | - B Mulilo
- Korea University, Seoul 02841, Korea
- RIKEN Nishina Center for Accelerator-Based Science, Wako, Saitama 351-0198, Japan
- Department of Physics, School of Natural Sciences, University of Zambia, Great East Road Campus, Box 32379 Lusaka, Zambia
| | - T Murakami
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| | - J Murata
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- Physics Department, Rikkyo University, 3-34-1 Nishi-Ikebukuro, Toshima, Tokyo 171-8501, Japan
| | - A Mwai
- Chemistry Department, Stony Brook University, SUNY, Stony Brook, New York 11794-3400, USA
| | - S Nagamiya
- KEK, High Energy Accelerator Research Organization, Tsukuba, Ibaraki 305-0801, Japan
- RIKEN Nishina Center for Accelerator-Based Science, Wako, Saitama 351-0198, Japan
| | - J L Nagle
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| | - M I Nagy
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| | - I Nakagawa
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| | - H Nakagomi
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| | - K Nakano
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- Department of Physics, Tokyo Institute of Technology, Oh-okayama, Meguro, Tokyo 152-8551, Japan
| | - C Nattrass
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| | - S Nelson
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| | | | - M Nihashi
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| | - T Niida
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| | - R Nouicer
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| | - N Novitzky
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| | - G Nukazuka
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- RIKEN BNL Research Center, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - A S Nyanin
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| | - E O'Brien
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| | - C A Ogilvie
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| | - J Oh
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| | | | - M Orosz
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| | - J D Osborn
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| | - A Oskarsson
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| | - K Ozawa
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- Tomonaga Center for the History of the Universe, University of Tsukuba, Tsukuba, Ibaraki 305, Japan
| | - R Pak
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| | - V Pantuev
- Institute for Nuclear Research of the Russian Academy of Sciences, prospekt 60-letiya Oktyabrya 7a, Moscow 117312, Russia
| | - V Papavassiliou
- New Mexico State University, Las Cruces, New Mexico 88003, USA
| | - J S Park
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| | - S Park
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- Department of Physics and Astronomy, Seoul National University, Seoul 151-742, Korea
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - L Patel
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| | - M Patel
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| | - S F Pate
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| | - J-C Peng
- University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - W Peng
- Vanderbilt University, Nashville, Tennessee 37235, USA
| | - D V Perepelitsa
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- University of Colorado, Boulder, Colorado 80309, USA
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| | - G D N Perera
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- National Research Center "Kurchatov Institute," Moscow 123098, Russia
| | - C E PerezLara
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - J Perry
- Iowa State University, Ames, Iowa 50011, USA
| | - R Petti
- Physics Department, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - C Pinkenburg
- Physics Department, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - R Pinson
- Abilene Christian University, Abilene, Texas 79699, USA
| | - R P Pisani
- Physics Department, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - M Potekhin
- Physics Department, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - A Pun
- Department of Physics and Astronomy, Ohio University, Athens, Ohio 45701, USA
| | - M L Purschke
- Physics Department, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - P V Radzevich
- Saint Petersburg State Polytechnic University, St. Petersburg 195251 Russia
| | - J Rak
- Helsinki Institute of Physics and University of Jyväskylä, P.O.Box 35, FI-40014 Jyväskylä, Finland
| | - N Ramasubramanian
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | | | - K F Read
- Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
- University of Tennessee, Knoxville, Tennessee 37996, USA
| | - D Reynolds
- Chemistry Department, Stony Brook University, SUNY, Stony Brook, New York 11794-3400, USA
| | - V Riabov
- National Research Nuclear University, MEPhI, Moscow Engineering Physics Institute, Moscow 115409, Russia
- PNPI, Petersburg Nuclear Physics Institute, Gatchina, Leningrad region 188300, Russia
| | - Y Riabov
- PNPI, Petersburg Nuclear Physics Institute, Gatchina, Leningrad region 188300, Russia
- Saint Petersburg State Polytechnic University, St. Petersburg 195251 Russia
| | - D Richford
- Baruch College, City University of New York, New York, New York 10010, USA
| | - N Riveli
- Department of Physics and Astronomy, Ohio University, Athens, Ohio 45701, USA
| | - D Roach
- Vanderbilt University, Nashville, Tennessee 37235, USA
| | - S D Rolnick
- University of California-Riverside, Riverside, California 92521, USA
| | - M Rosati
- Iowa State University, Ames, Iowa 50011, USA
| | - Z Rowan
- Baruch College, City University of New York, New York, New York 10010, USA
| | - J G Rubin
- Department of Physics, University of Michigan, Ann Arbor, Michigan 48109-1040, USA
| | - J Runchey
- Iowa State University, Ames, Iowa 50011, USA
| | - N Saito
- KEK, High Energy Accelerator Research Organization, Tsukuba, Ibaraki 305-0801, Japan
| | - T Sakaguchi
- Physics Department, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - H Sako
- Advanced Science Research Center, Japan Atomic Energy Agency, 2-4 Shirakata Shirane, Tokai-mura, Naka-gun, Ibaraki-ken 319-1195, Japan
| | - V Samsonov
- National Research Nuclear University, MEPhI, Moscow Engineering Physics Institute, Moscow 115409, Russia
- PNPI, Petersburg Nuclear Physics Institute, Gatchina, Leningrad region 188300, Russia
| | - M Sarsour
- Georgia State University, Atlanta, Georgia 30303, USA
| | - S Sato
- Advanced Science Research Center, Japan Atomic Energy Agency, 2-4 Shirakata Shirane, Tokai-mura, Naka-gun, Ibaraki-ken 319-1195, Japan
| | - S Sawada
- KEK, High Energy Accelerator Research Organization, Tsukuba, Ibaraki 305-0801, Japan
| | - B Schaefer
- Vanderbilt University, Nashville, Tennessee 37235, USA
| | - B K Schmoll
- University of Tennessee, Knoxville, Tennessee 37996, USA
| | - K Sedgwick
- University of California-Riverside, Riverside, California 92521, USA
| | - J Seele
- RIKEN BNL Research Center, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - R Seidl
- RIKEN Nishina Center for Accelerator-Based Science, Wako, Saitama 351-0198, Japan
- RIKEN BNL Research Center, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - A Sen
- Iowa State University, Ames, Iowa 50011, USA
- University of Tennessee, Knoxville, Tennessee 37996, USA
| | - R Seto
- University of California-Riverside, Riverside, California 92521, USA
| | - P Sett
- Bhabha Atomic Research Centre, Bombay 400 085, India
| | - A Sexton
- University of Maryland, College Park, Maryland 20742, USA
| | - D Sharma
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - I Shein
- IHEP Protvino, State Research Center of Russian Federation, Institute for High Energy Physics, Protvino 142281, Russia
| | - M Shibata
- Nara Women's University, Kita-uoya Nishi-machi Nara 630-8506, Japan
| | - T-A Shibata
- RIKEN Nishina Center for Accelerator-Based Science, Wako, Saitama 351-0198, Japan
- Department of Physics, Tokyo Institute of Technology, Oh-okayama, Meguro, Tokyo 152-8551, Japan
| | - K Shigaki
- Hiroshima University, Kagamiyama, Higashi-Hiroshima 739-8526, Japan
| | - M Shimomura
- Iowa State University, Ames, Iowa 50011, USA
- Nara Women's University, Kita-uoya Nishi-machi Nara 630-8506, Japan
| | - Z Shi
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - P Shukla
- Bhabha Atomic Research Centre, Bombay 400 085, India
| | - A Sickles
- Physics Department, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
- University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - C L Silva
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - D Silvermyr
- Department of Physics, Lund University, Box 118, SE-221 00 Lund, Sweden
- Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
| | - B K Singh
- Department of Physics, Banaras Hindu University, Varanasi 221005, India
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- Department of Physics, Banaras Hindu University, Varanasi 221005, India
| | - V Singh
- Department of Physics, Banaras Hindu University, Varanasi 221005, India
| | - M Slunečka
- Charles University, Faculty of Mathematics and Physics, 180 00 Troja, Prague, Czech Republic
| | - K L Smith
- Florida State University, Tallahassee, Florida 32306, USA
| | - R A Soltz
- Lawrence Livermore National Laboratory, Livermore, California 94550, USA
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- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - S P Sorensen
- University of Tennessee, Knoxville, Tennessee 37996, USA
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- Physics Department, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
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- Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
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- Department of Physics, University of Massachusetts, Amherst, Massachusetts 01003-9337, USA
| | - S P Stoll
- Physics Department, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - T Sugitate
- Hiroshima University, Kagamiyama, Higashi-Hiroshima 739-8526, Japan
| | - A Sukhanov
- Physics Department, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - T Sumita
- RIKEN Nishina Center for Accelerator-Based Science, Wako, Saitama 351-0198, Japan
| | - J Sun
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - Z Sun
- Debrecen University, H-4010 Debrecen, Egyetem tér 1, Hungary
| | - J Sziklai
- Institute for Particle and Nuclear Physics, Wigner Research Centre for Physics, Hungarian Academy of Sciences (Wigner RCP, RMKI) H-1525 Budapest 114, P.O. Box 49, Budapest, Hungary
| | - R Takahama
- Nara Women's University, Kita-uoya Nishi-machi Nara 630-8506, Japan
| | - A Takahara
- Center for Nuclear Study, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo 113-0033, Japan
| | - A Taketani
- RIKEN Nishina Center for Accelerator-Based Science, Wako, Saitama 351-0198, Japan
- RIKEN BNL Research Center, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
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- Advanced Science Research Center, Japan Atomic Energy Agency, 2-4 Shirakata Shirane, Tokai-mura, Naka-gun, Ibaraki-ken 319-1195, Japan
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| | - M J Tannenbaum
- Physics Department, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - S Tarafdar
- Vanderbilt University, Nashville, Tennessee 37235, USA
- Weizmann Institute, Rehovot 76100, Israel
| | - A Taranenko
- National Research Nuclear University, MEPhI, Moscow Engineering Physics Institute, Moscow 115409, Russia
- Chemistry Department, Stony Brook University, SUNY, Stony Brook, New York 11794-3400, USA
| | - A Timilsina
- Iowa State University, Ames, Iowa 50011, USA
| | - T Todoroki
- RIKEN Nishina Center for Accelerator-Based Science, Wako, Saitama 351-0198, Japan
- RIKEN BNL Research Center, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
- Tomonaga Center for the History of the Universe, University of Tsukuba, Tsukuba, Ibaraki 305, Japan
| | - M Tomášek
- Czech Technical University, Zikova 4, 166 36 Prague 6, Czech Republic
| | - H Torii
- Center for Nuclear Study, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo 113-0033, Japan
| | - M Towell
- Abilene Christian University, Abilene, Texas 79699, USA
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- Abilene Christian University, Abilene, Texas 79699, USA
| | - R S Towell
- Abilene Christian University, Abilene, Texas 79699, USA
| | - I Tserruya
- Weizmann Institute, Rehovot 76100, Israel
| | - Y Ueda
- Hiroshima University, Kagamiyama, Higashi-Hiroshima 739-8526, Japan
| | - B Ujvari
- Debrecen University, H-4010 Debrecen, Egyetem tér 1, Hungary
| | - H W van Hecke
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - M Vargyas
- ELTE, Eötvös Loránd University, H-1117 Budapest, Pázmány P. s. 1/A, Hungary
- Institute for Particle and Nuclear Physics, Wigner Research Centre for Physics, Hungarian Academy of Sciences (Wigner RCP, RMKI) H-1525 Budapest 114, P.O. Box 49, Budapest, Hungary
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- Vanderbilt University, Nashville, Tennessee 37235, USA
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- Czech Technical University, Zikova 4, 166 36 Prague 6, Czech Republic
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- Institute of Physics, Academy of Sciences of the Czech Republic, Na Slovance 2, 182 21 Prague 8, Czech Republic
| | - E Vznuzdaev
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| | - Y Watanabe
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- RIKEN BNL Research Center, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - Y S Watanabe
- Center for Nuclear Study, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo 113-0033, Japan
- KEK, High Energy Accelerator Research Organization, Tsukuba, Ibaraki 305-0801, Japan
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| | - S Wolin
- University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
| | - C P Wong
- Georgia State University, Atlanta, Georgia 30303, USA
- Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
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- Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
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- Department of Physics and Astronomy, Ohio University, Athens, Ohio 45701, USA
| | - L Xue
- Georgia State University, Atlanta, Georgia 30303, USA
| | - S Yalcin
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - Y L Yamaguchi
- Center for Nuclear Study, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo 113-0033, Japan
- Department of Physics and Astronomy, Stony Brook University, SUNY, Stony Brook, New York 11794-3800, USA
| | - A Yanovich
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| | - I Yoon
- Department of Physics and Astronomy, Seoul National University, Seoul 151-742, Korea
| | - I Younus
- Physics Department, Lahore University of Management Sciences, Lahore 54792, Pakistan
| | - I E Yushmanov
- National Research Center "Kurchatov Institute," Moscow 123098, Russia
| | - W A Zajc
- Columbia University, New York, New York 10027 and Nevis Laboratories, Irvington, New York 10533, USA
| | - A Zelenski
- Collider-Accelerator Department, Brookhaven National Laboratory, Upton, New York 11973-5000, USA
| | - L Zou
- University of California-Riverside, Riverside, California 92521, USA
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Ding L, Zhao X, Guo S, Liu Y, Liu L, Wang Y, Peng H. SNAP: a structure-based neuron morphology reconstruction automatic pruning pipeline. Front Neuroinform 2023; 17:1174049. [PMID: 37388757 PMCID: PMC10303825 DOI: 10.3389/fninf.2023.1174049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 05/22/2023] [Indexed: 07/01/2023] Open
Abstract
Background Neuron morphology analysis is an essential component of neuron cell-type definition. Morphology reconstruction represents a bottleneck in high-throughput morphology analysis workflow, and erroneous extra reconstruction owing to noise and entanglements in dense neuron regions restricts the usability of automated reconstruction results. We propose SNAP, a structure-based neuron morphology reconstruction pruning pipeline, to improve the usability of results by reducing erroneous extra reconstruction and splitting entangled neurons. Methods For the four different types of erroneous extra segments in reconstruction (caused by noise in the background, entanglement with dendrites of close-by neurons, entanglement with axons of other neurons, and entanglement within the same neuron), SNAP incorporates specific statistical structure information into rules for erroneous extra segment detection and achieves pruning and multiple dendrite splitting. Results Experimental results show that this pipeline accomplishes pruning with satisfactory precision and recall. It also demonstrates good multiple neuron-splitting performance. As an effective tool for post-processing reconstruction, SNAP can facilitate neuron morphology analysis.
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Affiliation(s)
- Liya Ding
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Xuan Zhao
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Shuxia Guo
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Yufeng Liu
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Lijuan Liu
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Yimin Wang
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
- Guangdong Institute of Intelligence Science and Technology, Zhuhai, China
| | - Hanchuan Peng
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
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Qi X, Muñoz-Castañeda R, Narasimhan A, Ding L, Chen X, Elowsky C, Palmer J, Drewes R, Sun J, Mizrachi J, Peng H, Wu Z, Osten P. High-throughput confocal airy beam oblique light-sheet tomography of brain-wide imaging at single-cell resolution. bioRxiv 2023:2023.06.04.543586. [PMID: 37333143 PMCID: PMC10274617 DOI: 10.1101/2023.06.04.543586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Brain research is an area of research characterized by its cutting-edge nature, with brain mapping constituting a crucial aspect of this field. As sequencing tools have played a crucial role in gene sequencing, brain mapping largely depends on automated, high-throughput and high-resolution imaging techniques. Over the years, the demand for high-throughput imaging has scaled exponentially with the rapid development of microscopic brain mapping. In this paper, we introduce the novel concept of confocal Airy beam into oblique light-sheet tomography named CAB-OLST. We demonstrate that this technique enables the high throughput of brain-wide imaging of long-distance axon projection for the entire mouse brain at a resolution of 0.26 μm × 0.26 μm × 1.06 μm in 58 hours. This technique represents an innovative contribution to the field of brain research by setting a new standard for high-throughput imaging techniques.
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An H, Ding L, Ma M, Huang A, Gan Y, Sheng D, Jiang Z, Zhang X. Deep Learning-Based Recognition of Cervical Squamous Interepithelial Lesions. Diagnostics (Basel) 2023; 13:diagnostics13101720. [PMID: 37238206 DOI: 10.3390/diagnostics13101720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 05/05/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023] Open
Abstract
Cervical squamous intraepithelial lesions (SILs) are precursor lesions of cervical cancer, and their accurate diagnosis enables patients to be treated before malignancy manifests. However, the identification of SILs is usually laborious and has low diagnostic consistency due to the high similarity of pathological SIL images. Although artificial intelligence (AI), especially deep learning algorithms, has drawn a lot of attention for its good performance in cervical cytology tasks, the use of AI for cervical histology is still in its early stages. The feature extraction, representation capabilities, and use of p16 immunohistochemistry (IHC) among existing models are inadequate. Therefore, in this study, we first designed a squamous epithelium segmentation algorithm and assigned the corresponding labels. Second, p16-positive area of IHC slides were extracted with Whole Image Net (WI-Net), followed by mapping the p16-positive area back to the H&E slides and generating a p16-positive mask for training. Finally, the p16-positive areas were inputted into Swin-B and ResNet-50 to classify the SILs. The dataset comprised 6171 patches from 111 patients; patches from 80% of the 90 patients were used for the training set. The accuracy of the Swin-B method for high-grade squamous intraepithelial lesion (HSIL) that we propose was 0.914 [0.889-0.928]. The ResNet-50 model for HSIL achieved an area under the receiver operating characteristic curve (AUC) of 0.935 [0.921-0.946] at the patch level, and the accuracy, sensitivity, and specificity were 0.845, 0.922, and 0.829, respectively. Therefore, our model can accurately identify HSIL, assisting the pathologist in solving actual diagnostic issues and even directing the follow-up treatment of patients.
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Affiliation(s)
- Huimin An
- Department of Pathology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Liya Ding
- Department of Pathology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Mengyuan Ma
- Zhejiang Dahua Technology Co., Ltd., Hangzhou 310053, China
| | - Aihua Huang
- Department of Pathology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Yi Gan
- Department of Pathology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Danli Sheng
- Department of Pathology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Zhinong Jiang
- Department of Pathology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Xin Zhang
- Department of Pathology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
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Huang X, Mao Z, Li B, Hu M, Wang P, Ding L. 94P Neoadjuvant tislelizumab combined with (nab)-paclitaxel plus platinum-based chemotherapy for patients with stage IIA–IIIB squamous NSCLC: A real-world retrospective study. J Thorac Oncol 2023. [DOI: 10.1016/s1556-0864(23)00349-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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12
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Li YW, Li Z, Song HC, Ding L, Ji SS, Zhang M, Qu YL, Sun Q, Zhu YD, Fu H, Cai JY, Li CF, Han YY, Zhang WL, Zhao F, Lyu YB, Shi XM. [Association between urinary arsenic level and serum testosterone in Chinese men aged 18 to 79 years]. Zhonghua Yu Fang Yi Xue Za Zhi 2023; 57:686-692. [PMID: 36977566 DOI: 10.3760/cma.j.cn112150-20221110-01095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
Objective: To investigate the association between the urinary arsenic level and serum total testosterone in Chinese men aged 18 to 79 years. Methods: A total of 5 048 male participants aged 18 to 79 years were recruited from the China National Human Biomonitoring (CNHBM) from 2017 to 2018. Questionnaires and physical examinations were used to collect information on demographic characteristics, lifestyle, food intake frequency and health status. Venous blood and urine samples were collected to detect the level of serum total testosterone, urine arsenic and urine creatinine. Participants were divided into three groups (low, middle, and high) based on the tertiles of creatinine-adjusted urine arsenic concentration. Weighted multiple linear regression was fitted to analyze the association of urinary arsenic with serum total testosterone. Results: The weighted average age of 5 048 Chinese men was (46.72±0.40) years. Geometric mean concentration (95%CI) of urinary arsenic, creatinine-adjusted urine arsenic and serum testosterone was 22.46 (20.08, 25.12) μg/L, 19.36 (16.92, 22.15) μg/L and 18.13 (17.42, 18.85) nmol/L, respectively. After controlling for covariates, compared with the low-level urinary arsenic group, the testosterone level of the participants in the middle-level group and the high-level group decreased gradually. The percentile ratio (95%CI) was -5.17% (-13.14%, 3.54%) and -10.33% (-15.68%, -4.63). The subgroup analysis showed that the association between the urinary arsenic level and testosterone level was more obvious in the group with BMI<24 kg/m2 group (Pinteraction<0.05). Conclusion: There is a negative association between the urinary arsenic level and serum total testosterone in Chinese men aged 18-79 years.
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Affiliation(s)
- Y W Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Z Li
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H C Song
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - L Ding
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - S S Ji
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - M Zhang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y L Qu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Q Sun
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y D Zhu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - H Fu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - J Y Cai
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - C F Li
- School of Public Health, Anhui Medical University, Hefei 230032, China
| | - Y Y Han
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - W L Zhang
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - F Zhao
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Y B Lyu
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - X M Shi
- China CDC Key Laboratory of Environment and Population Health/National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Zhang MX, Wang JH, Zhang L, Yan JX, Wu CH, Pei RX, Lyu YJ, Song L, Cui M, Ding L, Wang ZL, Wang JT. [The characteristics and correlations of vaginal flora in women with cervical lesions]. Zhonghua Zhong Liu Za Zhi 2023; 45:253-258. [PMID: 36944546 DOI: 10.3760/cma.j.cn112152-20211024-00782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
Objective: To explore the characteristics and correlations of vaginal flora in women with cervical lesions. Methods: A total of 132 women, including 41 women diagnosed with normal cervical (NC), 39 patients with low-grade cervical intraepithelial neoplasia (CIN 1), 37 patients with high-grade cervical intraepithelial neoplasia (CIN 2/3) and 15 patients with cervical squamous cell carcinoma (SCC), who came from the gynecological clinic of Second Hospital of Shanxi Medical University during January 2018 to June 2018, were enrolled in this study according to the inclusive and exclusive criteria strictly. The vaginal flora was detected by 16S rDNA sequencing technology. Co-occurrence network analysis was used to investigate the Spearman correlations between different genera of bacteria. Results: The dominant bacteria in NC, CIN 1 and CIN 2/3 groups were Lactobacillus [constituent ratios 79.4% (1 869 598/2 354 098), 63.6% (1 536 466/2 415 100) and 58.3% (1 342 896/2 301 536), respectively], while Peptophilus [20.4% (246 072/1 205 154) ] was the dominant bacteria in SCC group. With the aggravation of cervical lesions, the diversity of vaginal flora gradually increased (Shannon index: F=6.39, P=0.001; Simpson index: F=3.95, P=0.012). During the cervical lesion progress, the ratio of Lactobacillus gradually decreased, the ratio of other anaerobes such as Peptophilus, Sneathia, Prevotella and etc. gradually increased, and the differential bacteria (LDA score >3.5) gradually evolved from Lactobacillus to other anaerobes. The top 10 relative abundance bacteria, spearman correlation coefficient>0.4 and P<0.05 were selected. Co-occurrence network analysis showed that Prevotella, Peptophilus, Porphyrinomonas, Anaerococcus, Sneathia, Atopobium, Gardnerella and Streptococcus were positively correlated in different stages of cervical lesions, while Lactobacillus was negatively correlated with the above anaerobes. It was found that the relationship between vaginal floras in CIN 1 group was the most complex and only Peptophilus was significantly negatively correlated with Lactobacillus in SCC group. Conclusions: The increased diversity and changed correlations between vaginal floras are closely related to cervical lesions. Peptophilus is of great significance in the diagnosis, prediction and early warning of cervical carcinogenesis.
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Affiliation(s)
- M X Zhang
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - J H Wang
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - L Zhang
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - J X Yan
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - C H Wu
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - R X Pei
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - Y J Lyu
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - L Song
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - M Cui
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - L Ding
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - Z L Wang
- Department of Obstetrics and Gynecology, Second Hospital of Shanxi Medical University, Taiyuan 030001, China
| | - J T Wang
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
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Lu ZM, Sun ZY, Zhang S, Jiang X, Ding L, Li CZ, Tian XW, Wang QL. Lipolysis is accompanied by immune microenvironment remodeling in adipose tissue of obesity with different exercise intensity. Eur Rev Med Pharmacol Sci 2023; 27:867-878. [PMID: 36808332 DOI: 10.26355/eurrev_202302_31179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
OBJECTIVE Obesity and overweight are risk factors for chronic disease worldwide. The purpose of this study was to compare the transcriptome of exercise-induced fat mobilization in obese people, and to explore the effect of different exercise intensity on the correlation of immune microenvironment remodeling and lipolysis in adipose tissue. MATERIALS AND METHODS Microarray datasets of adipose tissue before and after exercise were downloaded from the Gene Expression Omnibus. Then, we used gene-enrichment analysis and PPI-network construction to elucidate the function and enrichment pathways of the differentially expressed genes (DEGs) and to identify the central genes. A network of protein-protein interactions was obtained using STRING and visualized with Cytoscape. RESULTS A total of 929 DEGs were identified between 40 pre-exercise (BX) samples and 65 post-exercise (AX) samples from GSE58559, GSE116801, and GSE43471. Among these DEGs, adipose tissue-expressed genes were duly recognized. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicated that DEGs were mostly enriched in lipid metabolism. Studies have found that mitogen-activated protein kinase (MAPK) signaling pathway and forkhead box O (FOXO) signaling pathway are up-regulated, while Ribosome, coronavirus disease (COVID-19) and IGF-1 gene are down-regulated. Although we found the up-regulated genes that noted IL-1 among others, and the down-regulated gene was IL-34. The increase of inflammatory factors leads to changes in cellular immune microenvironment, and high-intensity exercise leads to increased expression of inflammatory factors in adipose tissue, leading to inflammatory responses. CONCLUSIONS Exercise at different intensities leads to the degradation of adipose and is accompanied by changes in the immune microenvironment within adipose tissue. High intensity exercise can cause the imbalance of immune microenvironment of adipose tissue while causing fat degradation. Therefore, moderate intensity and below exercise is the best way for the general population to reduce fat and weight.
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Affiliation(s)
- Z-M Lu
- College of Sports and Health, Shandong Sport University, Jinan, China.
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15
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Jiang XT, Ding L, Zhu Y. [Research progress on risk factors of post-pancreatitis diabetes mellitus after acute pancreatitis]. Zhonghua Nei Ke Za Zhi 2023; 62:212-216. [PMID: 36740415 DOI: 10.3760/cma.j.cn112138-20220729-00555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- X T Jiang
- Department of Gastroenterology, Digestive Disease Hospital, the First Affiliated Hospital of Nanchang University, Nanchang 330006,China
| | - L Ding
- Department of Gastroenterology, Digestive Disease Hospital, the First Affiliated Hospital of Nanchang University, Nanchang 330006,China
| | - Y Zhu
- Department of Gastroenterology, Digestive Disease Hospital, the First Affiliated Hospital of Nanchang University, Nanchang 330006,China
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Ding L, Wang Q, Martincuks A, Kearns MJ, Jiang T, Lin Z, Cheng X, Qian C, Xie S, Kim HJ, Launonen IM, Färkkilä A, Roberts TM, Freeman GJ, Liu JF, Konstantinopoulos PA, Matulonis U, Yu H, Zhao JJ. STING agonism overcomes STAT3-mediated immunosuppression and adaptive resistance to PARP inhibition in ovarian cancer. J Immunother Cancer 2023; 11:e005627. [PMID: 36609487 PMCID: PMC9827255 DOI: 10.1136/jitc-2022-005627] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/12/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Poly (ADP-ribose) polymerase (PARP) inhibition (PARPi) has demonstrated potent therapeutic efficacy in patients with BRCA-mutant ovarian cancer. However, acquired resistance to PARPi remains a major challenge in the clinic. METHODS PARPi-resistant ovarian cancer mouse models were generated by long-term treatment of olaparib in syngeneic Brca1-deficient ovarian tumors. Signal transducer and activator of transcription 3 (STAT3)-mediated immunosuppression was investigated in vitro by co-culture experiments and in vivo by analysis of immune cells in the tumor microenvironment (TME) of human and mouse PARPi-resistant tumors. Whole genome transcriptome analysis was performed to assess the antitumor immunomodulatory effect of STING (stimulator of interferon genes) agonists on myeloid cells in the TME of PARPi-resistant ovarian tumors. A STING agonist was used to overcome STAT3-mediated immunosuppression and acquired PARPi resistance in syngeneic and patient-derived xenografts models of ovarian cancer. RESULTS In this study, we uncover an adaptive resistance mechanism to PARP inhibition mediated by tumor-associated macrophages (TAMs) in the TME. Markedly increased populations of protumor macrophages are found in BRCA-deficient ovarian tumors that rendered resistance to PARPi in both murine models and patients. Mechanistically, PARP inhibition elevates the STAT3 signaling pathway in tumor cells, which in turn promotes protumor polarization of TAMs. STAT3 ablation in tumor cells mitigates polarization of protumor macrophages and increases tumor-infiltrating T cells on PARP inhibition. These findings are corroborated in patient-derived, PARPi-resistant BRCA1-mutant ovarian tumors. Importantly, STING agonists reshape the immunosuppressive TME by reprogramming myeloid cells and overcome the TME-dependent adaptive resistance to PARPi in ovarian cancer. This effect is further enhanced by addition of the programmed cell death protein-1 blockade. CONCLUSIONS We elucidate an adaptive immunosuppression mechanism rendering resistance to PARPi in BRCA1-mutant ovarian tumors. This is mediated by enrichment of protumor TAMs propelled by PARPi-induced STAT3 activation in tumor cells. We also provide a new strategy to reshape the immunosuppressive TME with STING agonists and overcome PARPi resistance in ovarian cancer.
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Affiliation(s)
- Liya Ding
- Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Biological Chemistry & Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Qiwei Wang
- Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Biological Chemistry & Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts, USA
| | - Antons Martincuks
- Immuno-Oncology, City of Hope Comprehensive Cancer Center, Duarte, California, USA
| | - Michael J Kearns
- Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Tao Jiang
- Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Ziying Lin
- Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Respiratory and Critical Care Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Xin Cheng
- Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Biological Chemistry & Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts, USA
| | - Changli Qian
- Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Shaozhen Xie
- Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Hye-Jung Kim
- Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | | | - Anniina Färkkilä
- Obstetrics and Gynecology, University of Helsinki, Helsinki, Finland
| | - Thomas M Roberts
- Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Biological Chemistry & Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts, USA
| | - Gordon J Freeman
- Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Joyce F Liu
- Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | | | - Ursula Matulonis
- Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Hua Yu
- Immuno-Oncology, City of Hope Comprehensive Cancer Center, Duarte, California, USA
| | - Jean J Zhao
- Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Biological Chemistry & Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
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Zhang X, Ding L, Hu H, He H, Xiong Z, Zhu X. Associations of Body-Roundness Index and Sarcopenia with Cardiovascular Disease among Middle-Aged and Older Adults: Findings from CHARLS. J Nutr Health Aging 2023; 27:953-959. [PMID: 37997715 DOI: 10.1007/s12603-023-2001-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 09/19/2023] [Indexed: 11/25/2023]
Abstract
OBJECTIVES Sarcopenia and obesity may contribute to chronic disease. However, little is known about the association between sarcopenia, body roundness index (BRI), and cardiovascular disease (CVD). The aim of this study was to investigate the association of sarcopenia and BRI with CVD in middle-aged and older Chinese population. DESIGN Cohort study with an 8-year follow-up. SETTING AND PARTICIPANTS Data were derived from 4 waves of the China Health and Retirement Longitudinal Study, and 6152 participants aged 45 or above were included in the study. METHODS Sarcopenia was defined according to the Asian Working Group for Sarcopenia 2019 criteria. CVD was defined as the presence of physician-diagnosed heart disease, diabetes and/or stroke. The associations of BRI and sarcopenia with CVD risk were explored using Cox proportional hazards regression models. RESULTS The mean age of the participants was 58.3 (8.9) years, and 2936 (47.7%) were males. During the 8 years follow-up, 2385 cases (38.8%) with incident CVD were identified. Longitudinal results demonstrated that compared to neither sarcopenia or high BRI, both sarcopenia and high BRI (HR: 1.49, 95%CI: 1.08, 2.07) were associated with higher risk of CVD. In the subgroup analysis, individuals with both sarcopenia and high BRI were more likely to have new onset stroke (HR: 1.93, 95%CI: 1.12, 3.32) and increased risk of multimorbidity (HR: 2.15, 95% CI: 1.14, 4.04). CONCLUSIONS Coexistence of sarcopenia and high BRI was associated with higher risk of CVD. Early identification and intervention for sarcopenia and BRI not only allows the implementation of therapeutic strategies, but also provides an opportunity to mitigate the risk of developing CVD.
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Affiliation(s)
- X Zhang
- Pro. Zhenfang Xiong and Pro. Xinhong Zhu, #1 Huangjiahu west road, Wuhan, China, phone: +86027-688890395., Pro. Zhenfang Xiong, E-mail: , Pro. Xinhong Zhu, E-mail:
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Zhu X, Zhang X, Ding L, Tang Y, Xu A, Yang F, Qiao G, Gao X, Zhou J. Associations of Pain and Sarcopenia with Successful Aging among Older People in China: Evidence from CHARLS. J Nutr Health Aging 2023; 27:196-201. [PMID: 36973927 DOI: 10.1007/s12603-023-1892-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
OBJECTIVES Sarcopenia and chronic pain are geriatric syndromes that negatively impact the lives of older people. The aim of this study was to explore the relationship among sarcopenia, pain, and successful aging among older persons participating in the China Health and Retirement Longitudinal Study (CHARLS). DESIGN Cohort study with a 2-year follow-up. SETTING AND PARTICIPANTS Data were derived from 2 waves of the CHARLS, and 4280 community-dwelling participants aged ≥ 60 years were included in the study. METHODS Sarcopenia status was defined according to the Asian Working Group for Sarcopenia 2019 (AWGS 2019) criteria. Successful aging was defined following Rowe and Kahn's multidimensional model. Pain was assessed by a self-reported questionnaire. A generalized estimating equation (GEE) was used to examine the associations. RESULTS Longitudinal results demonstrated that compared with no sarcopenia, possible sarcopenia [OR (95%CI): 0.600 (0.304~1.188)] was not significantly associated with successful aging. Pain only was strongly associated with successful aging [0.388 (0.251~0.600)], whereas the association between sarcopenia only and successful aging was weaker [0.509 (0.287~0.905)]. The likelihood of being successful aging was substantially lower in the presence of coexisting sarcopenia and pain [0.268 (0.108~0.759)]. CONCLUSIONS Both pain and sarcopenia are significant predictors for achieving successful aging among community-dwelling older adults. Early identification of sarcopenia and pain permits the implementation of treatment strategies and presents an opportunity to mitigate the risk of being unsuccessful aging.
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Affiliation(s)
- X Zhu
- Xinhong Zhu, Nursing Educator, School of Nursing, Hubei University of Chinese Medicine, Wuhan, China, phone: +86027-688890395;
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Li Y, Jiang S, Ding L, Liu L. NRRS: a re-tracing strategy to refine neuron reconstruction. Bioinform Adv 2023; 3:vbad054. [PMID: 37213868 PMCID: PMC10199312 DOI: 10.1093/bioadv/vbad054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 04/04/2023] [Accepted: 04/24/2023] [Indexed: 05/23/2023]
Abstract
It is crucial to develop accurate and reliable algorithms for fine reconstruction of neural morphology from whole-brain image datasets. Even though the involvement of human experts in the reconstruction process can help to ensure the quality and accuracy of the reconstructions, automated refinement algorithms are necessary to handle substantial deviations problems of reconstructed branches and bifurcation points from the large-scale and high-dimensional nature of the image data. Our proposed Neuron Reconstruction Refinement Strategy (NRRS) is a novel approach to address the problem of deviation errors in neuron morphology reconstruction. Our method partitions the reconstruction into fixed-size segments and resolves the deviation problems by re-tracing in two steps. We also validate the performance of our method using a synthetic dataset. Our results show that NRRS outperforms existing solutions and can handle most deviation errors. We apply our method to SEU-ALLEN/BICCN dataset containing 1741 complete neuron reconstructions and achieve remarkable improvements in the accuracy of the neuron skeleton representation, the task of radius estimation and axonal bouton detection. Our findings demonstrate the critical role of NRRS in refining neuron morphology reconstruction. Availability and implementation The proposed refinement method is implemented as a Vaa3D plugin and the source code are available under the repository of vaa3d_tools/hackathon/Levy/refinement. The original fMOST images of mouse brains can be found at the BICCN's Brain Image Library (BIL) (https://www.brainimagelibrary.org). The synthetic dataset is hosted on GitHub (https://github.com/Vaa3D/vaa3d_tools/tree/master/hackathon/Levy/refinement). Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
| | | | - Liya Ding
- Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China
| | - Lijuan Liu
- To whom correspondence should be addressed.
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Liu Y, Zhong Y, Zhao X, Liu L, Ding L, Peng H. Tracing weak neuron fibers. Bioinformatics 2022; 39:6960919. [PMID: 36571479 PMCID: PMC9848051 DOI: 10.1093/bioinformatics/btac816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 11/01/2022] [Accepted: 12/23/2022] [Indexed: 12/27/2022] Open
Abstract
MOTIVATION Precise reconstruction of neuronal arbors is important for circuitry mapping. Many auto-tracing algorithms have been developed toward full reconstruction. However, it is still challenging to trace the weak signals of neurite fibers that often correspond to axons. RESULTS We proposed a method, named the NeuMiner, for tracing weak fibers by combining two strategies: an online sample mining strategy and a modified gamma transformation. NeuMiner improved the recall of weak signals (voxel values <20) by a large margin, from 5.1 to 27.8%. This is prominent for axons, which increased by 6.4 times, compared to 2.0 times for dendrites. Both strategies were shown to be beneficial for weak fiber recognition, and they reduced the average axonal spatial distances to gold standards by 46 and 13%, respectively. The improvement was observed on two prevalent automatic tracing algorithms and can be applied to any other tracers and image types. AVAILABILITY AND IMPLEMENTATION Source codes of NeuMiner are freely available on GitHub (https://github.com/crazylyf/neuronet/tree/semantic_fnm). Image visualization, preprocessing and tracing are conducted on the Vaa3D platform, which is accessible at the Vaa3D GitHub repository (https://github.com/Vaa3D). All training and testing images are cropped from high-resolution fMOST mouse brains downloaded from the Brain Image Library (https://www.brainimagelibrary.org/), and the corresponding gold standards are available at https://doi.brainimagelibrary.org/doi/10.35077/g.25. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yufeng Liu
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China
| | - Ye Zhong
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China
| | - Xuan Zhao
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China
| | - Lijuan Liu
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China
| | - Liya Ding
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China
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Guo CY, Wang JT, Ran ZX, Gong L, Zhu JJ, Li DC, Ding L. [The correlation between methylation in HPV16 long control region and cervical intraepithelial neoplasia grade 2 or more: a Meta-analysis]. Zhonghua Liu Xing Bing Xue Za Zhi 2022; 43:1821-1827. [PMID: 36444468 DOI: 10.3760/cma.j.cn112338-20220307-00172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Objective: To investigate the correlation between methylation in human papillomavirus 16 (HPV16) long control region (LCR) and cervical intraepithelial neoplasia grade ≥2 (CIN2+). Methods: The literature retrieval was conducted by using the databases of PubMed, Embase, Cochrane Library, Web of Science, CNKI, Wanfang data and Weipu according to the inclusion and exclusion criteria, and the retrieval period was from the establishment of the databases to February 27th, 2022. Software RevMan 5.3 and Stata 15.1 were used for Meta-analysis. Results: A total of 17 literatures were included involving 1 421 subjects. Results of Meta-analysis showed that OR of the correlation between methylation of HPV16 LCR and CIN2+ was 1.56 (95%CI: 0.70-3.47). Subgroup analysis showed that methylation of the 5' terminal, enhancer and promoter regions were not associated with CIN2+, while in four E2 binding sites (E2BS), the methylation of E2BS1, E2BS3 and E2BS4 increased the risk of CIN2+, with the ORs of 3.92 (95%CI: 1.92-7.99), 10.50 (95%CI: 3.67-30.04) and 3.65 (95%CI: 1.58-8.41), respectively. However, subgroup analysis on E2BS2 was not performed due to the limitation of the number of literatures. According to the different sources of population, the risk of CIN2+ in Chinese population was associated with methylation of HPV16 LCR (OR=2.14, 95%CI: 1.31-3.50). There was a correlation between the risk of CIN2+ and HPV16 LCR methylation in the population with pyrosequencing of HPV16 LCR, and OR was 1.75 (95%CI: 1.03-2.98). Conclusion: The risk of CIN2+ is correlated with the methylation of E2BS in HPV16 LCR, which can be used as potential biomarkers.
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Affiliation(s)
- C Y Guo
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - J T Wang
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - Z X Ran
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - L Gong
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - J J Zhu
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - D C Li
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - L Ding
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
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Yang Y, Jin L, Rao N, Gong C, Li S, Li Y, Wu J, Zhao J, Ding L, Liu Q. 192P A phase II single-arm clinical study of neoadjuvant treatment with pegylated liposomal doxorubicin (PLD) plus cyclophosphamide (C) combined with trastuzumab (H) and pertuzumab (P) in HER2-positive (HER2+) breast cancer (BC). Ann Oncol 2022. [DOI: 10.1016/j.annonc.2022.07.227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022] Open
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Ding L, Shen Y, Jawad M, Wu T, Maloney SK, Wang M, Chen N, Blache D. Effect of arginine supplementation on the production of milk fat in dairy cows. J Dairy Sci 2022; 105:8115-8129. [PMID: 35965125 DOI: 10.3168/jds.2021-21312] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 05/25/2022] [Indexed: 11/19/2022]
Abstract
Arginine, one of the conditionally essential AA, has been reported to affect fat synthesis and metabolism in nonruminant animals by influencing adenosine monophosphate activated protein kinase (AMPK) in some organs. In dairy cows, the effect of Arg on milk fat production is not clear, and any potential mechanism that underlies the effect is unknown. We tested the hypothesis that Arg infusion would improve the production of milk fat, and explored possible mechanism that might underlie any effect. We used 6 healthy lactating cows at 20 ± 2 d in milk, in fourth parity, with a body weight of 508 ± 14 kg, body condition score of 3.0 ± 0, and a milk yield of 30.6 ± 1.8 kg/d (mean ± standard deviation). The cows were blocked by days in milk and milk yield and each cow received 3 treatments in a replicated 3 × 3 Latin square design, with each of the experimental periods lasting 7 d with a 14-d washout between each period. The treatments, delivered in random order, were (1) infusion of saline (control); (2) infusion of 0.216 mol/d of l-Arg in saline (Arg); (3) infusion of 0.868 mol/d of l-Ala in saline (the Arg and Ala treatments were iso-nitrogenous) through a jugular vein. On the last day of each experimental period, blood was sampled to measure insulin, nitric oxide, glucose, and nonesterified fatty acid, and the liver and mammary gland were biopsied to measure the expression of genes. Milk yield was recorded, and milk fat percentage was measured daily during each of the experimental periods. The yield and composition of fatty acid (FA) in milk was measured daily on the last 3 d during each of the experimental periods. The data were analyzed using a mixed model with treatment as a fixed factor, and cow, period, and block as random factors. The daily milk yield and milk fat yield when the cows were infused with Arg were 2.2 kg and 76 g, respectively, higher than that in control, and 1.8 kg and 111 g, respectively, higher than that in Ala. When the cows were infused with Arg they had higher concentration and yield of de novo synthesized FA, than when they received the control or Ala infusions, although milk fat percentage, daily feed intake, and the digestibility of nutrients were not affected by treatment. The serum concentration of nitric oxide and insulin were higher during Arg than during control or Ala, with no difference between control and Ala. In the liver, the expression of the genes coding for AMPK (PRKAA1, PRKAB1, and PRKAG1) and genes related to the oxidation of FA were higher during Arg than during control or Ala, whereas in the mammary gland the expression PRKAB1 was lowest, and the expression of genes involved in the synthesis of milk fat were highest, during Arg infusion. The results suggest the intravenous infusion of Arg enhanced the production of milk fat by promoting the de novo synthesis of FA and increasing milk yield.
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Affiliation(s)
- L Ding
- College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, Jiangsu, P.R. China; State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural Reclamation Sciences, Shihezi, 832000, P.R. China; UWA Institute of Agriculture, The University of Western Australia, Perth 6009, WA, Australia; School of Agriculture and Environment, The University of Western Australia, Perth 6009, WA, Australia
| | - Y Shen
- College of Animal Science and Technology, Hebei Agricultural University, Baoding 071001, Hebei, P.R. China
| | - M Jawad
- College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, Jiangsu, P.R. China
| | - T Wu
- College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, Jiangsu, P.R. China
| | - S K Maloney
- UWA Institute of Agriculture, The University of Western Australia, Perth 6009, WA, Australia; School of Human Sciences, The University of Western Australia, Perth 6009, WA, Australia
| | - M Wang
- College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, Jiangsu, P.R. China; State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural Reclamation Sciences, Shihezi, 832000, P.R. China.
| | - N Chen
- State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural Reclamation Sciences, Shihezi, 832000, P.R. China.
| | - D Blache
- UWA Institute of Agriculture, The University of Western Australia, Perth 6009, WA, Australia; School of Agriculture and Environment, The University of Western Australia, Perth 6009, WA, Australia.
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Ding L, Greuter M, Truyen I, Goossens M, De Schutter H, de Bock G, Van Hal G. Irregular screening participation increases advanced stage breast cancer at diagnosis: A population-based study. Breast 2022; 65:61-66. [PMID: 35820298 PMCID: PMC9284440 DOI: 10.1016/j.breast.2022.07.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 06/28/2022] [Accepted: 07/05/2022] [Indexed: 01/22/2023] Open
Abstract
OBJECTIVE To evaluate the effect of irregular screening behaviour on the risk of advanced stage breast cancer at diagnosis in Flanders. METHODS All women aged 50-69 who were invited to the organized breast cancer screening and diagnosed with breast cancer before age 72 from 2001 to 2018 were included. All prevalent screen and interval cancers within 2 years of a prevalent screen were excluded. Screening behaviour was categorized based on the number of invitations and performed screenings. Four groups were defined: regular, irregular, only-once, and never attenders. Advanced stage cancer was defined as a stage III + breast cancer. The association between screening regularity and breast cancer stage at diagnosis was evaluated in multivariable logistic regression models, taking age of diagnosis and socio-economic status into account. RESULTS In total 13.5% of the 38,005 breast cancer cases were diagnosed at the advanced stage. Compared to the regular attenders, the risk of advanced stage breast cancer for the irregular attenders, women who participated only-once, and never attenders was significantly higher with ORadjusted:1.17 (95%CI:1.06-1.29) and ORadjusted:2.18 (95%CI:1.94-2.45), and ORadjusted:5.95 (95%CI:5.33-6.65), respectively. CONCLUSIONS In our study, never attenders were nearly six times more likely to be diagnosed with advanced stage breast cancer than regular attenders, which was much higher than the estimates published thus far. An explanation for this is that the ever screened women is a heterogeneous group regarding the participation profiles which also includes irregular and only-once attenders. The benefit of regular screening should be informed to all women invited for screening.
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Affiliation(s)
- L. Ding
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands,Department of Social Epidemiology and Health Policy, University of Antwerp, Antwerp, Belgium
| | - M.J.W. Greuter
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands,Department of Robotics and Mechatronics, University of Twente, Enschede, the Netherlands
| | - I. Truyen
- Belgian Cancer Registry, Brussels, Belgium
| | - M. Goossens
- Center for Cancer Detection (CvKO) in Flanders, Belgium,Vrije Universiteit Brussel, Brussels, Belgium
| | | | - G.H. de Bock
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands,Corresponding author.
| | - G. Van Hal
- Department of Social Epidemiology and Health Policy, University of Antwerp, Antwerp, Belgium,Center for Cancer Detection (CvKO) in Flanders, Belgium
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Gong Y, Zhou L, Ding L, Zhao J, Wang Z, Ren G, Zhang J, Mao Z, Zhou R. KIF23 is a potential biomarker of diffuse large B cell lymphoma: Analysis based on bioinformatics and immunohistochemistry. Medicine (Baltimore) 2022; 101:e29312. [PMID: 35713434 PMCID: PMC9276187 DOI: 10.1097/md.0000000000029312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Accepted: 05/04/2022] [Indexed: 11/26/2022] Open
Abstract
Diffuse Large B Cell Lymphoma (DLBCL), the most common form of blood cancer. The genetic and clinical heterogeneity of DLBCL poses a major barrier to diagnosis and treatment. Hence, we aim to identify potential biomarkers for DLBCL.Differentially expressed genes were screened between DLBCL and the corresponding normal tissues. Kyoto Encyclopedia of Genes and Genomes and Gene oncology analyses were performed to obtain an insight into these differentially expressed genes. PPI network was constructed to identify hub genes. survival analysis was applied to evaluate the prognostic value of those hub genes. DNA methylation analysis was implemented to explore the epigenetic dysregulation of genes in DLBCL.In this study, Kinesin family member 23 (KIF23) showed higher expression in DLBCL and was identified as a risk factor in DLBCL. The immunohistochemistry experiment further confirmed this finding. Subsequently, the univariate and multivariate analysis indicated that KIF23 might be an independent adverse factor in DLBCL. Upregulation of KIF23 might be a risk factor for the overall survival of patients who received an R-CHOP regimen, in late-stage, whatever with or without extranodal sites. Higher expression of KIF23 also significantly reduced 3, 5, 10-year overall survival. Furthermore, functional enrichment analyses (Kyoto Encyclopedia of Genes and Genomes, Gene oncology, and Gene Set Enrichment Analysis) showed that KIF23 was mainly involved in cell cycle, nuclear division, PI3K/AKT/mTOR, TGF-beta, and Wnt/beta-catenin pathway in DLBCL. Finally, results of DNA methylation analysis indicated that hypomethylation in KIF23's promoter region might be the result of its higher expression in DLBCL.The findings of this study suggested that KIF23 is a potential biomarker for the diagnosis and prognosis of DLBCL. However, further studies were needed to validate these findings.
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Affiliation(s)
- Yuqi Gong
- Department of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Pathology and Pathophysiology, Institute of Pathology and Forensic Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Lingna Zhou
- Department of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Pathology and Pathophysiology, Institute of Pathology and Forensic Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Liya Ding
- Department of Pathology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jing Zhao
- Department of Pathology and Pathophysiology, Institute of Pathology and Forensic Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhe Wang
- Department of Pathology, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Guoping Ren
- Department of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Pathology and Pathophysiology, Institute of Pathology and Forensic Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Jing Zhang
- Department of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Pathology and Pathophysiology, Institute of Pathology and Forensic Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhengrong Mao
- Department of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Pathology and Pathophysiology, Institute of Pathology and Forensic Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Ren Zhou
- Department of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Pathology and Pathophysiology, Institute of Pathology and Forensic Medicine, Zhejiang University School of Medicine, Hangzhou, China
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Ding L, Wang Q, Kearns M, Jiang T, Cheng X, Qian C, Zhao J. Abstract LB004: Upregulation of STAT3 signaling in tumor cells fosters a TME-dependent secondary resistance of BRCA1-deficient HGSOC to PARP inhibition. Cancer Res 2022. [DOI: 10.1158/1538-7445.am2022-lb004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
PARP inhibitors (PARPi) have demonstrated potent therapeutic efficacy in patients with ovarian cancer. However, acquired resistance to PARPi is a major challenge in the clinic. Therefore, understanding the resistance mechanism and developing new treatment approaches are important to overcome therapeutic resistance to PARP inhibition. By using our GEM of Brca1-deficient ovarian tumor model, we uncovered a new mechanism underlying a secondary resistance to PARP inhibition mediated by tumor associated macrophages (TAMs) in the tumor microenvironment (TME). Mechanistically, PARP inhibition induced the STAT3 signaling pathway in tumor cells, which in turn promoted pro-tumor polarization of TAMs in the TME of ovarian cancer. Ablation of STAT3 in tumor cells mitigated polarization of M2-like pro-tumor macrophages in the TME and increased tumor infiltration T cells in response to PARP inhibition. Furthermore, we demonstrated that STING agonists reprogramed myeloid cells in the TME of ovarian tumor into antitumor M1-like macrophages and activated dendritic cells (DCs) in a myeloid cell STING-dependent manner. These findings were recapitulated in patient-derived PARPi-resistant ovarian tumor models. Finally, we show that STING agonism was able to overcome the secondary resistance to PARPi in ovarian cancer rendered by immunosuppressive TME. Our study elucidates a new mechanism of PARPi resistance and provides a new treatment strategy to overcome the TME-induced therapeutic resistance to PARP inhibition in ovarian cancers.
Citation Format: Liya Ding, Qiwei Wang, Michael Kearns, Tao Jiang, Xin Cheng, Changli Qian, Jean Zhao. Upregulation of STAT3 signaling in tumor cells fosters a TME-dependent secondary resistance of BRCA1-deficient HGSOC to PARP inhibition [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB004.
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Affiliation(s)
- Liya Ding
- 1Dana-Farber Cancer Institute, Boston, MA
| | - Qiwei Wang
- 1Dana-Farber Cancer Institute, Boston, MA
| | | | - Tao Jiang
- 1Dana-Farber Cancer Institute, Boston, MA
| | - Xin Cheng
- 1Dana-Farber Cancer Institute, Boston, MA
| | | | - Jean Zhao
- 1Dana-Farber Cancer Institute, Boston, MA
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Xing P, Zheng X, Wang Y, Chu T, Wang S, Jiang J, Qian J, Han X, Ding L, Wang Y, Cui L, Li H, Li L, Chen X, Han B, Hu P, Shi Y. Safety, pharmacokinetics, and efficacy of BPI-15086 in patients with EGFR T790M-mutated advanced non-small-cell lung cancer: results from a phase I, single-arm, multicenter study. ESMO Open 2022; 7:100473. [PMID: 35526510 PMCID: PMC9271465 DOI: 10.1016/j.esmoop.2022.100473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 03/13/2022] [Accepted: 03/18/2022] [Indexed: 11/23/2022] Open
Abstract
Background Epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) resistance frequently occurs in patients with non-small-cell lung cancer (NSCLC). EGFR Thr790Met mutation (T790M+) is seen in ∼50% of patients. We assessed the safety, tolerability, and pharmacokinetics (PK) of BPI-15086, a novel, ATP-competitive, irreversible, third-generation, mutation-selective EGFR-TKI in patients with EGFR T790M-mutated NSCLC. Patients and methods This two-center, phase I, dose-escalation study included patients who were 18-65 years old, with an Eastern Cooperative Oncology Group performance status of 0-2, with histologically or cytologically confirmed locally advanced or metastatic T790M+ NSCLC who were not surgical or radiotherapy candidates, and had imaging-identified disease progression after prior EGFR-TKIs. This dose-escalation study enrolled patients using a 3 + 3 study design. Patients received 25, 50, 100, 200, and 300 mg/day orally in 21-day cycles. The primary endpoints were safety, tolerability, and PK. Secondary endpoints were objective response rate (ORR) and disease control rate (DCR). The dose-expansion study was not conducted. Results We enrolled 17 patients from 29 December 2016 to 16 May 2018, in the safety and full analysis sets. All patients completed a single dosing trial, and no adverse events (AEs) causing drug discontinuation were seen. Grade 1-2 nausea, hypoalbuminemia, and decreased appetite were the most common treatment-related AEs. Grade 3 hyperglycemia was seen in one patient dosed at 300 mg/day. The ORR and DCR were 17.7% [95% confidence interval (CI) 3.8% to 43.4%] and 47.1% (95% CI 23.0% to 72.2%), respectively. Conclusion BPI-15086 is a safe and tolerable third-generation EGFR-TKI with a rationale for further clinical studies. BPI-15086 is safe and has partial effectiveness in patients with advanced T790M+ NSCLC after previous EGFR-TKI therapy. A different safety profile for BPI-15086 compared with other third-generation EGFR-TKIs. The modest efficacy in this study is still deemed important and should be added to the literature of third-generation TKIs.
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Wang Q, Bergholz JS, Ding L, Lin Z, Kabraji SK, Hughes ME, He X, Xie S, Jiang T, Wang W, Zoeller JJ, Kim HJ, Roberts TM, Konstantinopoulos PA, Matulonis UA, Dillon DA, Winer EP, Lin NU, Zhao JJ. STING agonism reprograms tumor-associated macrophages and overcomes resistance to PARP inhibition in BRCA1-deficient models of breast cancer. Nat Commun 2022; 13:3022. [PMID: 35641483 PMCID: PMC9156717 DOI: 10.1038/s41467-022-30568-1] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 05/06/2022] [Indexed: 12/12/2022] Open
Abstract
PARP inhibitors (PARPi) have drastically changed the treatment landscape of advanced ovarian tumors with BRCA mutations. However, the impact of this class of inhibitors in patients with advanced BRCA-mutant breast cancer is relatively modest. Using a syngeneic genetically-engineered mouse model of breast tumor driven by Brca1 deficiency, we show that tumor-associated macrophages (TAMs) blunt PARPi efficacy both in vivo and in vitro. Mechanistically, BRCA1-deficient breast tumor cells induce pro-tumor polarization of TAMs, which in turn suppress PARPi-elicited DNA damage in tumor cells, leading to reduced production of dsDNA fragments and synthetic lethality, hence impairing STING-dependent anti-tumor immunity. STING agonists reprogram M2-like pro-tumor macrophages into an M1-like anti-tumor state in a macrophage STING-dependent manner. Systemic administration of a STING agonist breaches multiple layers of tumor cell-mediated suppression of immune cells, and synergizes with PARPi to suppress tumor growth. The therapeutic benefits of this combination require host STING and are mediated by a type I IFN response and CD8+ T cells, but do not rely on tumor cell-intrinsic STING. Our data illustrate the importance of targeting innate immune suppression to facilitate PARPi-mediated engagement of anti-tumor immunity in breast cancer.
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Affiliation(s)
- Qiwei Wang
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Johann S Bergholz
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Liya Ding
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Ziying Lin
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Sheheryar K Kabraji
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Melissa E Hughes
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Xiadi He
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Shaozhen Xie
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Tao Jiang
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Weihua Wang
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Jason J Zoeller
- Department of Cell Biology and Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
| | - Hye-Jung Kim
- Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Thomas M Roberts
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
| | | | - Ursula A Matulonis
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Deborah A Dillon
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Eric P Winer
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Nancy U Lin
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Jean J Zhao
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA.
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Zhu JJ, Wang JT, Gong L, Ran ZX, Guo CY, Song L, Lyu YJ, Ding L. [A nested case-control study on the relationship between red blood cell folate and the prognosis of low-grade cervical intraepithelial neoplasia]. Zhonghua Yu Fang Yi Xue Za Zhi 2022; 56:453-458. [PMID: 35488542 DOI: 10.3760/cma.j.cn112150-20210906-00869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Objective: To evaluate the relationship between red blood cell folate (RBC folate) and the prognosis of low-grade cervical intraepithelial neoplasia (CIN 1). Methods: In the married women cohort established in 2014, 564 women with CIN 1 diagnosed by pathology were recruited. The demographic characteristics and factors of cervical intraepithelial neoplasia were collected. Meanwhile, the infection status of human papillomavirus (HPV) was detected by molecular diversion hybridization, and the level of RBC folate was measured by chemical photoimmunoassay. After 24 months of follow-up, pathological examination was performed again to observe the prognosis of participants. The women with reversal were taken as the control group,and those with continuous and progressive CIN 1 were taken as the case group respectively. The relationship between RBC folate and CIN 1 outcome was evaluated by logistic regression model. Results: 453 women completed the follow-up, aged (49.72±6.84) years old. CIN 1 was reversed in 342 women, continued in 58 cases and progressed in 53 cases. The RBC folate level M (Q1,Q3) were 399.01 (307.10, 538.97) ng/ml, 316.98 (184.74, 428.49) ng/ml and 247.14 (170.54, 348.97) ng/ml, respectively. With the decrease of RBC folate, the risk of continuous and progressive CIN 1 increased (all P<0.001), while the risk of reversal CIN 1 decreased gradually (P<0.001). Combined with high-risk human papillomavirus (HR-HPV) infection status, low level of RBC folate could increase the risk of CIN 1 progression regardless of HR-HPV infection (HR-HPV infection: OR=21.34, 95%CI: 3.98-114.54; HR-HPV uninfection: OR=11.15, 95%CI: 2.34-53.13). Conclusion: Low level of RBC folate could increase the risk of CIN 1 persistence and progression regardless of HR-HPV infection.
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Affiliation(s)
- J J Zhu
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - J T Wang
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - L Gong
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - Z X Ran
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - C Y Guo
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - L Song
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - Y J Lyu
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - L Ding
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
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Schumer A, Liu YGN, Leshin J, Ding L, Alahmadi Y, Hassan AU, Nasari H, Rotter S, Christodoulides DN, LiKamWa P, Khajavikhan M. Topological modes in a laser cavity through exceptional state transfer. Science 2022; 375:884-888. [PMID: 35201888 DOI: 10.1126/science.abl6571] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Shaping the light emission characteristics of laser systems is of great importance in various areas of science and technology. In a typical lasing arrangement, the transverse spatial profile of a laser mode tends to remain self-similar throughout the entire cavity. Going beyond this paradigm, we demonstrate here how to shape a spatially evolving mode such that it faithfully settles into a pair of bi-orthogonal states at the two opposing facets of a laser cavity. This was achieved by purposely designing a structure that allows the lasing mode to encircle a non-Hermitian exceptional point while deliberately avoiding non-adiabatic jumps. The resulting state transfer reflects the unique topology of the associated Riemann surfaces associated with this singularity. Our approach provides a route to developing versatile mode-selective active devices and sheds light on the interesting topological features of exceptional points.
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Affiliation(s)
- A Schumer
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, USA.,Institute for Theoretical Physics, Vienna University of Technology (TU Wien), A-1040 Vienna, Austria
| | - Y G N Liu
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, USA
| | - J Leshin
- CREOL, The College of Optics and Photonics, University of Central Florida, Orlando, FL 32816, USA
| | - L Ding
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, USA
| | - Y Alahmadi
- CREOL, The College of Optics and Photonics, University of Central Florida, Orlando, FL 32816, USA.,Center of Excellence for Telecomm Applications, Joint Centers of Excellence Program, King Abdul Aziz City for Science and Technology, Riyadh 11442, Saudi Arabia
| | - A U Hassan
- CREOL, The College of Optics and Photonics, University of Central Florida, Orlando, FL 32816, USA
| | - H Nasari
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, USA.,CREOL, The College of Optics and Photonics, University of Central Florida, Orlando, FL 32816, USA
| | - S Rotter
- Institute for Theoretical Physics, Vienna University of Technology (TU Wien), A-1040 Vienna, Austria
| | - D N Christodoulides
- CREOL, The College of Optics and Photonics, University of Central Florida, Orlando, FL 32816, USA
| | - P LiKamWa
- CREOL, The College of Optics and Photonics, University of Central Florida, Orlando, FL 32816, USA
| | - M Khajavikhan
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, USA
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31
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Vetterlein M, Kranzbühler B, Ding L, Kluth L, Kühnke L, König F, Soave A, Fisch M, Dahlem R, Marks P. Is the Urethral stricture score (U-score) a valid prognosticator in low complexity anterior urethral strictures? Making the case for further granular intraoperative stricture assessment. Eur Urol 2022. [DOI: 10.1016/s0302-2838(22)00781-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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32
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Ding L, Zhou R, Yuan Y, Yang H, Li J, Yu T, Liu C, Wang J, Li S, Gao H, Deng Z, Li N, Wang Z, Gong Z, Liu G, Xie J, Wang S, Rong Z, Deng D, Wang X, Han S, Wan W, Richter L, Huang L, Gou S, Liu Z, Yu H, Jia Y, Chen B, Dang Z, Zhang K, Li L, He X, Liu S, Di K. A 2-year locomotive exploration and scientific investigation of the lunar farside by the Yutu-2 rover. Sci Robot 2022; 7:eabj6660. [PMID: 35044796 DOI: 10.1126/scirobotics.abj6660] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
The lunar nearside has been investigated by many uncrewed and crewed missions, but the farside of the Moon remains poorly known. Lunar farside exploration is challenging because maneuvering rovers with efficient locomotion in harsh extraterrestrial environment is necessary to explore geological characteristics of scientific interest. Chang'E-4 mission successfully targeted the Moon's farside and deployed a teleoperated rover (Yutu-2) to explore inside the Von Kármán crater, conveying rich information regarding regolith, craters, and rocks. Here, we report mobile exploration on the lunar farside with Yutu-2 over the initial 2 years. During its journey, Yutu-2 has experienced varying degrees of mild slip and skid, indicating that the terrain is relatively flat at large scales but scattered with local gentle slopes. Cloddy soil sticking on its wheels implies a greater cohesion of the lunar soil than encountered at other lunar landing sites. Further identification results indicate that the regolith resembles dry sand and sandy loam on Earth in bearing properties, demonstrating greater bearing strength than that identified during the Apollo missions. In sharp contrast to the sparsity of rocks along the traverse route, small fresh craters with unilateral moldable ejecta are abundant, and some of them contain high-reflectance materials at the bottom, suggestive of secondary impact events. These findings hint at notable differences in the surface geology between the lunar farside and nearside. Experience gained with Yutu-2 improves the understanding of the farside of the Moon, which, in return, may lead to locomotion with improved efficiency and larger range.
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Affiliation(s)
- L Ding
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150080, China
| | - R Zhou
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150080, China
| | - Y Yuan
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150080, China
| | - H Yang
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150080, China
| | - J Li
- Beijing Aerospace Control Center, Beijing 100094, China
| | - T Yu
- Beijing Aerospace Control Center, Beijing 100094, China
| | - C Liu
- Beijing Aerospace Control Center, Beijing 100094, China.,Key Laboratory of Science and Technology on Aerospace Flight Dynamics, Beijing 100094, China
| | - J Wang
- Beijing Aerospace Control Center, Beijing 100094, China
| | - S Li
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150080, China
| | - H Gao
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150080, China
| | - Z Deng
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150080, China
| | - N Li
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150080, China
| | - Z Wang
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150080, China
| | - Z Gong
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150080, China
| | - G Liu
- Department of Aerospace Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada
| | - J Xie
- Beijing Aerospace Control Center, Beijing 100094, China
| | - S Wang
- Beijing Aerospace Control Center, Beijing 100094, China
| | - Z Rong
- Beijing Aerospace Control Center, Beijing 100094, China
| | - D Deng
- Beijing Aerospace Control Center, Beijing 100094, China
| | - X Wang
- Beijing Aerospace Control Center, Beijing 100094, China.,Key Laboratory of Science and Technology on Aerospace Flight Dynamics, Beijing 100094, China
| | - S Han
- Beijing Aerospace Control Center, Beijing 100094, China
| | - W Wan
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
| | - L Richter
- Large Space Structures GmbH, Hauptstrasse 1, D-85386 Eching, Germany
| | - L Huang
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150080, China
| | - S Gou
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
| | - Z Liu
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150080, China
| | - H Yu
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150080, China
| | - Y Jia
- China Academy of Space Technology, Beijing 100094, China
| | - B Chen
- China Academy of Space Technology, Beijing 100094, China
| | - Z Dang
- China Academy of Space Technology, Beijing 100094, China
| | - K Zhang
- Beijing Aerospace Control Center, Beijing 100094, China
| | - L Li
- Beijing Aerospace Control Center, Beijing 100094, China
| | - X He
- Beijing Aerospace Control Center, Beijing 100094, China
| | - S Liu
- Beijing Aerospace Control Center, Beijing 100094, China
| | - K Di
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
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Guo S, Zhao X, Jiang S, Ding L, Peng H. Image enhancement to leverage the 3D morphological reconstruction of single-cell neurons. Bioinformatics 2022; 38:503-512. [PMID: 34515755 DOI: 10.1093/bioinformatics/btab638] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 08/05/2021] [Accepted: 09/09/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION To digitally reconstruct the 3D neuron morphologies has long been a major bottleneck in neuroscience. One of the obstacles to automate the procedure is the low signal-background contrast (SBC) and the large dynamic range of signal and background both within and across images. RESULTS We developed a pipeline to enhance the neurite signal and to suppress the background, with the goal of high SBC and better within- and between-image homogeneity. The performance of the image enhancement was quantitatively verified according to the different figures of merit benchmarking the image quality. In addition, the method could improve the neuron reconstruction in approximately 1/3 of the cases, with very few cases of degrading the reconstruction. This significantly outperformed three other approaches of image enhancement. Moreover, the compression rate was increased five times by average comparing the enhanced to the raw image. All results demonstrated the potential of the proposed method in leveraging the neuroscience by providing better 3D morphological reconstruction and lower cost of data storage and transfer. AVAILABILITY AND IMPLEMENTATION The study is conducted based on the Vaa3D platform and python 3.7.9. The Vaa3D platform is available on the GitHub (https://github.com/Vaa3D). The source code of the proposed image enhancement as a Vaa3D plugin, the source code to benchmark the image quality and the example image blocks are available under the repository of vaa3d_tools/hackathon/SGuo/imPreProcess. The original fMost images of mouse brains can be found at the BICCN's Brain Image Library (BIL) (https://www.brainimagelibrary.org). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Shuxia Guo
- Institute for Brain and Intelligence, Southeast University, 210096 Nanjing, Jiangsu Province, China
| | - Xuan Zhao
- Institute for Brain and Intelligence, Southeast University, 210096 Nanjing, Jiangsu Province, China
| | - Shengdian Jiang
- Institute for Brain and Intelligence, Southeast University, 210096 Nanjing, Jiangsu Province, China
| | - Liya Ding
- Institute for Brain and Intelligence, Southeast University, 210096 Nanjing, Jiangsu Province, China
| | - Hanchuan Peng
- Institute for Brain and Intelligence, Southeast University, 210096 Nanjing, Jiangsu Province, China
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Li X, Lang X, Peng S, Ding L, Li S, Li Y, Yin L, Liu X. Calf Circumference and All-Cause Mortality: A Systematic Review and Meta-Analysis Based on Trend Estimation Approaches. J Nutr Health Aging 2022; 26:826-838. [PMID: 36156674 DOI: 10.1007/s12603-022-1838-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVES To perform a systematic review and meta-analysis and quantify the associations of total mortality with calf circumference (CC) in adults 18 years and older via combining various analyses based on empirical dichotomic CC, continuous CC, and dose-response CC. METHODS We conducted a systematic search of relevant studies in PubMed, EMBASE, Cochrane Library, and Web of Science published through April 12, 2022. This systematic review includes longitudinal observational studies reporting the relationships of total mortality with CC. We calculated the pooled relative risk (RR) and 95% confidence interval (CI) of total mortality with CC per 1 cm for each study and combined the values using standard meta-analysis approaches. Newcastle-Ottawa scale (NOS), Grading of Recommendations, Assessment, Development and Evaluations approach (GRADE), and the Instrument for assessing the Credibility of Effect Modification Analyses (ICEMAN) were assessed for meta-analyses. RESULTS Our analysis included a total of 37 cohort studies involving 62,736 participants, across which moderate heterogeneity was observed (I2=75.7%, P<0.001), but no publication bias was found. Study quality scores ranged from 6 to 9 (mean 7.7), with only three studies awarded a score of 6 (fair quality). We observed an inverse trend between total death risk and CC per 1 cm increase (RR, 0.95, 95% CI, 0.94-0.96; P<0.001; GRADE quality=high). Only a very slight difference was found among residents of nursing homes (6.9% mortality risk reduction per one cm CC increase), community-dwellers (5.4%), and those living in hospitals (4.8%), respectively (P for meta-regression=0.617). Low credible subgroup difference was found based on the ICEMAN tool. CONCLUSIONS Calf circumference is a valid anthropometric measure for mortality risk prediction in a community, nursing home, or hospital.
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Affiliation(s)
- X Li
- Lu Yin, Medical Research and Biometrics Center, National Center for Cardiovascular Diseases, Beijing 102300, China. E-mail: ; Xiaomei Liu, Department of Emergency, Zhongshan Hospital of Xiamen University, Xiamen, China. Tel:
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Donovan-Maiye RM, Brown JM, Chan CK, Ding L, Yan C, Gaudreault N, Theriot JA, Maleckar MM, Knijnenburg TA, Johnson GR. A deep generative model of 3D single-cell organization. PLoS Comput Biol 2022; 18:e1009155. [PMID: 35041651 PMCID: PMC8797242 DOI: 10.1371/journal.pcbi.1009155] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 01/28/2022] [Accepted: 11/29/2021] [Indexed: 11/18/2022] Open
Abstract
We introduce a framework for end-to-end integrative modeling of 3D single-cell multi-channel fluorescent image data of diverse subcellular structures. We employ stacked conditional β-variational autoencoders to first learn a latent representation of cell morphology, and then learn a latent representation of subcellular structure localization which is conditioned on the learned cell morphology. Our model is flexible and can be trained on images of arbitrary subcellular structures and at varying degrees of sparsity and reconstruction fidelity. We train our full model on 3D cell image data and explore design trade-offs in the 2D setting. Once trained, our model can be used to predict plausible locations of structures in cells where these structures were not imaged. The trained model can also be used to quantify the variation in the location of subcellular structures by generating plausible instantiations of each structure in arbitrary cell geometries. We apply our trained model to a small drug perturbation screen to demonstrate its applicability to new data. We show how the latent representations of drugged cells differ from unperturbed cells as expected by on-target effects of the drugs.
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Affiliation(s)
| | - Jackson M. Brown
- Allen Institute for Cell Science, Seattle, Washington, United States of America
| | - Caleb K. Chan
- Allen Institute for Cell Science, Seattle, Washington, United States of America
| | - Liya Ding
- Allen Institute for Cell Science, Seattle, Washington, United States of America
| | - Calysta Yan
- Allen Institute for Cell Science, Seattle, Washington, United States of America
| | - Nathalie Gaudreault
- Allen Institute for Cell Science, Seattle, Washington, United States of America
| | - Julie A. Theriot
- Allen Institute for Cell Science, Seattle, Washington, United States of America
- Department of Biology and Howard Hughes Medical Institute, University of Washington, Seattle, Washington, United States of America
| | - Mary M. Maleckar
- Allen Institute for Cell Science, Seattle, Washington, United States of America
| | - Theo A. Knijnenburg
- Allen Institute for Cell Science, Seattle, Washington, United States of America
- * E-mail:
| | - Gregory R. Johnson
- Allen Institute for Cell Science, Seattle, Washington, United States of America
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Wu CH, Pei RX, Yan JX, Ding L, Lyu YJ, Song L, Wang J, Meng D, Liu H, Qi Z, Hao M, Wang JT. [The effect of red blood cell folate on the prognosis of high-risk human papillomavirus infection: a community-based cohort study]. Zhonghua Liu Xing Bing Xue Za Zhi 2021; 42:2174-2178. [PMID: 34954983 DOI: 10.3760/cma.j.cn112338-20210408-00291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Objective: To investigate the effect of red blood cell folate on the prognosis of high-risk human papillomavirus (HR-HPV) infection. Methods: A total of 564 participants with low-grade cervical intraepithelial neoplasias (CINⅠ) were selected from the community-based married women cohort established in 2014. The general baseline information and factors related to HPV infection were collected. Meanwhile, HPV genotyping and levels of folate were measured. The subjects were divided into different levels of exposure group according to the folate levels and followed up for 24 months to observe the changes of HR-HPV infection status. There were four changes, including persistent infection, infection turned negative, from negative to positive and constant negative by comparing HR-HPV infection status at baseline and follow-up to 24 months. Results: 483 participators completed 24 months of follow-up observation, with a follow-up rate of 85.64% (483/564). The rates of persistent infection, infection turned negative, from negative to positive, and the constant negative were 52.45% (75/143), 47.55% (68/143), 19.71% (67/340), 80.29% (273/340), respectively. Our results demonstrated that the risk of persistent infection (aRR=2.50, 95%CI: 1.55-4.02) and from negative to positive (aRR=4.55, 95%CI: 2.52-8.23) in the low level of folate were significantly higher than that in the high level of folate, especially the risk of homotype persistent infection (aRR=2.72, 95%CI: 1.51-4.90). The risk of persistent infection (trend χ2=20.62, P<0.001), from negative to positive (trend χ2=31.76, P<0.001), persistent homotypic infection (trend χ2=20.09, P<0.001) increased with the decrease of red blood cell folate level. On the contrary, no similar results were found in persistent heterotypic infection. Conclusions: A low level of red blood cell folate could increase the risk of HR-HPV persistent infection and from negative to positive. In women with HR-HPV infection, the risk of persistent homotypic infection is higher.
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Affiliation(s)
- C H Wu
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - R X Pei
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - J X Yan
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - L Ding
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - Y J Lyu
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - L Song
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - J Wang
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - D Meng
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - H Liu
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - Z Qi
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - M Hao
- Department of Obstetrics and Gynecology, Second Hospital of Shanxi Medical University, Taiyuan 030001, China
| | - J T Wang
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
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Pei RX, Wu CH, Yan JX, Ding L, Song L, Lyu YJ, Wang J, Liu H, Meng D, Qi Z, Hao M, Wang JT. [Effects of polycyclic aromatic hydrocarbons exposure on prognosis of high risk human papillomavirus infection: a prospective cohort study]. Zhonghua Liu Xing Bing Xue Za Zhi 2021; 42:2060-2065. [PMID: 34818855 DOI: 10.3760/cma.j.cn112338-20210406-00278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Objective: To investigate the effects of polycyclic aromatic hydrocarbons (PAHs) exposure on the prognosis of high risk human papillomavirus (HR-HPV) infection. Methods: In this prospective study, 564 patients with low-grade cervical intraepithelial neoplasia confirmed by pathology were selected from the natural cohort population established by our research group in Shanxi province in 2014. Based on the baseline data of demographic characteristics and factors related to HPV infection, the concentrations of 1-hydroxypyrene in urine samples of the patients were determined by high performance liquid chromatography to define the exposure level of PAHs. At baseline survey and follow-up after 24 months, flow-through hybridization was used to detect HPV infection types, and to evaluate the prognosis of HR-HPV (persistent infection, negative conversion, positive conversion and persistent negative status). Results: Of the 564 subjects, 483 completed the follow-up, with a follow-up rate of 85.6% (483/564). Among them, the persistent infection rate was 52.4% (75/143), the persistent homotype infection rate was 35.7% (51/143), the negative conversion rate was 47.6% (68/143), the positive conversion rate was 19.7% (67/340), and the persistent negative rate was 80.3% (273/340). The follow-up results showed that the persistent infection rate (aRR=3.22, 95%CI: 1.85-5.62) and positive conversion rate (aRR=2.84, 95%CI: 1.64-4.94) of HR-HPV in high PAHs exposure group were higher than those in low PAHs exposure group, while the persistent negative rate (aRR=0.55, 95% CI: 0.43-0.70) of HR-HPV in high PAHs exposure group were lower than those in low PAHs exposure group. Based on restrictive cubic spline analysis, the results showed that the effects of PAHs exposure on persistent HR-HPV infection and persistent homotype infection showed an ascending linear dose-response relationship, while on HR-HPV positive conversion and persistent negative status showed an ascending and declining nonlinear dose-response relationship respectively (P<0.01). Conclusions: High PAHs exposure could promote persistent HR-HPV infection and persistent homotypic infection. Reducing PAHs exposure might conducive to HR-HPV continuous negative maintenance. Active prevention and control of PAHs exposure is of great significance to prevent HR-HPV infection and persistent infection.
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Affiliation(s)
- R X Pei
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - C H Wu
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - J X Yan
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - L Ding
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - L Song
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - Y J Lyu
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - J Wang
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - H Liu
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - D Meng
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - Z Qi
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - M Hao
- Department of Obstetrics and Gynecology, Second Hospital of Shanxi Medical University, Taiyuan 030001, China
| | - J T Wang
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
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Chen K, Chauhan P, Babbra R, Feng W, Pejovic N, Harris P, Dienstbach K, Atkocius A, Maguire L, Qaium F, Huang Y, Szymanski J, Baumann B, Ding L, Cao D, Reimers M, Kim E, Smith Z, Arora V, Chaudhuri A. Urine- and Plasma-Based Detection of Minimal Residual Disease in Localized Bladder Cancer Patients. Int J Radiat Oncol Biol Phys 2021. [DOI: 10.1016/j.ijrobp.2021.07.854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Ding L, Cai S, Wang J, Smyth H. 481: PEGylated tobramycin significantly improves anti-biofilm activity in vitro and in vivo. J Cyst Fibros 2021. [DOI: 10.1016/s1569-1993(21)01905-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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40
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Chati P, Storrs E, Usmani A, Krasnick B, Hollander T, Qaium F, Hephzibah A, Sloan I, Badiyan S, Lang G, Cosgrove N, Kushnir V, Mullady D, Early D, Hawkins W, Ding L, Fields R, Das K, Chaudhuri A. Derivation of Distinct Prognostic Tumor Cell States in PDAC via Single-Cell RNA Sequencing. Int J Radiat Oncol Biol Phys 2021. [DOI: 10.1016/j.ijrobp.2021.07.347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Ding L, Weng S, Tang M, Zhang S. Anatomical dilation for the coronary sinus ostium in patients with pulmonary arterial hypertension and its impact to trigger the atrioventricular reentrant tachycardia: a case control study. Eur Heart J 2021. [DOI: 10.1093/eurheartj/ehab724.0598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Background
The incidence of atrioventricular reentrant tachycardia (AVNRT) in pulmonary arterial hypertension (PAH) patients is higher than the general population [1–3]. AVNRT had been reported with larger coronary sinus (CS) ostium in general population, while the mechanism and correlation between AVNRT and the CS ostium in PAH patients are poorly understood.
Purpose
We aim to investigate the impact of the CS ostium on AVNRT and find out its risk factors in PAH patients.
Methods
Of 102 pulmonary arterial hypertension (PAH) patients with catheter ablation of SVT, 10 patients confirmed AVNRT who underwent computed tomographic angiography (CTA) were enrolled as study group. The control group (PAH patients without SVT, n=20) were matched in a ratio of 2:1 based on gender and BMI. We measured maxium diameter of CS ostium in axial and LAO plane by CTA. All baseline characteristics and imaging materials were collected.
Results
PAH patients with AVNRT were older (45.9±14.8 vs. 32.1±7.6 years, P=0.025) and more likely to have larger CS ostium in LAO plane (18.6±3.3 vs. 14.8±4.0 mm, P=0.011) than those without AVNRT. The maximal diameter of CS ostium in LAO plane was an independent predictor for AVNRT in PAH patients (Odds ratio, 1.389; 95% confidence interval, 1.003–1.923; P=0.048). The cut-off value of CS ostium in LAO plane was 14.1mm (Area under curve = 0.79, P=0.012), and the sensitivity and specificity were 90% and 55%, respectively.
Conclusions
The larger CS ostium in LAO plane correlated with the higher prevalence of AVNRT in PAH patients with age. Patients with CS ostium larger than 14.1mm in LAO plane are more likely to develop AVNRT.
Funding Acknowledgement
Type of funding sources: None. Measurements and diagnosis value
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Affiliation(s)
- L Ding
- Fuwai Hospital, CAMS and PUMC, Beijing, China
| | - S Weng
- Fuwai Hospital, CAMS and PUMC, Beijing, China
| | - M Tang
- Fuwai Hospital, CAMS and PUMC, Beijing, China
| | - S Zhang
- Fuwai Hospital, CAMS and PUMC, Beijing, China
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Zhang HD, Ding L, Weng SX, Zhou B, Ding XT, Hu LX, Qi YJ, Yu FY, Feng TJ, Zhang JT, Fang PF, Zhang S, Tang M. Characteristics and long-term ablation outcomes of supraventricular arrhythmias in hypertrophic cardiomyopathy: a 10-year, single-center experience. Eur Heart J 2021. [DOI: 10.1093/eurheartj/ehab724.0291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Background
A variety of supraventricular arrhythmias (SVAs) may occur in patients with hypertrophic cardiomyopathy (HCM). The characteristics and long-term ablation outcomes of different types of SVAs in HCM have not been comprehensively investigated.
Methods
We retrospectively enrolled 101 consecutive symptomatic HCM patients with suspected arrhythmia from May 2010 to October 2020. The clinical features and ablation outcomes of patients with SVAs were further analyzed.
Results
Seventy-eight patients had SVAs, consisting of 50 (64.1%) atrial fibrillation (AF), 16 (20.5%) atrial flutter (AFL), 15 (19.2%) atrioventricular reentrant tachycardia (AVRT), 11 (14.1%) atrial arrhythmia (AT), and 3 (3.8%) atrioventricular nodal reentrant tachycardia (AVNRT). Thirty-four patients underwent catheter ablation including 14 for AF, 9 for AVRT, 6 for AFL, 3 for AVNRT, 1 for both AF and AFL, and 1 for both AF and AVRT. They were followed up for a median (interquartile range) of 58.5 (82.9) months. There were no recurrences for patients with non-AF SVAs. For patients with AF, the 1- and 7-year AF-free survival were 87.5% and 49.5%, respectively. A ROC analysis revealed that a greater left ventricular end-diastolic dimension (LVEDD) was associated with higher recurrence of AF with an optimum cutoff value of 47mm (c-statistic = 0.91, p=0.011, sensitivity = 1.00, specificity = 0.82). In Kaplan-Meier analysis, patients with LVEDD ≥47mm had worse AF-free survival (log-rank p=0.014).
Conclusions
AF is the most common SVA in HCM, with AFL, AVRT, AT, and AVNRT ranking the second to the last according to the prevalence. The long-term catheter ablation outcome for non-AF SVAs in HCM is satisfying. A greater LVEDD predicts AF recurrence after catheter ablation in patients with HCM.
Funding Acknowledgement
Type of funding sources: Foundation. Main funding source(s): National Natural Science Foundation of China Figure 1Figure 2
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Affiliation(s)
- H D Zhang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular D, Beijing, China
| | - L Ding
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular D, Beijing, China
| | - S X Weng
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular D, Beijing, China
| | - B Zhou
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular D, Beijing, China
| | - X T Ding
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular D, Beijing, China
| | - L X Hu
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular D, Beijing, China
| | - Y J Qi
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular D, Beijing, China
| | - F Y Yu
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular D, Beijing, China
| | - T J Feng
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular D, Beijing, China
| | - J T Zhang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular D, Beijing, China
| | - P F Fang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular D, Beijing, China
| | - S Zhang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular D, Beijing, China
| | - M Tang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular D, Beijing, China
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43
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Muñoz-Castañeda R, Zingg B, Matho KS, Chen X, Wang Q, Foster NN, Li A, Narasimhan A, Hirokawa KE, Huo B, Bannerjee S, Korobkova L, Park CS, Park YG, Bienkowski MS, Chon U, Wheeler DW, Li X, Wang Y, Naeemi M, Xie P, Liu L, Kelly K, An X, Attili SM, Bowman I, Bludova A, Cetin A, Ding L, Drewes R, D'Orazi F, Elowsky C, Fischer S, Galbavy W, Gao L, Gillis J, Groblewski PA, Gou L, Hahn JD, Hatfield JT, Hintiryan H, Huang JJ, Kondo H, Kuang X, Lesnar P, Li X, Li Y, Lin M, Lo D, Mizrachi J, Mok S, Nicovich PR, Palaniswamy R, Palmer J, Qi X, Shen E, Sun YC, Tao HW, Wakemen W, Wang Y, Yao S, Yuan J, Zhan H, Zhu M, Ng L, Zhang LI, Lim BK, Hawrylycz M, Gong H, Gee JC, Kim Y, Chung K, Yang XW, Peng H, Luo Q, Mitra PP, Zador AM, Zeng H, Ascoli GA, Josh Huang Z, Osten P, Harris JA, Dong HW. Cellular anatomy of the mouse primary motor cortex. Nature 2021; 598:159-166. [PMID: 34616071 PMCID: PMC8494646 DOI: 10.1038/s41586-021-03970-w] [Citation(s) in RCA: 82] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 08/27/2021] [Indexed: 12/24/2022]
Abstract
An essential step toward understanding brain function is to establish a structural framework with cellular resolution on which multi-scale datasets spanning molecules, cells, circuits and systems can be integrated and interpreted1. Here, as part of the collaborative Brain Initiative Cell Census Network (BICCN), we derive a comprehensive cell type-based anatomical description of one exemplar brain structure, the mouse primary motor cortex, upper limb area (MOp-ul). Using genetic and viral labelling, barcoded anatomy resolved by sequencing, single-neuron reconstruction, whole-brain imaging and cloud-based neuroinformatics tools, we delineated the MOp-ul in 3D and refined its sublaminar organization. We defined around two dozen projection neuron types in the MOp-ul and derived an input-output wiring diagram, which will facilitate future analyses of motor control circuitry across molecular, cellular and system levels. This work provides a roadmap towards a comprehensive cellular-resolution description of mammalian brain architecture.
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Affiliation(s)
| | - Brian Zingg
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | | | - Xiaoyin Chen
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Quanxin Wang
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Nicholas N Foster
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
| | | | - Karla E Hirokawa
- Allen Institute for Brain Science, Seattle, WA, USA
- Cajal Neuroscience, Seattle, WA, USA
| | - Bingxing Huo
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Laura Korobkova
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Chris Sin Park
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Young-Gyun Park
- Institute for Medical Engineering and Science, Department of Chemical Engineering, Picower Institute for Learning and Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | - Michael S Bienkowski
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
- Department of Physiology and Neuroscience, Zilkha Neurogenetic Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Uree Chon
- Department of Neural and Behavioral Sciences, College of Medicine, Penn State University, Hershey, PA, USA
| | - Diek W Wheeler
- Center for Neural Informatics, Structures and Plasticity, Bioengineering Department and Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Xiangning Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
| | - Yun Wang
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Peng Xie
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Lijuan Liu
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Kathleen Kelly
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Xu An
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA
| | - Sarojini M Attili
- Center for Neural Informatics, Structures and Plasticity, Bioengineering Department and Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Ian Bowman
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | | | - Ali Cetin
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Liya Ding
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Rhonda Drewes
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Corey Elowsky
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | | | - Lei Gao
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Jesse Gillis
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Lin Gou
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Joel D Hahn
- Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA
| | - Joshua T Hatfield
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA
| | - Houri Hintiryan
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Junxiang Jason Huang
- Center for Neural Circuits and Sensory Processing Disorders, Zilkha Neurogenetics Institute (ZNI), Department of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Hideki Kondo
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Xiuli Kuang
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | | | - Xu Li
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Yaoyao Li
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | - Mengkuan Lin
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Darrick Lo
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | | | | | - Philip R Nicovich
- Allen Institute for Brain Science, Seattle, WA, USA
- Cajal Neuroscience, Seattle, WA, USA
| | | | - Jason Palmer
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Xiaoli Qi
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Elise Shen
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Yu-Chi Sun
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Huizhong W Tao
- Center for Neural Circuits and Sensory Processing Disorders, Zilkha Neurogenetics Institute (ZNI), Department of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | - Yimin Wang
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Shenqin Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Jing Yuan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
| | - Huiqing Zhan
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Muye Zhu
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Li I Zhang
- Center for Neural Circuits and Sensory Processing Disorders, Zilkha Neurogenetics Institute (ZNI), Department of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Byung Kook Lim
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
- Division of Biological Science, Neurobiology section, University of California San Diego, San Diego, CA, USA
| | | | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
| | - James C Gee
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Yongsoo Kim
- Department of Neural and Behavioral Sciences, College of Medicine, Penn State University, Hershey, PA, USA
| | - Kwanghun Chung
- Institute for Medical Engineering and Science, Department of Chemical Engineering, Picower Institute for Learning and Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | - X William Yang
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Hanchuan Peng
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
| | - Partha P Mitra
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Giorgio A Ascoli
- Center for Neural Informatics, Structures and Plasticity, Bioengineering Department and Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA.
| | - Z Josh Huang
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA.
| | - Pavel Osten
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
| | - Julie A Harris
- Allen Institute for Brain Science, Seattle, WA, USA.
- Cajal Neuroscience, Seattle, WA, USA.
| | - Hong-Wei Dong
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
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Peng H, Xie P, Liu L, Kuang X, Wang Y, Qu L, Gong H, Jiang S, Li A, Ruan Z, Ding L, Yao Z, Chen C, Chen M, Daigle TL, Dalley R, Ding Z, Duan Y, Feiner A, He P, Hill C, Hirokawa KE, Hong G, Huang L, Kebede S, Kuo HC, Larsen R, Lesnar P, Li L, Li Q, Li X, Li Y, Li Y, Liu A, Lu D, Mok S, Ng L, Nguyen TN, Ouyang Q, Pan J, Shen E, Song Y, Sunkin SM, Tasic B, Veldman MB, Wakeman W, Wan W, Wang P, Wang Q, Wang T, Wang Y, Xiong F, Xiong W, Xu W, Ye M, Yin L, Yu Y, Yuan J, Yuan J, Yun Z, Zeng S, Zhang S, Zhao S, Zhao Z, Zhou Z, Huang ZJ, Esposito L, Hawrylycz MJ, Sorensen SA, Yang XW, Zheng Y, Gu Z, Xie W, Koch C, Luo Q, Harris JA, Wang Y, Zeng H. Morphological diversity of single neurons in molecularly defined cell types. Nature 2021; 598:174-181. [PMID: 34616072 PMCID: PMC8494643 DOI: 10.1038/s41586-021-03941-1] [Citation(s) in RCA: 136] [Impact Index Per Article: 45.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Accepted: 08/24/2021] [Indexed: 12/23/2022]
Abstract
Dendritic and axonal morphology reflects the input and output of neurons and is a defining feature of neuronal types1,2, yet our knowledge of its diversity remains limited. Here, to systematically examine complete single-neuron morphologies on a brain-wide scale, we established a pipeline encompassing sparse labelling, whole-brain imaging, reconstruction, registration and analysis. We fully reconstructed 1,741 neurons from cortex, claustrum, thalamus, striatum and other brain regions in mice. We identified 11 major projection neuron types with distinct morphological features and corresponding transcriptomic identities. Extensive projectional diversity was found within each of these major types, on the basis of which some types were clustered into more refined subtypes. This diversity follows a set of generalizable principles that govern long-range axonal projections at different levels, including molecular correspondence, divergent or convergent projection, axon termination pattern, regional specificity, topography, and individual cell variability. Although clear concordance with transcriptomic profiles is evident at the level of major projection type, fine-grained morphological diversity often does not readily correlate with transcriptomic subtypes derived from unsupervised clustering, highlighting the need for single-cell cross-modality studies. Overall, our study demonstrates the crucial need for quantitative description of complete single-cell anatomy in cell-type classification, as single-cell morphological diversity reveals a plethora of ways in which different cell types and their individual members may contribute to the configuration and function of their respective circuits.
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Affiliation(s)
- Hanchuan Peng
- Allen Institute for Brain Science, Seattle, WA, USA.
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China.
| | - Peng Xie
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Lijuan Liu
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
- Ministry of Education Key Laboratory of Developmental Genes and Human Disease, School of Life Science and Technology, Southeast University, Nanjing, China
| | - Xiuli Kuang
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | - Yimin Wang
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Lei Qu
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
- Key Laboratory of Intelligent Computation and Signal Processing, Ministry of Education, Anhui University, Hefei, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Shengdian Jiang
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Zongcai Ruan
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Liya Ding
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Chao Chen
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | - Mengya Chen
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | | | | | - Zhangcan Ding
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Yanjun Duan
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Aaron Feiner
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Ping He
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Chris Hill
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Karla E Hirokawa
- Allen Institute for Brain Science, Seattle, WA, USA
- Cajal Neuroscience, Seattle, WA, USA
| | - Guodong Hong
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
- Ministry of Education Key Laboratory of Developmental Genes and Human Disease, School of Life Science and Technology, Southeast University, Nanjing, China
| | - Lei Huang
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Sara Kebede
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Phil Lesnar
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Longfei Li
- Key Laboratory of Intelligent Computation and Signal Processing, Ministry of Education, Anhui University, Hefei, China
| | - Qi Li
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Xiangning Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Yaoyao Li
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | - Yuanyuan Li
- Key Laboratory of Intelligent Computation and Signal Processing, Ministry of Education, Anhui University, Hefei, China
| | - An Liu
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
- Ministry of Education Key Laboratory of Developmental Genes and Human Disease, School of Life Science and Technology, Southeast University, Nanjing, China
| | | | | | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Thuc Nghi Nguyen
- Allen Institute for Brain Science, Seattle, WA, USA
- Cajal Neuroscience, Seattle, WA, USA
| | - Qiang Ouyang
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Jintao Pan
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Elise Shen
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Yuanyuan Song
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | | | | | - Matthew B Veldman
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Wan Wan
- Key Laboratory of Intelligent Computation and Signal Processing, Ministry of Education, Anhui University, Hefei, China
| | - Peng Wang
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Quanxin Wang
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Tao Wang
- Key Laboratory of Intelligent Computation and Signal Processing, Ministry of Education, Anhui University, Hefei, China
| | - Yaping Wang
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Feng Xiong
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Wei Xiong
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | - Wenjie Xu
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Min Ye
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | - Lulu Yin
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Yang Yu
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Jia Yuan
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
- Ministry of Education Key Laboratory of Developmental Genes and Human Disease, School of Life Science and Technology, Southeast University, Nanjing, China
| | - Jing Yuan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Zhixi Yun
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Shaoqun Zeng
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
| | - Shichen Zhang
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Sujun Zhao
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Zijun Zhao
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Zhi Zhou
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Z Josh Huang
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA
| | | | | | | | - X William Yang
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Zhongze Gu
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Wei Xie
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
- Ministry of Education Key Laboratory of Developmental Genes and Human Disease, School of Life Science and Technology, Southeast University, Nanjing, China
| | | | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- School of Biomedical Engineering, Hainan University, Haikou, China
| | - Julie A Harris
- Allen Institute for Brain Science, Seattle, WA, USA
- Cajal Neuroscience, Seattle, WA, USA
| | - Yun Wang
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA.
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Callaway EM, Dong HW, Ecker JR, Hawrylycz MJ, Huang ZJ, Lein ES, Ngai J, Osten P, Ren B, Tolias AS, White O, Zeng H, Zhuang X, Ascoli GA, Behrens MM, Chun J, Feng G, Gee JC, Ghosh SS, Halchenko YO, Hertzano R, Lim BK, Martone ME, Ng L, Pachter L, Ropelewski AJ, Tickle TL, Yang XW, Zhang K, Bakken TE, Berens P, Daigle TL, Harris JA, Jorstad NL, Kalmbach BE, Kobak D, Li YE, Liu H, Matho KS, Mukamel EA, Naeemi M, Scala F, Tan P, Ting JT, Xie F, Zhang M, Zhang Z, Zhou J, Zingg B, Armand E, Yao Z, Bertagnolli D, Casper T, Crichton K, Dee N, Diep D, Ding SL, Dong W, Dougherty EL, Fong O, Goldman M, Goldy J, Hodge RD, Hu L, Keene CD, Krienen FM, Kroll M, Lake BB, Lathia K, Linnarsson S, Liu CS, Macosko EZ, McCarroll SA, McMillen D, Nadaf NM, Nguyen TN, Palmer CR, Pham T, Plongthongkum N, Reed NM, Regev A, Rimorin C, Romanow WJ, Savoia S, Siletti K, Smith K, Sulc J, Tasic B, Tieu M, Torkelson A, Tung H, van Velthoven CTJ, Vanderburg CR, Yanny AM, Fang R, Hou X, Lucero JD, Osteen JK, Pinto-Duarte A, Poirion O, Preissl S, Wang X, Aldridge AI, Bartlett A, Boggeman L, O’Connor C, Castanon RG, Chen H, Fitzpatrick C, Luo C, Nery JR, Nunn M, Rivkin AC, Tian W, Dominguez B, Ito-Cole T, Jacobs M, Jin X, Lee CT, Lee KF, Miyazaki PA, Pang Y, Rashid M, Smith JB, Vu M, Williams E, Biancalani T, Booeshaghi AS, Crow M, Dudoit S, Fischer S, Gillis J, Hu Q, Kharchenko PV, Niu SY, Ntranos V, Purdom E, Risso D, de Bézieux HR, Somasundaram S, Street K, Svensson V, Vaishnav ED, Van den Berge K, Welch JD, An X, Bateup HS, Bowman I, Chance RK, Foster NN, Galbavy W, Gong H, Gou L, Hatfield JT, Hintiryan H, Hirokawa KE, Kim G, Kramer DJ, Li A, Li X, Luo Q, Muñoz-Castañeda R, Stafford DA, Feng Z, Jia X, Jiang S, Jiang T, Kuang X, Larsen R, Lesnar P, Li Y, Li Y, Liu L, Peng H, Qu L, Ren M, Ruan Z, Shen E, Song Y, Wakeman W, Wang P, Wang Y, Wang Y, Yin L, Yuan J, Zhao S, Zhao X, Narasimhan A, Palaniswamy R, Banerjee S, Ding L, Huilgol D, Huo B, Kuo HC, Laturnus S, Li X, Mitra PP, Mizrachi J, Wang Q, Xie P, Xiong F, Yu Y, Eichhorn SW, Berg J, Bernabucci M, Bernaerts Y, Cadwell CR, Castro JR, Dalley R, Hartmanis L, Horwitz GD, Jiang X, Ko AL, Miranda E, Mulherkar S, Nicovich PR, Owen SF, Sandberg R, Sorensen SA, Tan ZH, Allen S, Hockemeyer D, Lee AY, Veldman MB, Adkins RS, Ament SA, Bravo HC, Carter R, Chatterjee A, Colantuoni C, Crabtree J, Creasy H, Felix V, Giglio M, Herb BR, Kancherla J, Mahurkar A, McCracken C, Nickel L, Olley D, Orvis J, Schor M, Hood G, Dichter B, Grauer M, Helba B, Bandrowski A, Barkas N, Carlin B, D’Orazi FD, Degatano K, Gillespie TH, Khajouei F, Konwar K, Thompson C, Kelly K, Mok S, Sunkin S. A multimodal cell census and atlas of the mammalian primary motor cortex. Nature 2021; 598:86-102. [PMID: 34616075 PMCID: PMC8494634 DOI: 10.1038/s41586-021-03950-0] [Citation(s) in RCA: 205] [Impact Index Per Article: 68.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Accepted: 08/25/2021] [Indexed: 12/14/2022]
Abstract
Here we report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Our results advance the collective knowledge and understanding of brain cell-type organization1-5. First, our study reveals a unified molecular genetic landscape of cortical cell types that integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a consensus taxonomy of transcriptomic types and their hierarchical organization that is conserved from mouse to marmoset and human. Third, in situ single-cell transcriptomics provides a spatially resolved cell-type atlas of the motor cortex. Fourth, cross-modal analysis provides compelling evidence for the transcriptomic, epigenomic and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types. We further present an extensive genetic toolset for targeting glutamatergic neuron types towards linking their molecular and developmental identity to their circuit function. Together, our results establish a unifying and mechanistic framework of neuronal cell-type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.
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Li JF, Ding L, Chi CY, Wang JX. [Removal of excessive length of superior cornu of thyroid cartilage under suspension laryngoscope: a case report]. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2021; 56:998-999. [PMID: 34666455 DOI: 10.3760/cma.j.cn115330-20201112-00865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- J F Li
- Department of Otorhinolaryngology Head and Neck Surgery,Dongfang Hospital,Beijing University of Chinese Medicine,Beijing 100078,China
| | - L Ding
- Department of Otorhinolaryngology Head and Neck Surgery,Dongfang Hospital,Beijing University of Chinese Medicine,Beijing 100078,China
| | - C Y Chi
- Department of Otorhinolaryngology Head and Neck Surgery,Dongfang Hospital,Beijing University of Chinese Medicine,Beijing 100078,China
| | - J X Wang
- Department of Otorhinolaryngology Head and Neck Surgery,Dongfang Hospital,Beijing University of Chinese Medicine,Beijing 100078,China
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Lu S, Zhou J, Jian H, Wu L, Cheng Y, Fan Y, Fang J, Chen G, Zhang Z, Lv D, Jiang L, Wu R, Jin X, Zhang X, Zhang J, Sun G, Huang D, Cui J, Guo R, Ding L. 1370TiP Befotertinib versus icotinib as first-line treatment in patients with advanced or metastatic EGFR-mutated non-small cell lung cancer: A multicenter, randomized, open-label, controlled phase III study. Ann Oncol 2021. [DOI: 10.1016/j.annonc.2021.08.1971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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48
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Tonneau M, Phan K, Kazandjian S, Elkrief A, Panasci J, Richard C, Nolin-Lapalme A, El Sayed R, Ding L, Nair T, Malo J, Chandelier F, Kafi K, O'Brien J, Di Jorio L, Muanza T, Routy B. 1357P A deep radiomics approach to assess PD-L1 expression and clinical outcomes in patients with advanced non-small cell lung cancer treated with immune checkpoint inhibitors: A multicentric study. Ann Oncol 2021. [DOI: 10.1016/j.annonc.2021.08.1957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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49
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Qi Z, Ding L, Meng D, Liu H, Wang J, Song L, Lyu YJ, Jia HX, Hao M, Tian ZQ, Wang JT. [Relationship between serum folate and CIN1 prognosis and its interaction with HR-HPV infection]. Zhonghua Zhong Liu Za Zhi 2021; 43:866-871. [PMID: 34407593 DOI: 10.3760/cma.j.cn112152-20200812-00732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To evaluate the relationship between serum folate and the prognosis of cervical intraepithelial neoplasia grade I (CIN1) and the interaction between folate and high risk human papillomavirus (HR-HPV) infection. Methods: From a community-based married women cohort established in Jiexiu and Yangqu County of Shanxi Province from June to December 2014, a total of 564 eligible women with CIN1 by pathologically diagnosed were recruited. The pathological examination was performed again 12 months later. According to the prognosis of CIN1, participants were divided into CIN1 regression group, persistence and progression group, respectively. Nested case-control study was used to explore the relationship between serum folate and CIN1 prognosis, and additive model was used to analyze the interaction between serum folate and HR-HPV infection. Results: Among 564 CIN1 patients, 479 cases underwent pathological examination again, 331 were divided in CIN1 regression group and other 148 in persistence and progression group. The levels of serum folate in CIN1 regression group and persistence and progression group were (18.890±8.360) and (15.640±5.550) nmol/L, respectively, and the difference was statistically significant (Z=-6.937, P<0.001). HPV infection was detected in 154 patients, including 148 cases of HR-HPV infection and 6 cases of low risk human papillomavirus (LR-HPV) infection. Univariate analysis showed that there were significant differences in the age, passive smoking, frequency of pudendal cleaning, frequency of cleaning after sex, frequency of changing underwear, serum folate and HR-HPV infection between regression group and persistence and progression group (P<0.05). Multivariate logistic regression analysis showed that the frequency of pudendal cleaning (OR=0.422, 95%CI: 0.238-0.750), frequency of changing underwear (OR=0.574, 95%CI: 0.355-0.928), serum folate (13.06-16.78nmol/L: OR=4.806, 95%CI: 2.355-9.810; ≤13.05nmol/L: OR=8.378, 95%CI: 4.024-17.445), HR-HPV infection (OR=1.852, 95%CI: 1.170-2.933) were the independent influencing factors of CIN1 prognosis. Interaction analysis showed that the relative excess risk of low serum folate level and HR-HPV infection for the CIN1 persistence and progression was 4.992 (95%CI: 0.189-9.796), attributable proportion due to interaction was 0.552 (95%CI: 0.279-0.824), synergy index was 2.632 (95%CI: 1.239-5.588), aOR of serum folate≤16.78 nmol/L and HR-HPV infection positive was 9.055 (95%CI: 4.878-16.807). Conclusion: Low serum folate level could increase the risk of CIN1 persistence and progression, and might enhance the risk when combined with HR-HPV infection.
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Affiliation(s)
- Z Qi
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - L Ding
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - D Meng
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - H Liu
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - J Wang
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - L Song
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - Y J Lyu
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - H X Jia
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
| | - M Hao
- Department of Obstetrics and Gynecology, the Second Hospital of Shanxi Medical University, Taiyuan 030001, China
| | - Z Q Tian
- Department of Personnel, Stomatological Hospital of Shanxi Medical University, Taiyuan 030001, China
| | - J T Wang
- Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
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Lauck S, Yu M, Ding L, Webb J. Quality of life after transcatheter aortic valve implantation: Perspectives from a Canadian data base. Eur J Cardiovasc Nurs 2021. [DOI: 10.1093/eurjcn/zvab060.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Abstract
Funding Acknowledgements
Type of funding sources: None.
Background
We examined changes in quality of life (QOL) of patients after transcatheter aortic valve implantation (TAVI).
Methods
We conducted an observational cohort study of consecutive patients who had TAVI between 2016-2019 in British Columbia, Canada. QOL was measured at baseline, 30-day and 1-year using the Kansas City Cardiomyopathy Questionnaire (KCCQ-OS). We used linear regression modelling to examine factors associated with 30-day changes in QOL, logistic regression modelling to identify predictors of having a poor outcome, and Cox regression modelling to ascertain risk estimates of the effect of QOL on 1-year mortality.
Results
The cohort included 1,706 patients [742 women (43.5%)]; median [interquartile range, IQR] age 83 (77,86). Median (IQR) baseline KCCQ-OS was 45 (28.2,67), indicating severe impairment. Patients alive at 1-year (91.3%) reported a mean improvement of 24.1 (95% CI, 22.7-25.6) points in the KCCQ-OS at 30-day, which was sustained at 1-year (25.3; 95% CI, 23.8,26.8). Older age, lower baseline health status, lower aortic valve gradient, lower hemoglobin, atrial fibrillation and non-transfemoral access were associated with worse 30-day QOL. At 1-year, 65% of patients had a favourable outcome; additional risk factors for 1-year mortality (8.7%) were male sex, NYHA Class IV, severe pulmonary and renal disease, diabetes, and in-patient status.
Conclusions
TAVI is associated with significant early improvement in QOL which is sustained at 1 year in a "real world" registry. The inclusion of QOL can support treatment decision and the patient-centred evaluation of TAVI
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Affiliation(s)
- S Lauck
- St Paul"s Hospital, Vancouver, Canada
| | - M Yu
- University of British Columbia, Vancouver, Canada
| | - L Ding
- University of British Columbia, Vancouver, Canada
| | - J Webb
- St Paul"s Hospital, Vancouver, Canada
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