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Yu J, Xing L, Cheng G, Chen L, Dong L, Fu X, Guo Y, Han Z, Jiang D, Li J, Lin Y, Liu A, Liu J, Liu J, Liu Y, Lv D, Ma C, Ren Y, Wang S, Wang Y, Xiao C, Yan S, Yang F, Yang W, Zang A, Zhang X, Zhang Y, Zhao R, Zhou J. P21.10 Real-World Treatment Patterns in Chinese Stage III NSCLC Patients - A Prospective, Non-Interventional Study (MOOREA trial). J Thorac Oncol 2021. [DOI: 10.1016/j.jtho.2021.01.590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Xing L, Hou JB. [Application and evolution of hybrid OCT-IVUS intravascular imaging technique]. ZHONGHUA XIN XUE GUAN BING ZA ZHI 2021; 49:115-120. [PMID: 33611896 DOI: 10.3760/cma.j.cn112148-20201115-00910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Pan YQ, Bahoussi AN, Guo F, Xing L. A single nucleotide distinguishes the SARS-CoV-2 in the Wuhan outbreak in December 2019 from that in Beijing-Xinfadi in June 2020, China. New Microbes New Infect 2021; 39:100835. [PMID: 33425367 PMCID: PMC7785950 DOI: 10.1016/j.nmni.2020.100835] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 12/08/2020] [Accepted: 12/10/2020] [Indexed: 12/28/2022] Open
Abstract
Two major locally transmitted outbreaks of coronavirus disease 2019 occurred in China, one in Wuhan from December 2019 to April 2020, another in Beijing-Xinfadi in June 2020. Severe acute respiratory syndrome coronavirus 2 isolated from these two outbreaks can be distinguished by a conserved pyrimidine nucleotide located at nucleotide position 241 in the 5′-untranslated region of the virus genome.
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Wang F, Xing L, Bagshaw H, Buyyounouski M, Han B. Automated Needle Digitization in Ultrasound-based Prostate High Dose-rate Brachytherapy Using a Deep Learning Algorithm. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.857] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Kiss M, Zhang H, Fix M, Manser P, Xing L. Z-Super Resolution CT-Image Generation With A Deep 3D Fully Convolutional Neural Network. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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56
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Bibault J, Xing L. Predicting Survival in Prostate Cancer Patients with Interpretable Artificial Intelligence. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Vasudevan V, Huang C, Simiele E, Yu L, Xing L, Schuler E. Combining Monte Carlo with Deep Learning: Predicting High-resolution, Low-noise Dose Distributions Using a Generative Adversarial Network for Fast and Precise Monte Carlo Simulations. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.2157] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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58
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Wang Y, Dai J, Fang C, Zhang S, Wang J, Yin Y, Jiang S, Guo J, Lei F, Tu Y, Xing L, Hou J, Yu B. Predictors of plaque erosion in current smokers and non-current smokers presented with ST-segment elevation myocardial infarction: an optical coherence tomography study. Eur Heart J 2020. [DOI: 10.1093/ehjci/ehaa946.1780] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Background
Plaque erosion with subsequent coronary thrombosis is considered as an important cause of ST-segment elevation myocardial infarction (STEMI). Smoking is a major risk factor for acute coronary thrombosis. However, the relationship between current smoking status and plaque erosion has not been systematically investigated.
Purpose
The present study aimed to investigate predictors of plaque erosion in current smokers and non-current smokers with STEMI by using optical coherence tomography (OCT).
Methods
Between January 2015 to December 2017, a total of 1313 STEMI patients underwent pre-intervention OCT of culprit lesion were enrolled and divided into two groups based on current smoking status: current smoking group (n=713) and non-current smoking group (n=600). Using established criteria, quantitative and qualitative underlying plaque characteristics were assessed by OCT. Clinical, angiographic and OCT characteristics of all enrolled patients were recorded. Univariable and multivariable logistic regression analyses were used to identify predictors of plaque erosion in two groups.
Results
Plaque erosion were found in 30.9% (220/713) culprit lesions in current smoking group and 20.8% (125/600) of those in non-current smoking group detected by OCT. In multivariate regression analysis, the predictors that strongly related to plaque erosion in the current smoking group were nearby bifurcation (OR: 4.84; 95% CI:2.38–9.87; p<0.001); the minimum fiber cap thickness (FCT, OR:1.05; 95% CI:1.03–1.08; p<0.001); thin-cap fibroatheroma (TCFA, OR: 0.22; 95% CI: 0.07–0.67; p=0.007) and lipid core length (OR: 0.91; 95% CI: 0.84–0.97; p=0.007). The predictors in the non-current smoking group were nearby bifurcation (OR: 4.84; 95% CI: 2.38–9.87; p=0.006); the minimal FCT (OR: 1.09; 95% CI: 1.06–1.13; p<0.001); multi-vessel disease (MVD, OR: 0.43; 95% CI: 0.19–0.97; p=0.042) and dyslipidemia (OR: 0.34; 95% CI: 0.14–0.84; p=0.020).
Conclusions
Predictors of plaque erosion causing STEMI onset are different between current smokers and non-current smoker, with nearby bifurcation and thicker minimal FCT both predicting plaque erosion in two groups of patients.
Funding Acknowledgement
Type of funding source: Public grant(s) – National budget only. Main funding source(s): National Key Research and Development Program of China, National Natural Science Foundation of China.
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Capaldi D, Binkley M, Ko R, Xing L, Maxim P, Diehn M, Loo B. Parametric Response Mapping as an Imaging Biomarker for Regional Ventilation in Stereotactic Ablative Radiotherapy. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.2353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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60
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Dong P, Xing L. Deep DoseNet: A Deep Neural Network based Dose Calculation Algorithm. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Fang C, Dai J, Zhang S, Wang J, Wang Y, Li L, Xing L, Hou J, Yu B. Morphological characteristics of plaque erosion with noncritical coronary stenosis: an optical coherence tomography study. Eur Heart J 2020. [DOI: 10.1093/ehjci/ehaa946.1533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Background
Plaque erosion is a frequent and important mechanism of acute coronary thrombosis only secondary to plaque rupture. Recent studies suggested plaque erosion with noncritical stenosis could be treated conservatively that distinct from those with critical stenosis. However, characteristics of plaque erosions with different coronary stenosis remain unknown.
Purpose
The present study aimed to investigate morphological features of plaque erosions with different coronary stenosis using optical coherence tomography (OCT).
Methods
Consecutive ST-segment elevated myocardial infarction (STEMI) patients with OCT images of culprit lesion between August 2014 and December 2017 were enrolled and 348 cases presented with plaque erosion identified by OCT. Based on the severity of lumen area stenosis [calculated by (1-minimal lumen area/reference lumen area) * 100%], all culprit plaque erosions were divided into three groups: Group A (area stenosis<50%, n=50, 14.4%); Group B (50%≤area stenosis<75%, n=146, 42.0%); Group C (area stenosis≥75%, n=152, 43.7%). Clinical characteristics, lesion features detected by coronary angiography and OCT were compared among three groups.
Results
Of all 348 STEMI patients with plaque erosions, patients in Group A were youngest (p=0.008) and had the lowest frequency of hypertension (p=0.029) as compared with those in Group B and C. Angiographic analysis showed 72.0% of plaque erosions in Group A located in LAD, while 67.8% in Group B and 53.9% in Group C (p=0.039). OCT findings (Figure 1-A) showed the prevalence of fibrous plaque was significantly highest in Group A than those in Group B and C (82.0% vs. 54.8% vs. 34.9%, p<0.001), whereas lipid rich plaque was most frequent in Group C (16.0% vs. 43.8% vs. 62.5%, p<0.001). The prevalence of macrophage (p<0.001), microvessel (p=0.009) and cholesterol crystals (p<0.001) increased gradually from plaque erosion with lumen area stenosis <50% to 50–75% to ≥75%. Notably, compared with Group B and C, nearby bifurcation was most common in Group A (72.0% vs. 67.1% vs. 55.3%, p=0.036). Multivariable regression analyses (Figure 1-B) showed fibrous plaque and nearby bifurcation were independently associated with plaque erosion with noncritical stenosis (area stenosis<75%).
Conclusion
56.3% plaque erosion in STEMI patients presented with noncritical stenosis, having distinct morphological features from erosion with critical stenosis. Fibrous plaque and nearby bifurcation were independently associated with the presence of noncritically stenotic plaque erosion, remaining a desire to tailor treatment therapy to individual patients.
Figure 1
Funding Acknowledgement
Type of funding source: Foundation. Main funding source(s): National Key R&D Program of China
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Yang Y, Kovalchuk N, Gensheimer M, Beadle B, Bagshaw H, Buyyounouski M, Swift P, Chang D, Le Q, Xing L. Evaluation of a Knowledge-Guided Automated Treatment Planning Tool. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.2365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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63
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Tong X, Chen X, Qiu Q, Sun X, Xing L. Integrative Nomogram of CT-based Radiomics and Clinical Features for Predicting Oligometastases at Recurrent after Definitive Chemoradiotherapy for Locally Advanced Non-Small Cell Lung Cancer. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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64
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Yan H, Schneider B, Graves E, Sun W, Xing L, MacDonald C, Liu W. Focused kV X-rays for Preclinical Studies of Radiation-based Neuromodulation. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.2081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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65
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Fan J, Xing L, Yang Y. Verification of the Machine Delivery Parameters of Treatment Plan via Deep Learning. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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66
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Schueler E, Chuang C, Yang Y, Xing L, Zhao W. Mitigating the Uncertainty in Small Field Dosimetry for Stereotactic Body Radiation Therapy by Leveraging Machine Learning Strategies. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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67
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Zhao W, Lv T, Chen Y, Xing L. Dual-energy CT Imaging Using a Single-energy CT Data via Deep Learning: A Contrast-enhanced CT Study. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.2154] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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68
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Zhao W, Capaldi D, Chuang C, Xing L. Fiducial-Free Image-Guided Spinal Stereotactic Radiosurgery Enabled Via Deep Learning. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.2348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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69
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Chen Y, Xing L, Bagshaw H, Buyyounouski M, Han B. Deep Learning-Based Intraprostatic Lesion Segmentation Using Multi-Parametric MRI For Prostate Radiation Therapy. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.2275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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70
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Shah PT, Xing L. Puzzling increase and decrease in COVID-19 cases in Pakistan. New Microbes New Infect 2020; 38:100791. [PMID: 33101693 PMCID: PMC7568489 DOI: 10.1016/j.nmni.2020.100791] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 10/11/2020] [Accepted: 10/13/2020] [Indexed: 12/15/2022] Open
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71
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Bibault JE, Xing L. Intelligence artificielle interprétable pour la prédiction de la survie dans le cancer de prostate. Cancer Radiother 2020. [DOI: 10.1016/j.canrad.2020.08.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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72
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Li X, Xing L, Lai R, Yuan C, Humbert P. Literature mapping: association of microscopic skin microflora and biomarkers with macroscopic skin health. Clin Exp Dermatol 2020; 46:21-27. [PMID: 32786033 PMCID: PMC7754415 DOI: 10.1111/ced.14353] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 03/09/2020] [Accepted: 05/27/2020] [Indexed: 10/27/2022]
Abstract
Associations between skin microbes or biomarkers and pathological conditions have been reported in the literature. However, there is a lack of clarity on the interaction between the coexistence of common skin microbes with skin physiology and subsequent development of clinical symptoms, and the role of biomarkers in mediating these changes before the development of skin disease. In this review, we aim to identify areas in which extensive research for the studied factors has already been conducted, and which research areas are under-represented. The SciFinder database was searched for articles containing key words including specific skin microbes, biomarkers, skin physiology and diseases from the beginning of the SciFinder data record to 26 April 2016, and we included an additional relevant recent publication from our group. Among the 8000 + articles selected, the frequency of keyword pairs between two roles [microscopic markers (microflora or biomarkers) and reactions (skin physiology or clinical symptoms, or skin disease)] was investigated. Associated research between the individual factors such as skin microflora or biomarkers (chosen based on our earlier publication) and specific biophysical parameters, symptoms or skin disease was identified. The present research heatmap emphasizes the significance of a structured review of research on concerned factor associations to identify early/subclinical clues that can be used to prevent progression to overt skin disease with the help of precise skin care or early intervention, as indicated by skin microflora, biomarkers and an interactive skin biophysics profile. The findings provide a novel approach to explore such associations and may guide future research directed towards predicting disease from early/subclinical symptoms.
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Nguyen HPT, Tuan TH, Xing L, Matsumoto M, Sakai G, Suzuki T, Ohishi Y. Supercontinuum generation in a chalcogenide all-solid hybrid microstructured optical fiber. OPTICS EXPRESS 2020; 28:17539-17555. [PMID: 32679961 DOI: 10.1364/oe.394968] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Accepted: 05/04/2020] [Indexed: 06/11/2023]
Abstract
We report the fabrication of a chalcogenide all-solid hybrid microstructured optical fiber and its application in supercontinuum generation for the first time, to the best of our knowledge. The fiber possesses all-normal and flattened chromatic dispersion, making it highly potential for broad and coherent supercontinuum generation. By pumping the fiber with a femtosecond laser at 3, 4, and 5 μm, broad supercontinua with good spectral flatness are generated. The broadest SC spectrum extending from 2.2 to 10 μm at -20 dB level was obtained when the fiber was pumped at 5 μm with an input power of 3.9 mW.
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74
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Bibault JE, Xing L, Giraud P, El Ayachy R, Giraud N, Decazes P, Burgun A, Giraud P. Radiomics: A primer for the radiation oncologist. Cancer Radiother 2020; 24:403-410. [PMID: 32265157 DOI: 10.1016/j.canrad.2020.01.011] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 01/22/2020] [Indexed: 12/14/2022]
Abstract
PURPOSE Radiomics are a set of methods used to leverage medical imaging and extract quantitative features that can characterize a patient's phenotype. All modalities can be used with several different software packages. Specific informatics methods can then be used to create meaningful predictive models. In this review, we will explain the major steps of a radiomics analysis pipeline and then present the studies published in the context of radiation therapy. METHODS A literature review was performed on Medline using the search engine PubMed. The search strategy included the search terms "radiotherapy", "radiation oncology" and "radiomics". The search was conducted in July 2019 and reference lists of selected articles were hand searched for relevance to this review. RESULTS A typical radiomics workflow always includes five steps: imaging and segmenting, data curation and preparation, feature extraction, exploration and selection and finally modeling. In radiation oncology, radiomics studies have been published to explore different clinical outcome in lung (n=5), head and neck (n=5), esophageal (n=3), rectal (n=3), pancreatic (n=2) cancer and brain metastases (n=2). The quality of these retrospective studies is heterogeneous and their results have not been translated to the clinic. CONCLUSION Radiomics has a great potential to predict clinical outcome and better personalize treatment. But the field is still young and constantly evolving. Improvement in bias reduction techniques and multicenter studies will hopefully allow more robust and generalizable models.
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Lee S, Chu YS, Yoo SK, Choi S, Choe SJ, Koh SB, Chung KY, Xing L, Oh B, Yang S. Augmented decision-making for acral lentiginous melanoma detection using deep convolutional neural networks. J Eur Acad Dermatol Venereol 2020; 34:1842-1850. [PMID: 31919901 DOI: 10.1111/jdv.16185] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Accepted: 12/13/2019] [Indexed: 12/14/2022]
Abstract
BACKGROUND Several studies have achieved high-level performance of melanoma detection using convolutional neural networks (CNNs). However, few have described the extent to which the implementation of CNNs improves the diagnostic performance of the physicians. OBJECTIVE This study is aimed at developing a CNN for detecting acral lentiginous melanoma (ALM) and investigating whether its implementation can improve the initial decision for ALM detection made by the physicians. METHODS A CNN was trained using 1072 dermoscopic images of acral benign nevi, ALM and intermediate tumours. To investigate whether the implementation of CNN can improve the initial decision for ALM detection, 60 physicians completed a three-stage survey. In Stage I, they were asked for their decisions solely on the basis of dermoscopic images provided to them. In Stage II, they were also provided with clinical information. In Stage III, they were provided with the additional diagnosis and probability predicted by the CNN. RESULTS The accuracy of ALM detection in the participants was 74.7% (95% confidence interval [CI], 72.6-76.8%) in Stage I and 79.0% (95% CI, 76.7-81.2%) in Stage II. In Stage III, it was 86.9% (95% CI, 85.3-88.4%), which exceeds the accuracy delivered in Stage I by 12.2%p (95% CI, 10.1-14.3%p) and Stage II by 7.9%p (95% CI, 6.0-9.9%p). Moreover, the concordance between the participants considerably increased (Fleiss-κ of 0.436 [95% CI, 0.437-0.573] in Stage I, 0.506 [95% CI, 0.621-0.749] in Stage II and 0.684 [95% CI, 0.621-0.749] in Stage III). CONCLUSIONS Augmented decision-making improved the performance of and concordance between the clinical decisions of a diverse group of experts. This study demonstrates the potential use of CNNs as an adjoining, decision-supporting system for physicians' decisions.
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Dong P, Xing L. DoseNet: A Deep Neural Network for Accurate Dosimetric Transformation between Different Spatial Resolutions and/or Different Dose Calculation Algorithms for Precision Radiation Therapy. Int J Radiat Oncol Biol Phys 2019. [DOI: 10.1016/j.ijrobp.2019.06.2471] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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77
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Liu S, Bush K, Bertini J, FU Y, Lewis J, Pham D, Yang Y, Niedermayr T, Skinner L, Xing L, Beadle B, Hsu A, Kovalchuk N. Optimizing Efficiency and Safety in External Beam Radiotherapy Using Automated Plan Check (APC) Tool and Six Sigma Methodology. Int J Radiat Oncol Biol Phys 2019. [DOI: 10.1016/j.ijrobp.2019.06.422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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78
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Shen L, Zhao W, Xing L. Volumetric Imaging with a Single Projection Enabled by Deep Learning. Int J Radiat Oncol Biol Phys 2019. [DOI: 10.1016/j.ijrobp.2019.06.259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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79
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Xu L, Yang P, Liang W, Xing L, Niu T, Huang M. A Machine Learning Approach with Support Vector Machine (SVM) for Prediction of Preoperative Lymph Node Status with MR Images and clinical features for Intrahepatic Cholangiocarcinoma. Int J Radiat Oncol Biol Phys 2019. [DOI: 10.1016/j.ijrobp.2019.06.203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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DONG X, Xing L, Sun X, Wan H, Yu J, Liu H, Cheng Y. Dynamic Monitoring of Response Heterogeneity between Primary and Metastases Reveals Resistance Molecular Mechanisms in Advanced Non-Small Cell Lung Cancer with Acquired Resistance to EGFR-TKIs. Int J Radiat Oncol Biol Phys 2019. [DOI: 10.1016/j.ijrobp.2019.06.1324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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81
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Zhao W, Shen L, Han B, Yang Y, Cheng K, Toesca D, Koong A, Chang D, Xing L. Deep Learning Approach for Markerless Pancreatic Tumor Target Localization. Int J Radiat Oncol Biol Phys 2019. [DOI: 10.1016/j.ijrobp.2019.06.268] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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82
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Han B, Xing L, Soltys S, Wang L. Machine Learning Application for Accurate Dose Verification of MLC-based Robotic Stereotactic Radiosurgery and Stereotactic Body Radiotherapy. Int J Radiat Oncol Biol Phys 2019. [DOI: 10.1016/j.ijrobp.2019.06.929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Wang J, Li Y, Xing L, Zhao M, Zhang S, Li Z, Yao Z, Li M. Three novel mutations in GPNMB in two pedigrees with amyloidosis cutis dyschromica. Br J Dermatol 2019; 181:1327-1329. [PMID: 31260093 DOI: 10.1111/bjd.18260] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Xing L, Jin B, Fu X, Zhu J, Guo X, Xu W, Mou X, Wang Z, Jiang F, Zhou Y, Chen X, Shu J. Identification of functional estrogen response elements in glycerol channel Aquaporin-7 gene. Climacteric 2019; 22:466-471. [PMID: 30888885 DOI: 10.1080/13697137.2019.1580255] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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85
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Lindsay C, Bazalova‐Carter M, Wang A, Shedlock D, Wu M, Newson M, Xing L, Ansbacher W, Fahrig R, Star‐Lack J. Investigation of combined
kV
/
MV CBCT
imaging with a high‐
DQE MV
detector. Med Phys 2018; 46:563-575. [DOI: 10.1002/mp.13291] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Revised: 11/01/2018] [Accepted: 11/02/2018] [Indexed: 01/23/2023] Open
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Zhou X, Xing L, Han B. Application of Machine Learning Techniques for Accurate Dose Calculation of Electron Treatment with Small and Irregular Fields. Int J Radiat Oncol Biol Phys 2018. [DOI: 10.1016/j.ijrobp.2018.07.1432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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87
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Yang Y, Xing L. Autonomous Treatment Plan Optimization Strategy Augmented By Using a Knowledge-Guided and Irregularly Downsampled Voxelization Scheme. Int J Radiat Oncol Biol Phys 2018. [DOI: 10.1016/j.ijrobp.2018.07.177] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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88
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Liu H, Xing L. Extraction of Spatial and Dosimetric Features of Isodose Distribution(s) and Its Application in Treatment Plan Optimization. Int J Radiat Oncol Biol Phys 2018. [DOI: 10.1016/j.ijrobp.2018.07.176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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89
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Zhao W, Han B, Yang Y, Buyyounouski M, Hancock S, Bagshaw H, Xing L. Visualizing the Invisible in Prostate Radiation Therapy: Markerless Prostate Target Localization Via a Deep Learning Model and Monoscopic Kv Projection X-Ray Image. Int J Radiat Oncol Biol Phys 2018. [DOI: 10.1016/j.ijrobp.2018.06.319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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90
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Han B, Yuan Y, Hancock S, Bagshaw H, Buyyounouski M, Xing L. Prostate Cancer Staging and Radiation Treatment Planning Using Deep Learning on MRI. Int J Radiat Oncol Biol Phys 2018. [DOI: 10.1016/j.ijrobp.2018.06.262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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91
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Vernekohl D, Tzoumas S, Xing L. Coded-Aperture Compressed Sensing for Image Guidance in Radiation Therapy. Int J Radiat Oncol Biol Phys 2018. [DOI: 10.1016/j.ijrobp.2018.06.195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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92
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Cheng K, Han F, Zhang G, Zhao W, Jenkins C, Vernekohl D, Xing L. Dual Modality Shortwave Infrared Fluorescence and Photoacoutic Imaging of Radiation-Induced Vascular Damage in Stereotactic Ablative Radiation Therapy. Int J Radiat Oncol Biol Phys 2018. [DOI: 10.1016/j.ijrobp.2018.07.662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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93
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Xing L, Pan Y, Shi Y, Shu Y, Feng J, Li W, Cao L, Wang L, Gu W, Song Y, Yu J. P1.13-25 Efficacy and Safety of Osimertinib in EGFR T790M-Positive Advanced NSCLC Patients with Brain Metastases (APOLLO Study). J Thorac Oncol 2018. [DOI: 10.1016/j.jtho.2018.08.882] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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94
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Li T, Lyu J, Zhu G, Li J, Zhao R, Zhu S, Wang J, Xing L, Yang D, Xie C, Shen LF, Shi HP, Lang J. Influence of enteral nutrition on nutritional status, treatment toxicities, and short-term outcomes in esophageal carcinoma patients treated with concurrent chemoradiotherapy: A prospective, multicenter, randomized controlled study. Ann Oncol 2018. [DOI: 10.1093/annonc/mdy282.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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95
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Xing L, Wang J, Li L, Ma Z, Hu C, Zhang H, Shan L, Chen Z, Zhang J, Zhou Q, Gao S, Ma X, Sun P, Ren Q, Wu M, Wu J, Li J, Yao J, Ma H, Wang W, Yao W, Wang D, Kang J, Li G, Wang X, Zhu W, Wang J, Yu J. MA02.06 A Randomized, Double-Blind, Placebo-Controlled Trial of Chemotherapy Combined with Yangzheng Xiaoji in Advanced NSCLC. J Thorac Oncol 2018. [DOI: 10.1016/j.jtho.2018.08.326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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96
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Jia SS, Zhang Y, Xing L. [Comparative study on several methods for collagen a1(Ⅰ) and collagen a1(Ⅲ) measurement]. ZHONGHUA BING LI XUE ZA ZHI = CHINESE JOURNAL OF PATHOLOGY 2018; 47:638-640. [PMID: 30107674 DOI: 10.3760/cma.j.issn.0529-5807.2018.08.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
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97
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Dai J, Zhang S, Fang C, Jia H, Xing L, Hu S, Zhang S, Hou J, Yu B. P578Clinical and angiographic characteristics, mechanisms of atherothrombosis, and plaque morphology in women versus men with ST-segment elevation myocardial infarction. Eur Heart J 2018. [DOI: 10.1093/eurheartj/ehy564.p578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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98
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Bai YP, Zhang Y, Tian C, Xing L, Liu HG. [Cytologic diagnosis of adenoid cystic carcinoma of salivary glands and distinction from basal cell adenoma]. ZHONGHUA BING LI XUE ZA ZHI = CHINESE JOURNAL OF PATHOLOGY 2018; 47:279-283. [PMID: 29690668 DOI: 10.3760/cma.j.issn.0529-5807.2018.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To describe the cytologic features of adenoid cystic carcinoma (ADCC) of salivary glands, and to identify distinguishing cytologic features of ADCC and basal cell adenoma (BCA). Methods: A retrospective review of cytology smears of 30 cases of ADCC and 12 cases of BCA of salivary glands were performed. All cases were collected from Beijing Tongren Hospital, Capital Medical University from January 2010 to January 2017. Except for 2 aspirate smears of ADCC, all were touch imprint smears. All cases had further histological confirmation. Results: Neoplastic ductal cells of ADCC were arranged in three-dimensional clusters, sheets and singles. Hyaline globules were found in most cases (20/30, 66.7%). The nuclei were round to oval, showing varying degrees of nuclear atypia. These included (1) the nuclei were hyperchromatic, demonstrating coarse or slightly coarse, irregularly distributed chromatin; (2) the nuclei were slightly large and vary in size; (3) appearance of the nuclei had a different degree of irregularity (often mild). Nucleoli were common seen (21/30, 70.0%), and were prominent in some cases. Mitosis and necrosis were rare. Cytologically, BCA showed cell arrangements and nuclear features overlapped with those of ADCC. The cytologic difference between these two tumors included: (1) the tumor cells presented rarely in singles; (2) hyaline globules were very uncommon (1/12) in BCA; (3) nuclei of BCA were hypochromatic or slightly hyperchromatic, homogeneous and uniform in appearance and size, overall without nuclear atypia and they were smaller and slender then those of ADCC and (4) individual cells of BCA showed relatively abundant cytoplasm. Conclusions: The cytologic features of ADCC and BCA both overlap and different from each other. Most cases can be diagnosed by cytologic examination. The presence of hyaline globules is an important diagnostic clue of ADCC, although not pathognomonic. Nuclear atypia of neoplastic ductal cells is an essential cytological feature in the diagnosis of ADCC, and is the most reliable point for differential diagnosis of ADCC and BCA.
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99
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Li X, Gu J, Wang C, Deng Q, Ma S, Ren Y, Xing L, Niu T. P1.14-001 The Feasibility of Predicting Radiation Pneumonitis Using Lung Equivalent Uniform Dose (LEUD) in Volumetric-Modulated Arc. J Thorac Oncol 2017. [DOI: 10.1016/j.jtho.2017.09.1019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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100
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Qian C, Cui C, Wang X, Zhou C, Hu P, Li M, Li R, Xiao J, Wang X, Chen P, Xing L, Cao A. Molecular characterisation of the broad-spectrum resistance to powdery mildew conferred by the Stpk-V gene from the wild species Haynaldia villosa. PLANT BIOLOGY (STUTTGART, GERMANY) 2017; 19:875-885. [PMID: 28881082 DOI: 10.1111/plb.12625] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2017] [Accepted: 08/30/2017] [Indexed: 06/07/2023]
Abstract
A key member of the Pm21 resistance gene locus, Stpk-V, derived from Haynaldia villosa, was shown to confer broad-spectrum resistance to wheat powdery mildew. The present study was planned to investigate the resistance mechanism mediated by Stpk-V. Transcriptome analysis was performed in Stpk-V transgenic plants and recipient Yangmai158 upon Bgt infection, and detailed histochemical observations were conducted. Chromosome location of Stpk-V orthologous genes in Triticeae species was conducted for evolutionary study and over-expression of Stpk-V both in barley and Arabidopsis was performed for functional study. The transcriptome results indicate, at the early infection stage, the ROS pathway, JA pathway and some PR proteins associated with the SA pathway were activated in both the resistant Stpk-V transgenic plants and susceptible Yangmai158. However, at the later infection stage, the genes up-regulated at the early stage were continuously held only in the transgenic plants, and a large number of new genes were also activated in the transgenic plants but not in Yangmai158. Results indicate that sustained activation of the early response genes combined with later-activated genes mediated by Stpk-V is critical for resistance in Stpk-V transgenic plants. Stpk-V orthologous genes in the representative grass species are all located on homologous group six chromosomes, indicating that Stpk-V is an ancient gene in the grasses. Over-expression of Stpk-V enhanced host resistance to powdery mildew in barley but not in Arabidopsis. Our results enable a better understanding of the resistance mechanism mediated by Stpk-V, and establish a solid foundation for its use in cereal breeding as a gene resource.
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