1
|
Yang JJ, Goff A, Wild DJ, Ding Y, Annis A, Kerber R, Foote B, Passi A, Duerksen JL, London S, Puhl AC, Lane TR, Braunstein M, Waddell SJ, Ekins S. Computational drug repositioning identifies niclosamide and tribromsalan as inhibitors of Mycobacterium tuberculosis and Mycobacterium abscessus. Tuberculosis (Edinb) 2024; 146:102500. [PMID: 38432118 PMCID: PMC10978224 DOI: 10.1016/j.tube.2024.102500] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/20/2024] [Accepted: 02/24/2024] [Indexed: 03/05/2024]
Abstract
Tuberculosis (TB) is still a major global health challenge, killing over 1.5 million people each year, and hence, there is a need to identify and develop novel treatments for Mycobacterium tuberculosis (M. tuberculosis). The prevalence of infections caused by nontuberculous mycobacteria (NTM) is also increasing and has overtaken TB cases in the United States and much of the developed world. Mycobacterium abscessus (M. abscessus) is one of the most frequently encountered NTM and is difficult to treat. We describe the use of drug-disease association using a semantic knowledge graph approach combined with machine learning models that has enabled the identification of several molecules for testing anti-mycobacterial activity. We established that niclosamide (M. tuberculosis IC90 2.95 μM; M. abscessus IC90 59.1 μM) and tribromsalan (M. tuberculosis IC90 76.92 μM; M. abscessus IC90 147.4 μM) inhibit M. tuberculosis and M. abscessus in vitro. To investigate the mode of action, we determined the transcriptional response of M. tuberculosis and M. abscessus to both compounds in axenic log phase, demonstrating a broad effect on gene expression that differed from known M. tuberculosis inhibitors. Both compounds elicited transcriptional responses indicative of respiratory pathway stress and the dysregulation of fatty acid metabolism.
Collapse
Affiliation(s)
- Jeremy J Yang
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA; Data2Discovery, Inc., Bloomington, IN, USA; Department of Internal Medicine Translational Informatics Division, University of New Mexico, Albuquerque, NM, USA
| | - Aaron Goff
- Department of Global Health and Infection, Brighton & Sussex Medical School, University of Sussex, UK
| | - David J Wild
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA; Data2Discovery, Inc., Bloomington, IN, USA
| | - Ying Ding
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA; Data2Discovery, Inc., Bloomington, IN, USA; School of Information, Dell Medical School, University of Texas, Austin, TX, USA
| | - Ayano Annis
- Department of Microbiology and Immunology, School of Medicine, University of North Carolina at Chapel Hill, NC, 27599, USA
| | | | | | - Anurag Passi
- Department of Pediatrics, UC San Diego, San Diego, CA, USA
| | | | | | - Ana C Puhl
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA
| | - Thomas R Lane
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA
| | - Miriam Braunstein
- Department of Microbiology and Immunology, School of Medicine, University of North Carolina at Chapel Hill, NC, 27599, USA
| | - Simon J Waddell
- Department of Global Health and Infection, Brighton & Sussex Medical School, University of Sussex, UK
| | - Sean Ekins
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA.
| |
Collapse
|
2
|
Cappelletti L, Rekerle L, Fontana T, Hansen P, Casiraghi E, Ravanmehr V, Mungall CJ, Yang JJ, Spranger L, Karlebach G, Caufield JH, Carmody L, Coleman B, Oprea TI, Reese J, Valentini G, Robinson PN. Node-degree aware edge sampling mitigates inflated classification performance in biomedical random walk-based graph representation learning. Bioinform Adv 2024; 4:vbae036. [PMID: 38577542 PMCID: PMC10994718 DOI: 10.1093/bioadv/vbae036] [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: 01/11/2023] [Revised: 01/31/2024] [Accepted: 02/29/2024] [Indexed: 04/06/2024]
Abstract
Motivation Graph representation learning is a family of related approaches that learn low-dimensional vector representations of nodes and other graph elements called embeddings. Embeddings approximate characteristics of the graph and can be used for a variety of machine-learning tasks such as novel edge prediction. For many biomedical applications, partial knowledge exists about positive edges that represent relationships between pairs of entities, but little to no knowledge is available about negative edges that represent the explicit lack of a relationship between two nodes. For this reason, classification procedures are forced to assume that the vast majority of unlabeled edges are negative. Existing approaches to sampling negative edges for training and evaluating classifiers do so by uniformly sampling pairs of nodes. Results We show here that this sampling strategy typically leads to sets of positive and negative examples with imbalanced node degree distributions. Using representative heterogeneous biomedical knowledge graph and random walk-based graph machine learning, we show that this strategy substantially impacts classification performance. If users of graph machine-learning models apply the models to prioritize examples that are drawn from approximately the same distribution as the positive examples are, then performance of models as estimated in the validation phase may be artificially inflated. We present a degree-aware node sampling approach that mitigates this effect and is simple to implement. Availability and implementation Our code and data are publicly available at https://github.com/monarch-initiative/negativeExampleSelection.
Collapse
Affiliation(s)
- Luca Cappelletti
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Milano 20133, Italy
| | - Lauren Rekerle
- The Jackson Laboratory for Genomic Medicine, CT 06032, United States
| | - Tommaso Fontana
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Milano 20133, Italy
| | - Peter Hansen
- The Jackson Laboratory for Genomic Medicine, CT 06032, United States
| | - Elena Casiraghi
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Milano 20133, Italy
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94710, United States
| | - Vida Ravanmehr
- The Jackson Laboratory for Genomic Medicine, CT 06032, United States
| | - Christopher J Mungall
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94710, United States
| | - Jeremy J Yang
- Department of Internal Medicine and UNM Comprehensive Cancer Center, UNM School of Medicine, Albuquerque, NM 87102, United States
| | - Leonard Spranger
- Institute of Bioinformatics, Freie Universität Berlin, Berlin, 14195, Germany
| | - Guy Karlebach
- The Jackson Laboratory for Genomic Medicine, CT 06032, United States
| | - J Harry Caufield
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94710, United States
| | - Leigh Carmody
- The Jackson Laboratory for Genomic Medicine, CT 06032, United States
| | - Ben Coleman
- The Jackson Laboratory for Genomic Medicine, CT 06032, United States
- Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, United States
| | - Tudor I Oprea
- Department of Internal Medicine and UNM Comprehensive Cancer Center, UNM School of Medicine, Albuquerque, NM 87102, United States
| | - Justin Reese
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94710, United States
| | - Giorgio Valentini
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Milano 20133, Italy
- ELLIS—European Laboratory for Learning and Intelligent Systems
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, CT 06032, United States
- Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, United States
- ELLIS—European Laboratory for Learning and Intelligent Systems
- Berlin Institute of Health, Charité – Universitätsmedizin Berlin, Berlin, 10117, Germany
| |
Collapse
|
3
|
Oprea TI, Bologa C, Holmes J, Mathias S, Metzger VT, Waller A, Yang JJ, Leach AR, Jensen LJ, Kelleher KJ, Sheils TK, Mathé E, Avram S, Edwards JS. Overview of the Knowledge Management Center for Illuminating the Druggable Genome. Drug Discov Today 2024; 29:103882. [PMID: 38218214 PMCID: PMC10939799 DOI: 10.1016/j.drudis.2024.103882] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 12/22/2023] [Accepted: 01/09/2024] [Indexed: 01/15/2024]
Abstract
The Knowledge Management Center (KMC) for the Illuminating the Druggable Genome (IDG) project aims to aggregate, update, and articulate protein-centric data knowledge for the entire human proteome, with emphasis on the understudied proteins from the three IDG protein families. KMC collates and analyzes data from over 70 resources to compile the Target Central Resource Database (TCRD), which is the web-based informatics platform (Pharos). These data include experimental, computational, and text-mined information on protein structures, compound interactions, and disease and phenotype associations. Based on this knowledge, proteins are classified into different Target Development Levels (TDLs) for identification of understudied targets. Additional work by the KMC focuses on enriching target knowledge and producing DrugCentral and other data visualization tools for expanding investigation of understudied targets.
Collapse
Affiliation(s)
- Tudor I Oprea
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Cristian Bologa
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Jayme Holmes
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Stephen Mathias
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Vincent T Metzger
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Anna Waller
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Jeremy J Yang
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Andrew R Leach
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Keith J Kelleher
- National Center for Advancing Translational Sciences (NCATS), NIH, Bethesda, MD, USA
| | - Timothy K Sheils
- National Center for Advancing Translational Sciences (NCATS), NIH, Bethesda, MD, USA
| | - Ewy Mathé
- National Center for Advancing Translational Sciences (NCATS), NIH, Bethesda, MD, USA
| | - Sorin Avram
- Coriolan Dragulescu Institute of Chemistry, Timisoara, Romania
| | - Jeremy S Edwards
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA; Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, NM, USA.
| |
Collapse
|
4
|
Wang JY, Xiao WH, Zhang LY, Zhang C, Wei J, Yang JJ, Zhou B, Zhao L, Zhang XL, Xu LY, Hong SD, Dong XS, Liu GL. [Application value of questionnaires in the screening obstructive sleep apnea syndrome in pregnancy across trimesters]. Zhonghua Yi Xue Za Zhi 2023; 103:3932-3937. [PMID: 38129170 DOI: 10.3760/cma.j.cn112137-20230726-00096] [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] [Grants] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Objective: To evaluate the clinical utility value of questionnaires of Berlin, STOP, STOP-Bang (SBQ), Epworth Sleepiness Scale (ESS) in screening obstructive sleep apnea syndrome (OSAS) in pregnant women of different trimesters. Methods: Consecutive pregnant women at high risk for OSAS were enrolled from January, 2021 to April, 2022 at the obstetric clinic of Peking University People's Hospital. They completed questionnaires of Berlin, STOP, SBQ, ESS and also underwent an overnight polysomnography (PSG). To evaluate the accuracy of questionnaires of Berlin, STOP, SBQ, ESS, sensitivity, specificity, positive predictive values, negative predictive values and the area under the receiver operating characteristics (ROC) curve of these questionnaires in pregnancy across trimesters (Pregnancy 1-15 weeks was the first stage, pregnancy 16-27 weeks was the second stage, and pregnancy 28-40 weeks was the third stage) were calculated. Results: A total of 100 pregnant women [(34.5±4.3) years old (26-46 years old)] were included in this study, including 20, 35 and 45 pregnant women in the first, second and third trimester of pregnancy, respectively. Based on PSG results, 45 (45%) of 100 pregnant women were diagnosed with OSAS. The overall predictive values of the four questionnaires were not good, area under[AUC(95%CI)] the ROC curve ESS, Berlin questionnaire STOP and SBQ were 0.54(0.43, 0.66), 0.59 (0.47, 0.70), 0.62(0.51, 0.73) and 0.61 (0.49, 0.72), respectively, sensitivity was 35.6%, 65.9%, 48.9%, 28.9%, specificity was 71.7%, 52.5%, 73.6%, 92.5%. When categorized according to trimesters, the predicted values of the four questionnaires increased in the first trimester, the AUC (95%CI) of STOP questionnaire was 0.81 (0.61, 1.00), sensitivity was 75.0%, specificity was 87.5%. Conclusion: The overall predictive power of the four screening questionnaires is limited in pregnant women. But predictive value of STOP questionnaire is acceptable in the first trimester.
Collapse
Affiliation(s)
- J Y Wang
- Division of Sleep Medicine, Peking University People's Hospital, Beijing 100044, China
| | - W H Xiao
- Department of Respiratory Medicine and Intensive Care Unit, Binzhou Medical University Hospital, Binzhou 256603, China
| | - L Y Zhang
- Department of Obstetrics and Gynecology, Peking University People's Hospital, Beijing 100044, China
| | - C Zhang
- Division of Sleep Medicine, Peking University People's Hospital, Beijing 100044, China
| | - J Wei
- Department of Obstetrics and Gynecology, Peking University People's Hospital, Beijing 100044, China
| | - J J Yang
- Department of Obstetrics and Gynecology, Peking University People's Hospital, Beijing 100044, China
| | - B Zhou
- Division of Sleep Medicine, Peking University People's Hospital, Beijing 100044, China
| | - L Zhao
- Division of Sleep Medicine, Peking University People's Hospital, Beijing 100044, China
| | - X L Zhang
- Division of Sleep Medicine, Peking University People's Hospital, Beijing 100044, China
| | - L Y Xu
- Division of Sleep Medicine, Peking University People's Hospital, Beijing 100044, China
| | - S D Hong
- National Institute of Health Data Science at Peking University, Beijing 100191, China
| | - X S Dong
- Division of Sleep Medicine, Peking University People's Hospital, Beijing 100044, China
| | - G L Liu
- Department of Obstetrics and Gynecology, Peking University People's Hospital, Beijing 100044, China
| |
Collapse
|
5
|
Yang JJ, Chapman M. What are the risks associated with lipiodol hysterosalpingography? A literature review. Radiography (Lond) 2023; 29:1041-1045. [PMID: 37714068 DOI: 10.1016/j.radi.2023.08.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [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: 07/19/2023] [Revised: 08/27/2023] [Accepted: 08/30/2023] [Indexed: 09/17/2023]
Abstract
INTRODUCTION Hysterosalpingography is widely used as a first-line investigation for infertility, and may also be therapeutic, increasing pregnancy rates. Aqueous and oil-based contrast agents can be used. Some studies suggest Lipiodol hysterosalpingography has a greater therapeutic effect on fertility than aqueous contrast, though this is contentious. There are additionally safety concerns surrounding Lipiodol hysterosalpingography. This review summarises the adverse effects associated with Lipiodol hysterosalpingography, particularly on thyroid function. KEY FINDINGS 331 articles were identified. Of these, 46 met inclusion criteria. 3 further articles were identified from reference lists. Complications typically cited in the literature include pain, intravasation, life-threatening oil embolism, and lipogranuloma formation. Emerging evidence suggests that Lipiodol hysterosalpingography may also impact maternal and neonatal thyroid function. Women may develop hypo- or hyperthyroidism. Thyroid dysfunction is clinically significant as even subclinical hypothyroidism reduces fertility, increases the risk of pregnancy complications including miscarriage, pre-eclampsia and perinatal mortality, and adversely impacts foetal neurodevelopment. One study suggested a possible link with neonatal congenital hypothyroidism. CONCLUSION There is emerging evidence to suggest that Lipiodol hysterosalpingography can cause hypo- or hyperthyroidism, in addition to known adverse effects of pain, intravasation, oil embolism, and lipogranuloma formation. IMPLICATIONS FOR PRACTICE Given the significance of these risks, and contention surrounding whether Lipiodol truly increases pregnancy rates compared to aqueous mediums, careful consideration is required in the selection of contrast agent. In particular, Lipiodol hysterosalpingography may not be suitable for women with pre-existing thyroid dysfunction.
Collapse
Affiliation(s)
- J J Yang
- Discipline of Women's Health, Faculty of Medicine, University of New South Wales, Sydney, Australia; Department of Women's and Children's Health, St George Hospital, Sydney, Australia.
| | - M Chapman
- Discipline of Women's Health, Faculty of Medicine, University of New South Wales, Sydney, Australia; Department of Women's and Children's Health, St George Hospital, Sydney, Australia; IVF Australia, St George Private Hospital, Sydney, Australia
| |
Collapse
|
6
|
Bai R, Wang JY, Zhang C, Hong SD, Zhang LY, Wei J, Wang Y, Yang JJ, Dong XS, Han F, Liu GL. [Relationships between hypertensive disorders in pregnancy and obstructive sleep apnea syndrome]. Zhonghua Fu Chan Ke Za Zhi 2023; 58:658-663. [PMID: 37724382 DOI: 10.3760/cma.j.cn112141-20230219-00074] [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: 09/20/2023]
Abstract
Objective: To investigate the impact of obstructive sleep apnea syndrome (OSAS) on pregnancy outcomes, especially the relationship between OSAS and hypertensive disorders in pregnancy (HDP). Methods: A total of 228 pregnant women with high risk of OSAS who underwent sleep monitoring during pregnancy in Peking University People's Hospital from January 2021 to April 2022 were collected by reviewing their medical records for retrospective analysis. According to the diagnosis of OSAS, the pregnant women were divided into OSAS group (105 cases) and non-OSAS group (123 cases). The non-parametric Mann-Whitney U test, χ2 test or Fisher's exact test were used to compare the general data and maternal and fetal outcomes between the two groups, and the occurrence of each type of HDP was further compared. Results: (1) Compared with the non-OSAS group, the median pre-pregnancy body mass index (23.6 vs 27.6 kg/m2) and the proportion of snoring [28.9% (33/114) vs 59.2% (61/103)] in the OSAS group were higher, and the differences were both statistically significant (both P<0.001). (2) The incidence of HDP [67.6% (71/105) vs 39.0% (48/123)] and gestational diabetes mellitus [GDM; 40.0% (42/105) vs 26.8% (33/123)] of pregnant women in the OSAS group were higher than those in the non-OSAS group, and the median delivery week was shorter than that in the non-OSAS group (38.4 vs 39.0 weeks). The differences were all statistically significant (all P<0.05). Between-group differences for the delivery way, postpartum hemorrhage, the rate of intensive care unit admission, preterm birth, small for gestational age infants, neonatal asphyxia, the rate of neonatal intensive care unit admission, newborn birth weight and the proportion of umbilical artery blood pH<7.00 were not statistically significant (all P>0.05). (3) Compared with the non-OSAS group, the incidence of chronic hypertension [11.4% (14/123) vs 22.9% (24/105)] and chronic hypertension with superimposed pre-eclampsia [11.4% (14/123) vs 30.5% (32/105)] were higher in the OSAS group, and the differences were both statistically significant (both P<0.01). Conclusion: OSAS is related to HDP (especially chronic hypertension and chronic hypertension with superimposed pre-eclampsia) and GDM, which could provide a practical basis for the screening, diagnosis and treatment of OSAS in pregnant women at high risk.
Collapse
Affiliation(s)
- R Bai
- Department of Obstetrics and Gynecology, Peking University People's Hospital, Beijing 100044, China
| | - J Y Wang
- Division of Sleep Medicine, Peking University People's Hospital, Beijing 100044, China
| | - C Zhang
- Division of Sleep Medicine, Peking University People's Hospital, Beijing 100044, China
| | - S D Hong
- National Institute of Health Data Science, Peking University, Beijing 100191, China
| | - L Y Zhang
- Department of Obstetrics and Gynecology, Peking University People's Hospital, Beijing 100044, China
| | - J Wei
- Department of Obstetrics and Gynecology, Peking University People's Hospital, Beijing 100044, China
| | - Y Wang
- Department of Obstetrics and Gynecology, Peking University People's Hospital, Beijing 100044, China
| | - J J Yang
- Department of Obstetrics and Gynecology, Peking University People's Hospital, Beijing 100044, China
| | - X S Dong
- Division of Sleep Medicine, Peking University People's Hospital, Beijing 100044, China
| | - F Han
- Division of Sleep Medicine, Peking University People's Hospital, Beijing 100044, China
| | - G L Liu
- Department of Obstetrics and Gynecology, Peking University People's Hospital, Beijing 100044, China
| |
Collapse
|
7
|
Evangelista JE, Clarke DJB, Xie Z, Marino GB, Utti V, Jenkins SL, Ahooyi TM, Bologa CG, Yang JJ, Binder JL, Kumar P, Lambert CG, Grethe JS, Wenger E, Taylor D, Oprea TI, de Bono B, Ma'ayan A. Toxicology knowledge graph for structural birth defects. Commun Med (Lond) 2023; 3:98. [PMID: 37460679 DOI: 10.1038/s43856-023-00329-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 06/29/2023] [Indexed: 07/20/2023] Open
Abstract
BACKGROUND Birth defects are functional and structural abnormalities that impact about 1 in 33 births in the United States. They have been attributed to genetic and other factors such as drugs, cosmetics, food, and environmental pollutants during pregnancy, but for most birth defects there are no known causes. METHODS To further characterize associations between small molecule compounds and their potential to induce specific birth abnormalities, we gathered knowledge from multiple sources to construct a reproductive toxicity Knowledge Graph (ReproTox-KG) with a focus on associations between birth defects, drugs, and genes. Specifically, we gathered data from drug/birth-defect associations from co-mentions in published abstracts, gene/birth-defect associations from genetic studies, drug- and preclinical-compound-induced gene expression changes in cell lines, known drug targets, genetic burden scores for human genes, and placental crossing scores for small molecules. RESULTS Using ReproTox-KG and semi-supervised learning (SSL), we scored >30,000 preclinical small molecules for their potential to cross the placenta and induce birth defects, and identified >500 birth-defect/gene/drug cliques that can be used to explain molecular mechanisms for drug-induced birth defects. The ReproTox-KG can be accessed via a web-based user interface available at https://maayanlab.cloud/reprotox-kg . This site enables users to explore the associations between birth defects, approved and preclinical drugs, and all human genes. CONCLUSIONS ReproTox-KG provides a resource for exploring knowledge about the molecular mechanisms of birth defects with the potential of predicting the likelihood of genes and preclinical small molecules to induce birth defects.
Collapse
Affiliation(s)
- John Erol Evangelista
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Daniel J B Clarke
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Zhuorui Xie
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Giacomo B Marino
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Vivian Utti
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Sherry L Jenkins
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Taha Mohseni Ahooyi
- The Children's Hospital of Philadelphia, Department of Biomedical and Health Informatics; Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Cristian G Bologa
- Department of Internal Medicine, Division of Translational Informatics, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Jeremy J Yang
- Department of Internal Medicine, Division of Translational Informatics, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Jessica L Binder
- Department of Internal Medicine, Division of Translational Informatics, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Praveen Kumar
- Department of Internal Medicine, Division of Translational Informatics, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Christophe G Lambert
- Department of Internal Medicine, Division of Translational Informatics, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Jeffrey S Grethe
- Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Eric Wenger
- The Children's Hospital of Philadelphia, Department of Biomedical and Health Informatics; Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Deanne Taylor
- The Children's Hospital of Philadelphia, Department of Biomedical and Health Informatics; Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Tudor I Oprea
- Department of Internal Medicine, Division of Translational Informatics, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Bernard de Bono
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Avi Ma'ayan
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
| |
Collapse
|
8
|
Yang JJ, Zhang Y, Zhai ZG, Yang YH. [Research progress of thrombolytic therapy for high risk and intermediate-high risk pulmonary thromboembolism]. Zhonghua Jie He He Hu Xi Za Zhi 2023; 46:720-725. [PMID: 37402665 DOI: 10.3760/cma.j.cn112147-20221102-00866] [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: 07/06/2023]
Abstract
Acute pulmonary thromboembolism (PTE) is a highly fatal disease. Fibrinolytic therapy can rapidly improve pulmonary hemodynamics and is an important life-saving treatment. How to screen patients who may benefit from thrombolytic therapy and how to reduce the complications of major bleeding are still the focus of PTE treatment. In addition, as our understanding of post-PE syndrome (PPES) has improved, much attention has been paid to whether thrombolytic therapy has any benefit in preventing PPES. This article reviewed the research progress of early risk stratification and prognosis assessment, early major bleeding risk assessment, thrombolytic drug dose reduction, interventional thrombolysis and the long-term prognosis of PTE thrombolysis in recent years.
Collapse
Affiliation(s)
- J J Yang
- Department of Pulmonary and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing Institute of Respiratory Medicine, Beijing 100020, China
| | - Y Zhang
- Peking University China-Japan Friendship School of Clinical Medicine, Department of Pulmonary and Critical Care Medicine,National Respiratory Disease Center,China-Japan Friendship Hospital,Beijing 100029,China
| | - Z G Zhai
- Peking University China-Japan Friendship School of Clinical Medicine, Department of Pulmonary and Critical Care Medicine,National Respiratory Disease Center,China-Japan Friendship Hospital,Beijing 100029,China
| | - Y H Yang
- Department of Pulmonary and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing Institute of Respiratory Medicine, Beijing 100020, China
| |
Collapse
|
9
|
Kelleher KJ, Sheils TK, Mathias SL, Yang JJ, Metzger V, Siramshetty V, Nguyen DT, Jensen LJ, Vidović D, Schürer S, Holmes J, Sharma K, Pillai A, Bologa C, Edwards J, Mathé E, Oprea T. Pharos 2023: an integrated resource for the understudied human proteome. Nucleic Acids Res 2022; 51:D1405-D1416. [PMID: 36624666 PMCID: PMC9825581 DOI: 10.1093/nar/gkac1033] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/12/2022] [Accepted: 11/28/2022] [Indexed: 11/30/2022] Open
Abstract
The Illuminating the Druggable Genome (IDG) project aims to improve our understanding of understudied proteins and our ability to study them in the context of disease biology by perturbing them with small molecules, biologics, or other therapeutic modalities. Two main products from the IDG effort are the Target Central Resource Database (TCRD) (http://juniper.health.unm.edu/tcrd/), which curates and aggregates information, and Pharos (https://pharos.nih.gov/), a web interface for fusers to extract and visualize data from TCRD. Since the 2021 release, TCRD/Pharos has focused on developing visualization and analysis tools that help reveal higher-level patterns in the underlying data. The current iterations of TCRD and Pharos enable users to perform enrichment calculations based on subsets of targets, diseases, or ligands and to create interactive heat maps and UpSet charts of many types of annotations. Using several examples, we show how to address disease biology and drug discovery questions through enrichment calculations and UpSet charts.
Collapse
Affiliation(s)
- Keith J Kelleher
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Timothy K Sheils
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Stephen L Mathias
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Jeremy J Yang
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Vincent T Metzger
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Vishal B Siramshetty
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Dac-Trung Nguyen
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen 2200, Copenhagen, Denmark
| | - Dušica Vidović
- Institute for Data Science and Computing, University of Miami, Coral Gables, FL 33146, USA,Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Stephan C Schürer
- Institute for Data Science and Computing, University of Miami, Coral Gables, FL 33146, USA,Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA,Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Jayme Holmes
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Karlie R Sharma
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Ajay Pillai
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Cristian G Bologa
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Jeremy S Edwards
- Correspondence may also be addressed to Jeremy Edwards. Tel: +1 505 277 6655;
| | - Ewy A Mathé
- To whom correspondence should be addressed. Tel: +1 301 402 8953;
| | - Tudor I Oprea
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| |
Collapse
|
10
|
Lin JJ, Jiang F, Xiang Y, Wan XR, Feng FZ, Ren T, Yang JJ, Zhao J. [The influence of lung metastasis on prognosis of previously untreated gestational trophoblastic neoplasia patients]. Zhonghua Zhong Liu Za Zhi 2022; 44:1139-1145. [PMID: 36319461 DOI: 10.3760/cma.j.cn112152-20211217-00943] [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] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Objective: To investigate the impact of lung metastases on the prognosis of patients with gestational trophoblastic neoplasia (GTN). Methods: Patients with International Federation of Gynaecology and Obstetrics (FIGO) stage Ⅰ-Ⅲ GTN receiving primary chemotherapy in Peking Union Medical College Hospital between July 2014 and December 2018 were retrospectively analyzed and divided into group 1 with lung metastasis and group 2 without lung metastasis. The baseline characteristics and treatment outcomes of the two groups were compared. The optimal cut-off values of the diameter of largest lung nodule associated with recurrence were identified by receiver operating characteristic (ROC) curves. Logistic regression analyses were performed to identify risk factors for prognosis. Survival analysis was performed by Kaplan-Meier method and Log rank test. Results: Of the 381 GTN patients enrolled (216 with lung metastases and 165 without lung metastases), the pretreatment β human chorionic gonadotrophin [median: 12 572 IU/L (1 832-51 594 IU/L) vs. 5 614 IU/L (559-26 140 IU/L), P=0.001] and FIGO score [median: 3 (1-6) vs. 2 (1-4), P=0.038] were significantly higher in patients with lung metastases than those without lung metastases. In patients with FIGO score≥5, the emergence of resistance (26.76% vs. 10.26%, P=0.036) and median number of chemotherapy courses to achieve complete remission [6 (6-8) vs. 5 (4-6), P<0.001] were significantly higher than patients with lung metastases. In patients with FIGO score 0-4, no significant difference was found in the treatment outcomes between the two groups(P=0.833). Among all patients with lung metastases, the ROC curve showed a sensitivity and specificity of 62.5% and 78.8%, respectively, for predicting recurrence when the length of the largest lung nodule was 1.6 cm, with an area under the curve (AUC) of 0.711 (95% CI: 0.550, 0.871, P=0.044). Multivariate logistic regression analysis suggested a significantly higher recurrence rate when the largest lung nodule was ≥1.6 cm (OR=7.394, 95% CI: 1.003, 54.520, P=0.049). The 1-year disease-free survival rate was significantly lower in patients with the largest lung nodule ≥1.6 cm than in patients with the nodule <1.6 cm (98.2% vs. 82.4%, P=0.001). Conclusions: Lung metastasis is associated with increased first-line chemotherapy resistance in patients with FIGO scores≥5. The diameter of the largest lung metastatic nodule ≥1.6 cm is an effective factor for predicting recurrence.
Collapse
Affiliation(s)
- J J Lin
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Obstetric & Gynecologic Diseases, Beijing 100073, China
| | - F Jiang
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Obstetric & Gynecologic Diseases, Beijing 100073, China
| | - Y Xiang
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Obstetric & Gynecologic Diseases, Beijing 100073, China
| | - X R Wan
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Obstetric & Gynecologic Diseases, Beijing 100073, China
| | - F Z Feng
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Obstetric & Gynecologic Diseases, Beijing 100073, China
| | - T Ren
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Obstetric & Gynecologic Diseases, Beijing 100073, China
| | - J J Yang
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Obstetric & Gynecologic Diseases, Beijing 100073, China
| | - J Zhao
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Obstetric & Gynecologic Diseases, Beijing 100073, China
| |
Collapse
|
11
|
Yang JJ, Chen YD. [Coronary CT angiography derived fractional flow reserve: opportunity for a win-win cooperation between cardiologists and radiologists]. Zhonghua Yi Xue Za Zhi 2022; 102:2575-2577. [PMID: 36058680 DOI: 10.3760/cma.j.cn112137-20220419-00850] [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: 06/15/2023]
Abstract
The advent of coronary CT angiography derived fractional flow reserve (CT-FFR) calculation technology has brought great improvements to the clinical diagnostic process and treatment decision-making towards coronary artery disease. In recent years, CT-FFR technology has gradually begun to be taken into clinical practice in China, however, currently, the popularization is not widespread, and it is imperative to further standardize the clinical application of CT-FFR technology. This paper focused on the opportunities, significance and challenges of CT-FFR application in China from the advantages and disadvantages perspectives of this new technology based on three international studies. Combined with specific national conditions and the latest evidence-based clinical medical results, this paper proposes a win-win cooperation initiative between cardiologists and radiologists for the reference and caution of both clinical practitioners and medical affairs bureaus.
Collapse
Affiliation(s)
- J J Yang
- Senior Department of Cardiology, Sixth Medical Center of PLA General Hospital, Beijing 100037, China
| | - Y D Chen
- Senior Department of Cardiology, Sixth Medical Center of PLA General Hospital, Beijing 100037, China
| |
Collapse
|
12
|
Wang LD, Li X, Song XK, Zhao FY, Zhou RH, Xu ZC, Liu AL, Li JL, Li XZ, Wang LG, Zhang FH, Zhu XM, Li WX, Zhao GZ, Guo WW, Gao XM, Li LX, Wan JW, Ku QX, Xu FG, Zhu AF, Ji HX, Li YL, Ren SL, Zhou PN, Chen QD, Bao SG, Gao HJ, Yang JC, Wei WM, Mao ZZ, Han ZW, Chang YF, Zhou XN, Han WL, Han LL, Lei ZM, Fan R, Wang YZ, Yang JJ, Ji Y, Chen ZJ, Li YF, Hu L, Sun YJ, Chen GL, Bai D, You D. [Clinical characteristics of 272 437 patients with different histopathological subtypes of primary esophageal malignant tumors]. Zhonghua Nei Ke Za Zhi 2022; 61:1023-1030. [PMID: 36008295 DOI: 10.3760/cma.j.cn112138-20210929-00668] [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: 06/15/2023]
Abstract
Objective: To characterize the histopathological subtypes and their clinicopathological parameters of gender and onset age by common, rare and sparse primary esophageal malignant tumors (PEMT). Methods: A total of 272 437 patients with PEMT were enrolled in this study, and all of the patients were received radical surgery. The clinicopathological information of the patients was obtained from the database established by the State Key Laboratory of Esophageal Cancer Prevention & Treatment from September 1973 to December 2020, which included the clinical treatment, pathological diagnosis and follow-up information of esophagus and gastric cardia cancers. All patients were diagnosed and classified by the criteria of esophageal tumor histopathological diagnosis and classification (2019) of the World Health Organization (WHO). The esophageal tumors, which were not included in the WHO classification, were analyzed separately according to the postoperative pathological diagnosis. The χ2 test was performed by the SPSS 25.0 software on count data, and the test standard α=0.05. Results: A total of 32 histopathological types were identified in the enrolled PEMT patients, of which 10 subtypes were not included in the WHO classification. According to the frequency, PEMT were divided into common (esophageal squamous cell carcinoma, ESCC, accounting for 97.1%), rare (esophageal adenocarcinoma, EAC, accounting for 2.3%) and sparse (mainly esophageal small cell carcinoma, malignant melanoma, etc., accounting for 0.6%). All the common, rare, and sparse types occurred predominantly in male patients, and the gender difference of rare type was most significant (EAC, male∶ female, 2.67∶1), followed with common type (ESCC, male∶ female, 1.78∶1) and sparse type (male∶ female, 1.71∶1). The common type (ESCC) mainly occurred in the middle thoracic segment (65.2%), while the rare type (EAC) mainly occurred in the lower thoracic segment (56.8%). Among the sparse type, malignant melanoma and malignant fibrous histiocytoma were both predominantly located in the lower thoracic segment (51.7%, 66.7%), and the others were mainly in the middle thoracic segment. Conclusion: ESCC is the most common type among the 32 histopathological types of PEMT, followed by EAC as the rare type, and esophageal small cell carcinoma and malignant melanoma as the major sparse type, and all of which are mainly occur in male patients. The common type of ESCC mainly occur in the middle thoracic segment, while the rare type of EAC mainly in the lower thoracic segment. The mainly sparse type of malignant melanoma and malignant fibrous histiocytoma predominately occur in the lower thoracic segment, and the remaining sparse types mainly occur in the middle thoracic segment.
Collapse
Affiliation(s)
- L D Wang
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, China
| | - X Li
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, China Department of Pathology and Pathophysiology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - X K Song
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, China
| | - F Y Zhao
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, China
| | - R H Zhou
- Department of Thoracic Surgery, Anyang Tumor Hospital, Anyang 455000, China
| | - Z C Xu
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, China
| | - A L Liu
- Department of Oncology, Linzhou Tumor Hospital, Linzhou 456550, China
| | - J L Li
- Department of Oncology, Linzhou Tumor Hospital, Linzhou 456550, China
| | - X Z Li
- Department of Pathology, Linzhou Esophageal Cancer Hospital, Linzhou 456592, China
| | - L G Wang
- Department of Oncology, Linzhou People's Hospital, Linzhou 456550, China
| | - F H Zhang
- Department of Thoracic Surgery, Xinxiang Central Hospital, Xinxiang 453000, China
| | - X M Zhu
- Department of Pathology, Xinxiang Central Hospital, Xinxiang 453000, China
| | - W X Li
- Department of Pathology, Cixian People's Hospital, Handan 056599, China
| | - G Z Zhao
- Department of Pathology, the First Affiliated Hospital of Xinxiang Medicine University, Xinxiang 453100, China
| | - W W Guo
- Department of Oncology, Linzhou Tumor Hospital, Linzhou 456550, China
| | - X M Gao
- Department of Oncology, Linzhou People's Hospital, Linzhou 456550, China
| | - L X Li
- Xinxiang Key Laboratory for Molecular Therapy of Cancer, Xinxiang Medical University, Xinxiang 453003, China
| | - J W Wan
- Department of Oncology, Nanyang Central Hospital, Nanyang 473009, China
| | - Q X Ku
- Department of Endoscopy, the Second Affiliated Hospital of Nanyang Medical College, Nanyang 473000, China
| | - F G Xu
- Department of Oncology, the First People's Hospital of Nanyang, Nanyang 473002, China
| | - A F Zhu
- Department of Oncology, the First People's Hospital of Shangqiu, Shangqiu 476000, China
| | - H X Ji
- Department of Clinical Laboratory, the Affiliated Heping Hospital of Changzhi Medical College, Changzhi 046000, China
| | - Y L Li
- Department of Pathology, the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450003, China
| | - S L Ren
- Department of Pathology, the Second Affiliated Hospital, Zhengzhou University, Zhengzhou 450003, China
| | - P N Zhou
- Department of Pathology, Henan People's Hospital, Zhengzhou 450003, China
| | - Q D Chen
- Department of Thoracic Surgery, Henan Tumor Hospital, Zhengzhou 450003, China
| | - S G Bao
- Department of Oncology, Anyang District Hospital, Anyang 455002, China
| | - H J Gao
- Department of Oncology, the First Affiliated Hospital, Henan University of Science and Technology, Luoyang 471003, China
| | - J C Yang
- Department of Pathology, Anyang Tumor Hospital, Anyang 455000, China
| | - W M Wei
- Department of Thoracic Surgery, Linzhou Esophageal Cancer Hospital, Linzhou 456592, China
| | - Z Z Mao
- Department of Thoracic Surgery, Cancer Hospital of the University of Chinese Academy of Sciences, Hangzhou 310005, China
| | - Z W Han
- Department of Pathology, Zhenping County People's Hospital, Nanyang 474250, China
| | - Y F Chang
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, China
| | - X N Zhou
- Department of Gastroenterology, the Second Affiliated Hospital, Zhengzhou University, Zhengzhou 450003, China
| | - W L Han
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, China
| | - L L Han
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, China
| | - Z M Lei
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, China
| | - R Fan
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, China
| | - Y Z Wang
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, China
| | - J J Yang
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, China
| | - Y Ji
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, China
| | - Z J Chen
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, China
| | - Y F Li
- Department of Gastroenterology, the Third People's Hospital of Huixian, Huixian 453600, China
| | - L Hu
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, China
| | - Y J Sun
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, China Department of Pathology and Pathophysiology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - G L Chen
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, China Department of Pathology and Pathophysiology, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - D Bai
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, China
| | - Duo You
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou 450052, China
| |
Collapse
|
13
|
Yang Z, Yang JJ, Zhu PJ, Han HM, Wan XL, Yang HM, Wang ZY. Effects of betaine on growth performance, intestinal health, and immune response of goslings challenged with lipopolysaccharide. Poult Sci 2022; 101:102153. [PMID: 36179650 PMCID: PMC9523388 DOI: 10.1016/j.psj.2022.102153] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 08/09/2022] [Accepted: 08/15/2022] [Indexed: 11/24/2022] Open
Abstract
The objective of this experiment was to investigate the effects of betaine on growth performance, serum parameters, intestinal health, and immune performance of goslings in response to lipopolysaccharide (LPS) challenge. A total of 168 healthy male 15-day-old Jiangnan White Goslings were randomly divided into 4 groups, with 6 replicates per treatment and seven goslings per replicate. A 2 × 2 factorial arrangement included 2 factors, that is, LPS challenge (injection of LPS or physiological saline) and betaine (added 0 or 0.06% betaine in diet). The results indicated that LPS challenge significantly reduced the average daily feed intake (ADFI), average daily gain (ADG), and body weight (BW) at 21 D of the goslings, while dietary betaine supplementation tended to increase the ADFI during the LPS stress period (P = 0.08) and BW at 21 D of the goslings (P = 0.09). The LPS-challenged goslings showed higher pro-inflammatory cytokines (interleukin-1 [IL-1β], interleukin-6 [IL-6], tumor necrosis factor-α (TNF-α), and Interferon-gamma [IFN-γ]) and lower anti-inflammatory cytokine (Interleukin-10 [IL-10]) (P < 0.05) at 21 D of age. Dietary betaine supplementation alleviated LPS-induced increase in pro-inflammatory cytokines. The LPS challenge significantly decreased duodenal and jejunal villus height (VH) and villus height and crypt depth ratio (VCR), while the addition of betaine significantly increased duodenal VH and VCR (P < 0.05). On the other hand, addition of betaine significantly alleviated decline of enzyme activity on lipase, amylase, trypsin, and chymotrypsin in the intestinal of goslings. The LPS challenge significantly increased the content of serum D-lactic acid (D-LA) and the activity of diamine oxidase (DAO) at 21 D of the goslings. The LPS challenge and betaine addition significantly increased the mRNA expression of Occcludin (OCLN) in jejunal mucosa at 28 D of the goslings (P < 0.05). In conclusion, our research demonstrated that betaine can alleviate the decline of growth performance and immune performance in goslings caused by LPS. The results also indicate betaine possesses anti-inflammation properties and improves intestinal barrier functions. We recommend that 0.06% betaine be added into the diet to improve the intestinal health and immune performance of goslings.
Collapse
Affiliation(s)
- Z Yang
- Joint International Research Laboratory of Agriculture and Agri-Product Safety of Ministry of Education of China, Yangzhou University, Yangzhou, Jiangsu Province, 225009, P. R. China
| | - J J Yang
- Crown Bioscience (Taicang) Co., Ltd, Suzhou, Jiangsu Province, 225009, P. R. China
| | - P J Zhu
- Jiangsu Lihua Animal Husbandry Co,. Ltd, Chongqing, Jiangsu Province, 225009, P. R. China
| | - H M Han
- Jiangsu Lihua Animal Husbandry Co,. Ltd, Chongqing, Jiangsu Province, 225009, P. R. China
| | - X L Wan
- College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu Province, 225009, P. R. China
| | - H M Yang
- College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu Province, 225009, P. R. China
| | - Z Y Wang
- Joint International Research Laboratory of Agriculture and Agri-Product Safety of Ministry of Education of China, Yangzhou University, Yangzhou, Jiangsu Province, 225009, P. R. China; College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu Province, 225009, P. R. China.
| |
Collapse
|
14
|
Zhao H, Luo D, Yang JJ, Yuan MJ, Liu L, Yu WH. [Clinical effect and analysis of exercise treatment for temporomandibular joint osteoarthritis]. Zhonghua Kou Qiang Yi Xue Za Zhi 2022; 57:701-707. [PMID: 35790509 DOI: 10.3760/cma.j.cn112144-20220314-00109] [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: 06/15/2023]
Abstract
Objective: To explore the clinical efficacy of (stomatognathic system functional exercise(SSFE) in the treatment of temporomandibular joint osteoarthritis (TMJOA), and to provide reference for the clinical treatment of TMJOA. Methods: Choose between January 2020 and June 2020 in the Affiliated Hospital of Qingdao University of Temporomandibular Disorder (TMD) Clinics, pain in the temporomandibular joint (TMJ), limited openings for complained of symptoms of TMD patients, diagnosed by clinical examination and cone beam CT (CBCT) examination of TMJOA patients 60 patients (64 joints), including 20 males and 45 females, the age was (42.6±2.5) years (33-47 years old). The patients were randomly divided into the experimental group (30 cases, 34 joints) and the control group (30 cases, 30 joints) according to the odd and even numbers of their treatment numbers. The experimental group was treated with SSFE method. The control group was treated with maxillary full dentition occlusal splint. Visual analogue score (VAS), natural mouth opening and maximal mouth opening (MMO) of each patient in each group were recorded at initial diagnosis, 2 weeks, 3 and 6 months after treatment, and CBCT imaging was compared for the changes of condylar bone at initial diagnosis, 3 and 6 months after treatment. Results: VAS values of the experimental groups were (2.90±1.42), (0.90±0.37), (0.87±0.23) at 2 weeks, 3 and 6 months after treatment, respectively. The VAS values of the control group were (4.57±1.94), (4.17±2.09), (3.73±2.21), respectively. The VAS score of the experimental group was significantly lower than that of the control group (F=42.93, P<0.001). Before SSFE treatment, all the patients in the experimental group had different degrees of restricted opening and characteristic abnormal opening and closing pattern. Two weeks after SSFE treatment, the opening degree of the patients was (37.69±2.4)mm, the opening shape "↓" and the closing shape "↑" were normal. At 3 and 6 months after treatment, the oral opening was (38.98±1.08) mm and (39.73±1.76) mm, respectively. The opening degree of control group was (36.85±2.33) mm 2 weeks after treatment, and the characteristic abnormal opening and closing pattern still existed. The opening degree of control group was (37.82±1.85) mm and (37.40±1.75) mm 3 and 6 months after treatment, respectively. The characteristic abnormal opening and closing pattern (stuffy, awkward, deliberate, unnatural) did not improve significantly. The openness of the experimental group was significantly higher than that of the control group (F=25.20, P<0.001). In the experimental group, 82.4% (28/34) had benign remodeling of condylar bone 6 months after treatment, and 17.6% (6/34) had no change of condylar bone. There was no significant change in condylar bone in control group. CBCT scores of the experimental group were (2.43±1.74) and (1.70±1.26) at 3 and 6 months after treatment, respectively. CBCT scores of the control group at 3 and 6 months after treatment were (4.23±1.50) and (4.10±1.37), they were significantly lower in the experimental group than in the control group (F=27.20, P<0.001). Conclusions: Full dentition occlusal splint can alleviate the pain in the joint area of TMJOA patients, but can not improve the characteristic abnormal mandibular movement, and the condyle bone repair is not obvious. SSFE can effectively relieve the symptoms and signs of TMJOA patients, especially improve the abnormal characteristic mandibular movement, and promote the normal reconstruction and repair of condylar bone.
Collapse
Affiliation(s)
- H Zhao
- Department of Oral and Maxillofacial Surgery, The Affiliated Hospital of Qingdao University, Qingdao 266003, China
| | - D Luo
- Department of Oral and Maxillofacial Surgery, The Affiliated Hospital of Qingdao University, Qingdao 266003, China
| | - J J Yang
- Department of Oral and Maxillofacial Surgery, The Affiliated Hospital of Qingdao University, Qingdao 266003, China
| | - M J Yuan
- Department of Oral and Maxillofacial Surgery, The Affiliated Hospital of Qingdao University, Qingdao 266003, China
| | - L Liu
- Department of Oral and Maxillofacial Surgery, The Affiliated Hospital of Qingdao University, Qingdao 266003, China
| | - W H Yu
- Department of Oral and Maxillofacial Surgery, The Affiliated Hospital of Qingdao University, Qingdao 266003, China
| |
Collapse
|
15
|
Kouli O, Murray V, Bhatia S, Cambridge WA, Kawka M, Shafi S, Knight SR, Kamarajah SK, McLean KA, Glasbey JC, Khaw RA, Ahmed W, Akhbari M, Baker D, Borakati A, Mills E, Thavayogan R, Yasin I, Raubenheimer K, Ridley W, Sarrami M, Zhang G, Egoroff N, Pockney P, Richards T, Bhangu A, Creagh-Brown B, Edwards M, Harrison EM, Lee M, Nepogodiev D, Pinkney T, Pearse R, Smart N, Vohra R, Sohrabi C, Jamieson A, Nguyen M, Rahman A, English C, Tincknell L, Kakodkar P, Kwek I, Punjabi N, Burns J, Varghese S, Erotocritou M, McGuckin S, Vayalapra S, Dominguez E, Moneim J, Salehi M, Tan HL, Yoong A, Zhu L, Seale B, Nowinka Z, Patel N, Chrisp B, Harris J, Maleyko I, Muneeb F, Gough M, James CE, Skan O, Chowdhury A, Rebuffa N, Khan H, Down B, Fatimah Hussain Q, Adams M, Bailey A, Cullen G, Fu YXJ, McClement B, Taylor A, Aitken S, Bachelet B, Brousse de Gersigny J, Chang C, Khehra B, Lahoud N, Lee Solano M, Louca M, Rozenbroek P, Rozitis E, Agbinya N, Anderson E, Arwi G, Barry I, Batchelor C, Chong T, Choo LY, Clark L, Daniels M, Goh J, Handa A, Hanna J, Huynh L, Jeon A, Kanbour A, Lee A, Lee J, Lee T, Leigh J, Ly D, McGregor F, Moss J, Nejatian M, O'Loughlin E, Ramos I, Sanchez B, Shrivathsa A, Sincari A, Sobhi S, Swart R, Trimboli J, Wignall P, Bourke E, Chong A, Clayton S, Dawson A, Hardy E, Iqbal R, Le L, Mao S, Marinelli I, Metcalfe H, Panicker D, R HH, Ridgway S, Tan HH, Thong S, Van M, Woon S, Woon-Shoo-Tong XS, Yu S, Ali K, Chee J, Chiu C, Chow YW, Duller A, Nagappan P, Ng S, Selvanathan M, Sheridan C, Temple M, Do JE, Dudi-Venkata NN, Humphries E, Li L, Mansour LT, Massy-Westropp C, Fang B, Farbood K, Hong H, Huang Y, Joan M, Koh C, Liu YHA, Mahajan T, Muller E, Park R, Tanudisastro M, Wu JJG, Chopra P, Giang S, Radcliffe S, Thach P, Wallace D, Wilkes A, Chinta SH, Li J, Phan J, Rahman F, Segaran A, Shannon J, Zhang M, Adams N, Bonte A, Choudhry A, Colterjohn N, Croyle JA, Donohue J, Feighery A, Keane A, McNamara D, Munir K, Roche D, Sabnani R, Seligman D, Sharma S, Stickney Z, Suchy H, Tan R, Yordi S, Ahmed I, Aranha M, El Sabawy D, Garwood P, Harnett M, Holohan R, Howard R, Kayyal Y, Krakoski N, Lupo M, McGilberry W, Nepon H, Scoleri Y, Urbina C, Ahmad Fuad MF, Ahmed O, Jaswantlal D, Kelly E, Khan MHT, Naidu D, Neo WX, O'Neill R, Sugrue M, Abbas JD, Abdul-Fattah S, Azlan A, Barry K, Idris NS, Kaka N, Mc Dermott D, Mohammad Nasir MN, Mozo M, Rehal A, Shaikh Yousef M, Wong RH, Curran E, Gardner M, Hogan A, Julka R, Lasser G, Ní Chorráin N, Ting J, Browne R, George S, Janjua Z, Leung Shing V, Megally M, Murphy S, Ravenscroft L, Vedadi A, Vyas V, Bryan A, Sheikh A, Ubhi J, Vannelli K, Vawda A, Adeusi L, Doherty C, Fitzgerald C, Gallagher H, Gill P, Hamza H, Hogan M, Kelly S, Larry J, Lynch P, Mazeni NA, O'Connell R, O'Loghlin R, Singh K, Abbas Syed R, Ali A, Alkandari B, Arnold A, Arora E, Azam R, Breathnach C, Cheema J, Compton M, Curran S, Elliott JA, Jayasamraj O, Mohammed N, Noone A, Pal A, Pandey S, Quinn P, Sheridan R, Siew L, Tan EP, Tio SW, Toh VTR, Walsh M, Yap C, Yassa J, Young T, Agarwal N, Almoosawy SA, Bowen K, Bruce D, Connachan R, Cook A, Daniell A, Elliott M, Fung HKF, Irving A, Laurie S, Lee YJ, Lim ZX, Maddineni S, McClenaghan RE, Muthuganesan V, Ravichandran P, Roberts N, Shaji S, Solt S, Toshney E, Arnold C, Baker O, Belais F, Bojanic C, Byrne M, Chau CYC, De Soysa S, Eldridge M, Fairey M, Fearnhead N, Guéroult A, Ho JSY, Joshi K, Kadiyala N, Khalid S, Khan F, Kumar K, Lewis E, Magee J, Manetta-Jones D, Mann S, McKeown L, Mitrofan C, Mohamed T, Monnickendam A, Ng AYKC, Ortu A, Patel M, Pope T, Pressling S, Purohit K, Saji S, Shah Foridi J, Shah R, Siddiqui SS, Surman K, Utukuri M, Varghese A, Williams CYK, Yang JJ, Billson E, Cheah E, Holmes P, Hussain S, Murdock D, Nicholls A, Patel P, Ramana G, Saleki M, Spence H, Thomas D, Yu C, Abousamra M, Brown C, Conti I, Donnelly A, Durand M, French N, Goan R, O'Kane E, Rubinchik P, Gardiner H, Kempf B, Lai YL, Matthews H, Minford E, Rafferty C, Reid C, Sheridan N, Al Bahri T, Bhoombla N, Rao BM, Titu L, Chatha S, Field C, Gandhi T, Gulati R, Jha R, Jones Sam MT, Karim S, Patel R, Saunders M, Sharma K, Abid S, Heath E, Kurup D, Patel A, Ali M, Cresswell B, Felstead D, Jennings K, Kaluarachchi T, Lazzereschi L, Mayson H, Miah JE, Reinders B, Rosser A, Thomas C, Williams H, Al-Hamid Z, Alsadoun L, Chlubek M, Fernando P, Gaunt E, Gercek Y, Maniar R, Ma R, Matson M, Moore S, Morris A, Nagappan PG, Ratnayake M, Rockall L, Shallcross O, Sinha A, Tan KE, Virdee S, Wenlock R, Donnelly HA, Ghazal R, Hughes I, Liu X, McFadden M, Misbert E, Mogey P, O'Hara A, Peace C, Rainey C, Raja P, Salem M, Salmon J, Tan CH, Alves D, Bahl S, Baker C, Coulthurst J, Koysombat K, Linn T, Rai P, Sharma A, Shergill A, Ahmed M, Ahmed S, Belk LH, Choudhry H, Cummings D, Dixon Y, Dobinson C, Edwards J, Flint J, Franco Da Silva C, Gallie R, Gardener M, Glover T, Greasley M, Hatab A, Howells R, Hussey T, Khan A, Mann A, Morrison H, Ng A, Osmond R, Padmakumar N, Pervaiz F, Prince R, Qureshi A, Sawhney R, Sigurdson B, Stephenson L, Vora K, Zacken A, Cope P, Di Traglia R, Ferarrio I, Hackett N, Healicon R, Horseman L, Lam LI, Meerdink M, Menham D, Murphy R, Nimmo I, Ramaesh A, Rees J, Soame R, Dilaver N, Adebambo D, Brown E, Burt J, Foster K, Kaliyappan L, Knight P, Politis A, Richardson E, Townsend J, Abdi M, Ball M, Easby S, Gill N, Ho E, Iqbal H, Matthews M, Nubi S, Nwokocha JO, Okafor I, Perry G, Sinartio B, Vanukuru N, Walkley D, Welch T, Yates J, Yeshitila N, Bryans K, Campbell B, Gray C, Keys R, Macartney M, Chamberlain G, Khatri A, Kucheria A, Lee STP, Reese G, Roy choudhury J, Tan WYR, Teh JJ, Ting A, Kazi S, Kontovounisios C, Vutipongsatorn K, Amarnath T, Balasubramanian N, Bassett E, Gurung P, Lim J, Panjikkaran A, Sanalla A, Alkoot M, Bacigalupo V, Eardley N, Horton M, Hurry A, Isti C, Maskell P, Nursiah K, Punn G, Salih H, Epanomeritakis E, Foulkes A, Henderson R, Johnston E, McCullough H, McLarnon M, Morrison E, Cheung A, Cho SH, Eriksson F, Hedges J, Low Z, May C, Musto L, Nagi S, Nur S, Salau E, Shabbir S, Thomas MC, Uthayanan L, Vig S, Zaheer M, Zeng G, Ashcroft-Quinn S, Brown R, Hayes J, McConville R, French R, Gilliam A, Sheetal S, Shehzad MU, Bani W, Christie I, Franklyn J, Khan M, Russell J, Smolarek S, Varadarassou R, Ahmed SK, Narayanaswamy S, Sealy J, Shah M, Dodhia V, Manukyan A, O'Hare R, Orbell J, Chung I, Forenc K, Gupta A, Agarwal A, Al Dabbagh A, Bennewith R, Bottomley J, Chu TSM, Chu YYA, Doherty W, Evans B, Hainsworth P, Hosfield T, Li CH, McCullagh I, Mehta A, Thaker A, Thompson B, Virdi A, Walker H, Wilkins E, Dixon C, Hassan MR, Lotca N, Tong KS, Batchelor-Parry H, Chaudhari S, Harris T, Hooper J, Johnson C, Mulvihill C, Nayler J, Olutobi O, Piramanayagam B, Stones K, Sussman M, Weaver C, Alam F, Al Rawi M, Andrew F, Arrayeh A, Azizan N, Hassan A, Iqbal Z, John I, Jones M, Kalake O, Keast M, Nicholas J, Patil A, Powell K, Roberts P, Sabri A, Segue AK, Shah A, Shaik Mohamed SA, Shehadeh A, Shenoy S, Tong A, Upcott M, Vijayasingam D, Anarfi S, Dauncey J, Devindaran A, Havalda P, Komninos G, Mwendwa E, Norman C, Richards J, Urquhart A, Allan J, Cahya E, Hunt H, McWhirter C, Norton R, Roxburgh C, Tan JY, Ali Butt S, Hansdot S, Haq I, Mootien A, Sanchez I, Vainas T, Deliyannis E, Tan M, Vipond M, Chittoor Satish NN, Dattani A, De Carvalho L, Gaston-Grubb M, Karunanithy L, Lowe B, Pace C, Raju K, Roope J, Taylor C, Youssef H, Munro T, Thorn C, Wong KHF, Yunus A, Chawla S, Datta A, Dinesh AA, Field D, Georgi T, Gwozdz A, Hamstead E, Howard N, Isleyen N, Jackson N, Kingdon J, Sagoo KS, Schizas A, Yin L, Aung E, Aung YY, Franklin S, Han SM, Kim WC, Martin Segura A, Rossi M, Ross T, Tirimanna R, Wang B, Zakieh O, Ben-Arzi H, Flach A, Jackson E, Magers S, Olu abara C, Rogers E, Sugden K, Tan H, Veliah S, Walton U, Asif A, Bharwada Y, Bowley D, Broekhuizen A, Cooper L, Evans N, Girdlestone H, Ling C, Mann H, Mehmood N, Mulvenna CL, Rainer N, Trout I, Gujjuri R, Jeyaraman D, Leong E, Singh D, Smith E, Anderton J, Barabas M, Goyal S, Howard D, Joshi A, Mitchell D, Weatherby T, Badminton R, Bird R, Burtle D, Choi NY, Devalia K, Farr E, Fischer F, Fish J, Gunn F, Jacobs D, Johnston P, Kalakoutas A, Lau E, Loo YNAF, Louden H, Makariou N, Mohammadi K, Nayab Y, Ruhomaun S, Ryliskyte R, Saeed M, Shinde P, Sudul M, Theodoropoulou K, Valadao-Spoorenberg J, Vlachou F, Arshad SR, Janmohamed AM, Noor M, Oyerinde O, Saha A, Syed Y, Watkinson W, Ahmadi H, Akintunde A, Alsaady A, Bradley J, Brothwood D, Burton M, Higgs M, Hoyle C, Katsura C, Lathan R, Louani A, Mandalia R, Prihartadi AS, Qaddoura B, Sandland-Taylor L, Thadani S, Thompson A, Walshaw J, Teo S, Ali S, Bawa JH, Fox S, Gargan K, Haider SA, Hanna N, Hatoum A, Khan Z, Krzak AM, Li T, Pitt J, Tan GJS, Ullah Z, Wilson E, Cleaver J, Colman J, Copeland L, Coulson A, Davis P, Faisal H, Hassan F, Hughes JT, Jabr Y, Mahmoud Ali F, Nahaboo Solim ZN, Sangheli A, Shaya S, Thompson R, Cornwall H, De Andres Crespo M, Fay E, Findlay J, Groves E, Jones O, Killen A, Millo J, Thomas S, Ward J, Wilkins M, Zaki F, Zilber E, Bhavra K, Bilolikar A, Charalambous M, Elawad A, Eleni A, Fawdon R, Gibbins A, Livingstone D, Mala D, Oke SE, Padmakumar D, Patsalides MA, Payne D, Ralphs C, Roney A, Sardar N, Stefanova K, Surti F, Timms R, Tosney G, Bannister J, Clement NS, Cullimore V, Kamal F, Lendor J, McKay J, Mcswiggan J, Minhas N, Seneviratne K, Simeen S, Valverde J, Watson N, Bloom I, Dinh TH, Hirniak J, Joseph R, Kansagra M, Lai CKN, Melamed N, Patel J, Randev J, Sedighi T, Shurovi B, Sodhi J, Vadgama N, Abdulla S, Adabavazeh B, Champion A, Chennupati R, Chu K, Devi S, Haji A, Schulz J, Testa F, Davies P, Gurung B, Howell S, Modi P, Pervaiz A, Zahid M, Abdolrazaghi S, Abi Aoun R, Anjum Z, Bawa G, Bhardwaj R, Brown S, Enver M, Gill D, Gopikrishna D, Gurung D, Kanwal A, Kaushal P, Khanna A, Lovell E, McEvoy C, Mirza M, Nabeel S, Naseem S, Pandya K, Perkins R, Pulakal R, Ray M, Reay C, Reilly S, Round A, Seehra J, Shakeel NM, Singh B, Vijay Sukhnani M, Brown L, Desai B, Elzanati H, Godhaniya J, Kavanagh E, Kent J, Kishor A, Liu A, Norwood M, Shaari N, Wood C, Wood M, Brown A, Chellapuri A, Ferriman A, Ghosh I, Kulkarni N, Noton T, Pinto A, Rajesh S, Varghese B, Wenban C, Aly R, Barciela C, Brookes T, Corrin E, Goldsworthy M, Mohamed Azhar MS, Moore J, Nakhuda S, Ng D, Pillay S, Port S, Abdullah M, Akinyemi J, Islam S, Kale A, Lewis A, Manjunath T, McCabe H, Misra S, Stubley T, Tam JP, Waraich N, Chaora T, Ford C, Osinkolu I, Pong G, Rai J, Risquet R, Ainsworth J, Ayandokun P, Barham E, Barrett G, Barry J, Bisson E, Bridges I, Burke D, Cann J, Cloney M, Coates S, Cripps P, Davies C, Francis N, Green S, Handley G, Hathaway D, Hurt L, Jenkins S, Johnston C, Khadka A, McGee U, Morris D, Murray R, Norbury C, Pierrepont Z, Richards C, Ross O, Ruddy A, Salmon C, Shield M, Soanes K, Spencer N, Taverner S, Williams C, Wills-Wood W, Woodward S, Chow J, Fan J, Guest O, Hunter I, Moon WY, Arthur-Quarm S, Edwards P, Hamlyn V, McEneaney L, N D G, Pranoy S, Ting M, Abada S, Alawattegama LH, Ashok A, Carey C, Gogna A, Haglund C, Hurley P, Leelo N, Liu B, Mannan F, Paramjothy K, Ramlogan K, Raymond-Hayling O, Shanmugarajah A, Solichan D, Wilkinson B, Ahmad NA, Allan D, Amin A, Bakina C, Burns F, Cameron F, Campbell A, Cavanagh S, Chan SMZ, Chapman S, Chong V, Edelsten E, Ekpete O, El Sheikh M, Ghose R, Hassane A, Henderson C, Hilton-Christie S, Husain M, Hussain H, Javid Z, Johnson-Ogbuneke J, Johnston A, Khalil M, Leung TCC, Makin I, Muralidharan V, Naeem M, Patil P, Ravichandran S, Saraeva D, Shankey-Smith W, Sharma N, Swan R, Waudby-West R, Wilkinson A, Wright K, Balasubramanian A, Bhatti S, Chalkley M, Chou WK, Dixon M, Evans L, Fisher K, Gandhi P, Ho S, Lau YB, Lowe S, Meechan C, Murali N, Musonda C, Njoku P, Ochieng L, Pervez MU, Seebah K, Shaikh I, Sikder MA, Vanker R, Alom J, Bajaj V, Coleman O, Finch G, Goss J, Jenkins C, Kontothanassis A, Liew MS, Ng K, Outram M, Shakeel MM, Tawn J, Zuhairy S, Chapple K, Cinnamond A, Coleman S, George HA, Goulder L, Hare N, Hawksley J, Kret A, Luesley A, Mecia L, Porter H, Puddy E, Richardson G, Sohail B, Srikaran V, Tadross D, Tobin J, Tokidis E, Young L, Ashdown T, Bratsos S, Koomson A, Kufuor A, Lim MQ, Shah S, Thorne EPC, Warusavitarne J, Xu S, Abigail S, Ahmed A, Ahmed J, Akmal A, Al-Khafaji M, Amini B, Arshad M, Bogie E, Brazkiewicz M, Carroll M, Chandegra A, Cirelli C, Deng A, Fairclough S, Fung YJ, Gornell C, Green RL, Green SV, Gulamhussein AHM, Isaac AG, Jan R, Jegatheeswaran L, Knee M, Kotecha J, Kotecha S, Maxwell-Armstrong C, McIntyre C, Mendis N, Naing TKP, Oberman J, Ong ZX, Ramalingam A, Saeed Adam A, Tan LL, Towell S, Yadav J, Anandampillai R, Chung S, Hounat A, Ibrahim B, Jeyakumar G, Khalil A, Khan UA, Nair G, Owusu-Ayim M, Wilson M, Kanani A, Kilkelly B, Ogunmwonyi I, Ong L, Samra B, Schomerus L, Shea J, Turner O, Yang Y, Amin M, Blott N, Clark A, Feather A, Forrest M, Hague S, Hamilton K, Higginbotham G, Hope E, Karimian S, Loveday K, Malik H, McKenna O, Noor A, Onsiong C, Patel B, Radcliffe N, Shah P, Tye L, Verma K, Walford R, Yusufi U, Zachariah M, Casey A, Doré C, Fludder V, Fortescue L, Kalapu SS, Karel E, Khera G, Smith C, Appleton B, Ashaye A, Boggon E, Evans A, Faris Mahmood H, Hinchcliffe Z, Marei O, Silva I, Spooner C, Thomas G, Timlin M, Wellington J, Yao SL, Abdelrazek M, Abdelrazik Y, Bee F, Joseph A, Mounce A, Parry G, Vignarajah N, Biddles D, Creissen A, Kolhe S, K T, Lea A, Ledda V, O'Loughlin P, Scanlon J, Shetty N, Weller C, Abdalla M, Adeoye A, Bhatti M, Chadda KR, Chu J, Elhakim H, Foster-Davies H, Rabie M, Tailor B, Webb S, Abdelrahim ASA, Choo SY, Jiwa A, Mangam S, Murray S, Shandramohan A, Aghanenu O, Budd W, Hayre J, Khanom S, Liew ZY, McKinney R, Moody N, Muhammad-Kamal H, Odogwu J, Patel D, Roy C, Sattar Z, Shahrokhi N, Sinha I, Thomson E, Wonga L, Bain J, Khan J, Ricardo D, Bevis R, Cherry C, Darkwa S, Drew W, Griffiths E, Konda N, Madani D, Mak JKC, Meda B, Odunukwe U, Preest G, Raheel F, Rajaseharan A, Ramgopal A, Risbrooke C, Selvaratnam K, Sethunath G, Tabassum R, Taylor J, Thakker A, Wijesingha N, Wybrew R, Yasin T, Ahmed Osman A, Alfadhel S, Carberry E, Chen JY, Drake I, Glen P, Jayasuriya N, Kawar L, Myatt R, Sinan LOH, Siu SSY, Tjen V, Adeboyejo O, Bacon H, Barnes R, Birnie C, D'Cunha Kamath A, Hughes E, Middleton S, Owen R, Schofield E, Short C, Smith R, Wang H, Willett M, Zimmerman M, Balfour J, Chadwick T, Coombe-Jones M, Do Le HP, Faulkner G, Hobson K, Shehata Z, Beattie M, Chmielewski G, Chong C, Donnelly B, Drusch B, Ellis J, Farrelly C, Feyi-Waboso J, Hibell I, Hoade L, Ho C, Jones H, Kodiatt B, Lidder P, Ni Cheallaigh L, Norman R, Patabendi I, Penfold H, Playfair M, Pomeroy S, Ralph C, Rottenburg H, Sebastian J, Sheehan M, Stanley V, Welchman J, Ajdarpasic D, Antypas A, Azouaghe O, Basi S, Bettoli G, Bhattarai S, Bommireddy L, Bourne K, Budding J, Cookey-Bresi R, Cummins T, Davies G, Fabelurin C, Gwilliam R, Hanley J, Hird A, Kruczynska A, Langhorne B, Lund J, Lutchman I, McGuinness R, Neary M, Pampapathi S, Pang E, Podbicanin S, Rai N, Redhouse White G, Sujith J, Thomas P, Walker I, Winterton R, Anderson P, Barrington M, Bhadra K, Clark G, Fowler G, Gibson C, Hudson S, Kaminskaite V, Lawday S, Longshaw A, MacKrill E, McLachlan F, Murdeshwar A, Nieuwoudt R, Parker P, Randall R, Rawlins E, Reeves SA, Rye D, Sirkis T, Sykes B, Ventress N, Wosinska N, Akram B, Burton L, Coombs A, Long R, Magowan D, Ong C, Sethi M, Williams G, Chan C, Chan LH, Fernando D, Gaba F, Khor Z, Les JW, Mak R, Moin S, Ng Kee Kwong KC, Paterson-Brown S, Tew YY, Bardon A, Burrell K, Coldwell C, Costa I, Dexter E, Hardy A, Khojani M, Mazurek J, Raymond T, Reddy V, Reynolds J, Soma A, Agiotakis S, Alsusa H, Desai N, Peristerakis I, Adcock A, Ayub H, Bennett T, Bibi F, Brenac S, Chapman T, Clarke G, Clark F, Galvin C, Gwyn-Jones A, Henry-Blake C, Kerner S, Kiandee M, Lovett A, Pilecka A, Ravindran R, Siddique H, Sikand T, Treadwell K, Akmal K, Apata A, Barton O, Broad G, Darling H, Dhuga Y, Emms L, Habib S, Jain R, Jeater J, Kan CYP, Kathiravelupillai A, Khatkar H, Kirmani S, Kulasabanathan K, Lacey H, Lal K, Manafa C, Mansoor M, McDonald S, Mittal A, Mustoe S, Nottrodt L, Oliver P, Papapetrou I, Pattinson F, Raja M, Reyhani H, Shahmiri A, Small O, Soni U, Aguirrezabala Armbruster B, Bunni J, Hakim MA, Hawkins-Hooker L, Howell KA, Hullait R, Jaskowska A, Ottewell L, Thomas-Jones I, Vasudev A, Clements B, Fenton J, Gill M, Haider S, Lim AJM, Maguire H, McMullan J, Nicoletti J, Samuel S, Unais MA, White N, Yao PC, Yow L, Boyle C, Brady R, Cheekoty P, Cheong J, Chew SJHL, Chow R, Ganewatta Kankanamge D, Mamer L, Mohammed B, Ng Chieng Hin J, Renji Chungath R, Royston A, Sharrad E, Sinclair R, Tingle S, Treherne K, Wyatt F, Maniarasu VS, Moug S, Appanna T, Bucknall T, Hussain F, Owen A, Parry M, Parry R, Sagua N, Spofforth K, Yuen ECT, Bosley N, Hardie W, Moore T, Regas C, Abdel-Khaleq S, Ali N, Bashiti H, Buxton-Hopley R, Constantinides M, D'Afflitto M, Deshpande A, Duque Golding J, Frisira E, Germani Batacchi M, Gomaa A, Hay D, Hutchison R, Iakovou A, Iakovou D, Ismail E, Jefferson S, Jones L, Khouli Y, Knowles C, Mason J, McCaughan R, Moffatt J, Morawala A, Nadir H, Neyroud F, Nikookam Y, Parmar A, Pinto L, Ramamoorthy R, Richards E, Thomson S, Trainer C, Valetopoulou A, Vassiliou A, Wantman A, Wilde S, Dickinson M, Rockall T, Senn D, Wcislo K, Zalmay P, Adelekan K, Allen K, Bajaj M, Gatumbu P, Hang S, Hashmi Y, Kaur T, Kawesha A, Kisiel A, Woodmass M, Adelowo T, Ahari D, Alhwaishel K, Atherton R, Clayton B, Cockroft A, Curtis Lopez C, Hilton M, Ismail N, Kouadria M, Lee L, MacConnachie A, Monks F, Mungroo S, Nikoletopoulou C, Pearce L, Sara X, Shahid A, Suresh G, Wilcha R, Atiyah A, Davies E, Dermanis A, Gibbons H, Hyde A, Lawson A, Lee C, Leung-Tack M, Li Saw Hee J, Mostafa O, Nair D, Pattani N, Plumbley-Jones J, Pufal K, Ramesh P, Sanghera J, Saram S, Scadding S, See S, Stringer H, Torrance A, Vardon H, Wyn-Griffiths F, Brew A, Kaur G, Soni D, Tickle A, Akbar Z, Appleyard T, Figg K, Jayawardena P, Johnson A, Kamran Siddiqui Z, Lacy-Colson J, Oatham R, Rowlands B, Sludden E, Turnbull C, Allin D, Ansar Z, Azeez Z, Dale VH, Garg J, Horner A, Jones S, Knight S, McGregor C, McKenna J, McLelland T, Packham-Smith A, Rowsell K, Spector-Hill I, Adeniken E, Baker J, Bartlett M, Chikomba L, Connell B, Deekonda P, Dhar M, Elmansouri A, Gamage K, Goodhew R, Hanna P, Knight J, Luca A, Maasoumi N, Mahamoud F, Manji S, Marwaha PK, Mason F, Oluboyede A, Pigott L, Razaq AM, Richardson M, Saddaoui I, Wijeyendram P, Yau S, Atkins W, Liang K, Miles N, Praveen B, Ashai S, Braganza J, Common J, Cundy A, Davies R, Guthrie J, Handa I, Iqbal M, Ismail R, Jones C, Jones I, Lee KS, Levene A, Okocha M, Olivier J, Smith A, Subramaniam E, Tandle S, Wang A, Watson A, Wilson C, Chan XHF, Khoo E, Montgomery C, Norris M, Pugalenthi PP, Common T, Cook E, Mistry H, Shinmar HS, Agarwal G, Bandyopadhyay S, Brazier B, Carroll L, Goede A, Harbourne A, Lakhani A, Lami M, Larwood J, Martin J, Merchant J, Pattenden S, Pradhan A, Raafat N, Rothwell E, Shammoon Y, Sudarshan R, Vickers E, Wingfield L, Ashworth I, Azizi S, Bhate R, Chowdhury T, Christou A, Davies L, Dwaraknath M, Farah Y, Garner J, Gureviciute E, Hart E, Jain A, Javid S, Kankam HK, Kaur Toor P, Kaz R, Kermali M, Khan I, Mattson A, McManus A, Murphy M, Nair K, Ngemoh D, Norton E, Olabiran A, Parry L, Payne T, Pillai K, Price S, Punjabi K, Raghunathan A, Ramwell A, Raza M, Ritehnia J, Simpson G, Smith W, Sodeinde S, Studd L, Subramaniam M, Thomas J, Towey S, Tsang E, Tuteja D, Vasani J, Vio M, Badran A, Adams J, Anthony Wilkinson J, Asvandi S, Austin T, Bald A, Bix E, Carrick M, Chander B, Chowdhury S, Cooper Drake B, Crosbie S, D Portela S, Francis D, Gallagher C, Gillespie R, Gravett H, Gupta P, Ilyas C, James G, Johny J, Jones A, Kinder F, MacLeod C, Macrow C, Maqsood-Shah A, Mather J, McCann L, McMahon R, Mitham E, Mohamed M, Munton E, Nightingale K, O'Neill K, Onyemuchara I, Senior R, Shanahan A, Sherlock J, Spyridoulias A, Stavrou C, Stokes D, Tamang R, Taylor E, Trafford C, Uden C, Waddington C, Yassin D, Zaman M, Bangi S, Cheng T, Chew D, Hussain N, Imani-Masouleh S, Mahasivam G, McKnight G, Ng HL, Ota HC, Pasha T, Ravindran W, Shah K, Vishnu K S, Zaman S, Carr W, Cope S, Eagles EJ, Howarth-Maddison M, Li CY, Reed J, Ridge A, Stubbs T, Teasdaled D, Umar R, Worthington J, Dhebri A, Kalenderov R, Alattas A, Arain Z, Bhudia R, Chia D, Daniel S, Dar T, Garland H, Girish M, Hampson A, Kyriacou H, Lehovsky K, Mullins W, Omorphos N, Vasdev N, Venkatesh A, Waldock W, Bhandari A, Brown G, Choa G, Eichenauer CE, Ezennia K, Kidwai Z, Lloyd-Thomas A, Macaskill Stewart A, Massardi C, Sinclair E, Skajaa N, Smith M, Tan I, Afsheen N, Anuar A, Azam Z, Bhatia P, Davies-kelly N, Dickinson S, Elkawafi M, Ganapathy M, Gupta S, Khoury EG, Licudi D, Mehta V, Neequaye S, Nita G, Tay VL, Zhao S, Botsa E, Cuthbert H, Elliott J, Furlepa M, Lehmann J, Mangtani A, Narayan A, Nazarian S, Parmar C, Shah D, Shaw C, Zhao Z, Beck C, Caldwell S, Clements JM, French B, Kenny R, Kirk S, Lindsay J, McClung A, McLaughlin N, Watson S, Whiteside E, Alyacoubi S, Arumugam V, Beg R, Dawas K, Garg S, Lloyd ER, Mahfouz Y, Manobharath N, Moonesinghe R, Morka N, Patel K, Prashar J, Yip S, Adeeko ES, Ajekigbe F, Bhat A, Evans C, Farrugia A, Gurung C, Long T, Malik B, Manirajan S, Newport D, Rayer J, Ridha A, Ross E, Saran T, Sinker A, Waruingi D, Allen R, Al Sadek Y, Alves do Canto Brum H, Asharaf H, Ashman M, Balakumar V, Barrington J, Baskaran R, Berry A, Bhachoo H, Bilal A, Boaden L, Chia WL, Covell G, Crook D, Dadnam F, Davis L, De Berker H, Doyle C, Fox C, Gruffydd-Davies M, Hafouda Y, Hill A, Hubbard E, Hunter A, Inpadhas V, Jamshaid M, Jandu G, Jeyanthi M, Jones T, Kantor C, Kwak SY, Malik N, Matt R, McNulty P, Miles C, Mohomed A, Myat P, Niharika J, Nixon A, O'Reilly D, Parmar K, Pengelly S, Price L, Ramsden M, Turnor R, Wales E, Waring H, Wu M, Yang T, Ye TTS, Zander A, Zeicu C, Bellam S, Francombe J, Kawamoto N, Rahman MR, Sathyanarayana A, Tang HT, Cheung J, Hollingshead J, Page V, Sugarman J, Wong E, Chiong J, Fung E, Kan SY, Kiang J, Kok J, Krahelski O, Liew MY, Lyell B, Sharif Z, Speake D, Alim L, Amakye NY, Chandrasekaran J, Chandratreya N, Drake J, Owoso T, Thu YM, Abou El Ela Bourquin B, Alberts J, Chapman D, Rehnnuma N, Ainsworth K, Carpenter H, Emmanuel T, Fisher T, Gabrel M, Guan Z, Hollows S, Hotouras A, Ip Fung Chun N, Jaffer S, Kallikas G, Kennedy N, Lewinsohn B, Liu FY, Mohammed S, Rutherfurd A, Situ T, Stammer A, Taylor F, Thin N, Urgesi E, Zhang N, Ahmad MA, Bishop A, Bowes A, Dixit A, Glasson R, Hatta S, Hatt K, Larcombe S, Preece J, Riordan E, Fegredo D, Haq MZ, Li C, McCann G, Stewart D, Baraza W, Bhullar D, Burt G, Coyle J, Deans J, Devine A, Hird R, Ikotun O, Manchip G, Ross C, Storey L, Tan WWL, Tse C, Warner C, Whitehead M, Wu F, Court EL, Crisp E, Huttman M, Mayes F, Robertson H, Rosen H, Sandberg C, Smith H, Al Bakry M, Ashwell W, Bajaj S, Bandyopadhyay D, Browlee O, Burway S, Chand CP, Elsayeh K, Elsharkawi A, Evans E, Ferrin S, Fort-Schaale A, Iacob M, I K, Impelliziere Licastro G, Mankoo AS, Olaniyan T, Otun J, Pereira R, Reddy R, Saeed D, Simmonds O, Singhal G, Tron K, Wickstone C, Williams R, Bradshaw E, De Kock Jewell V, Houlden C, Knight C, Metezai H, Mirza-Davies A, Seymour Z, Spink D, Wischhusen S. Evaluation of prognostic risk models for postoperative pulmonary complications in adult patients undergoing major abdominal surgery: a systematic review and international external validation cohort study. Lancet Digit Health 2022; 4:e520-e531. [PMID: 35750401 DOI: 10.1016/s2589-7500(22)00069-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 01/07/2022] [Accepted: 04/06/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Stratifying risk of postoperative pulmonary complications after major abdominal surgery allows clinicians to modify risk through targeted interventions and enhanced monitoring. In this study, we aimed to identify and validate prognostic models against a new consensus definition of postoperative pulmonary complications. METHODS We did a systematic review and international external validation cohort study. The systematic review was done in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched MEDLINE and Embase on March 1, 2020, for articles published in English that reported on risk prediction models for postoperative pulmonary complications following abdominal surgery. External validation of existing models was done within a prospective international cohort study of adult patients (≥18 years) undergoing major abdominal surgery. Data were collected between Jan 1, 2019, and April 30, 2019, in the UK, Ireland, and Australia. Discriminative ability and prognostic accuracy summary statistics were compared between models for the 30-day postoperative pulmonary complication rate as defined by the Standardised Endpoints in Perioperative Medicine Core Outcome Measures in Perioperative and Anaesthetic Care (StEP-COMPAC). Model performance was compared using the area under the receiver operating characteristic curve (AUROCC). FINDINGS In total, we identified 2903 records from our literature search; of which, 2514 (86·6%) unique records were screened, 121 (4·8%) of 2514 full texts were assessed for eligibility, and 29 unique prognostic models were identified. Nine (31·0%) of 29 models had score development reported only, 19 (65·5%) had undergone internal validation, and only four (13·8%) had been externally validated. Data to validate six eligible models were collected in the international external validation cohort study. Data from 11 591 patients were available, with an overall postoperative pulmonary complication rate of 7·8% (n=903). None of the six models showed good discrimination (defined as AUROCC ≥0·70) for identifying postoperative pulmonary complications, with the Assess Respiratory Risk in Surgical Patients in Catalonia score showing the best discrimination (AUROCC 0·700 [95% CI 0·683-0·717]). INTERPRETATION In the pre-COVID-19 pandemic data, variability in the risk of pulmonary complications (StEP-COMPAC definition) following major abdominal surgery was poorly described by existing prognostication tools. To improve surgical safety during the COVID-19 pandemic recovery and beyond, novel risk stratification tools are required. FUNDING British Journal of Surgery Society.
Collapse
|
16
|
Yuan JJ, Chen SH, Xie YL, Xue Q, Mao YY, Xing F, Wang DM, Yang JJ. [Effects of subanesthetic dose of esketamine on opioid consumption after thoracoscopic surgery]. Zhonghua Yi Xue Za Zhi 2022; 102:1108-1113. [PMID: 35436810 DOI: 10.3760/cma.j.cn112137-20211116-02559] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [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 continuous intravenous infusion of subanesthetic dose of esketamine intraoperatively on postoperative opioid consumption in patients undergoing thoracoscopic surgery. Methods: A total of 71 patients with elective thoracoscopic lung surgery in the First Affiliated Hospital of Zhengzhou University from December 2020 to December 2021 were selected. Patients who were classified as grade Ⅰ or Ⅱ by the American Society of Anesthesiologists (ASA) and aged 18-70 years were included, including 32 males and 39 females, with a body mass index (BMI) of 18.5-30.0 kg/m2. The patients were randomly divided into three groups: (1) Control group (group C, n=24): continuous intravenous infusion of normal saline at the same rate during surgery; (2) Subanesthetic dose of esketamine 0.125 mg·kg-1·h-1 group (group ES1, n=23): continuous intravenous infusion of esketamine at a rate of 0.125 mg·kg-1·h-1 during surgery; (3) Subanesthetic dose of esketamine 0.250 mg·kg-1·h-1 group (group ES2, n=24): continuous intravenous infusion of esketamine at a rate of 0.250 mg·kg-1·h-1 during surgery. The main outcome measures were the total consumptions of hydromorphone of 3 groups within 24 and 48 hours after surgery. The secondary outcome measures were the extubation time, length of postanesthesia care unit (PACU) stay, the time of first feeding, and the incidences of adverse effects within 24 h after surgery in 3 groups. Results: The 24 h postoperative consumption of hydromorphone in group C, ES1 and ES2 was (5.4±1.0) mg, (4.5±1.5) mg and (4.0±0.8) mg, respectively. Likewise, the 48 h postoperative consumption of hydromorphone was (9.7±2.2) mg, (9.0±3.0) mg and (7.7±1.8) mg, respectively. Compared with group C, the 24 h postoperative hydromorphone consumptions were significantly reduced in group ES1 and ES2 (both P<0.05). The extubation time, length of PACU stay and the time of first feeding after surgery in group C were (23±10) min,(70±12) min,(17±3) h,in group ES1 were (22±4) min,(69±11) min,(14±5) h,in group ES2 were (16±8) min,(58±12) min,(14±3) h, respectively. Compared with group C and group ES1, both of the extubation time and length of PACU stay were shortened in group ES2 (both P<0.05). Compared with group C, the first postoperative feeding time of group ES1 and ES2 was shortened (both P<0.05). There were no differences in the incidences of adverse effects at postoperative 24 h among 3 groups (all P>0.05). Conclusion: Continuously intravenous infusion of subanesthetic esketamine at a rate of 0.250 mg·kg-1·h-1 can significantly reduce the postoperative opioid consumption and improve the patient's outcomes.
Collapse
Affiliation(s)
- J J Yuan
- Department of Anesthesiology, Pain and Perioperative Medicine, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China
| | - S H Chen
- Department of Anesthesiology, Pain and Perioperative Medicine, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China
| | - Y L Xie
- Department of Anesthesiology, Pain and Perioperative Medicine, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China
| | - Q Xue
- Department of Anesthesiology, Pain and Perioperative Medicine, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China
| | - Y Y Mao
- Department of Anesthesiology, Pain and Perioperative Medicine, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China
| | - F Xing
- Department of Anesthesiology, Pain and Perioperative Medicine, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China
| | - D M Wang
- Department of Anesthesiology, Pain and Perioperative Medicine, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China
| | - J J Yang
- Department of Anesthesiology, Pain and Perioperative Medicine, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China
| |
Collapse
|
17
|
Xiao Y, Zhong CH, Wei FH, Dai LF, Yang JJ, Chen YY. [Epidemiological trends for human schistosomiasis prevalence in Hubei Province from 2004 to 2018 based on Joinpoint regression analysis]. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi 2022; 34:122-127. [PMID: 35537833 DOI: 10.16250/j.32.1374.2022011] [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] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
OBJECTIVE To analyze the trends of human schistosomiasis prevalence in Hubei Province from 2004 to 2018, so as to provide the evidence for formulating the schistosomiasis elimination strategy in the province. METHODS All data pertaining to human schistosomiasis prevalence in Hubei Province were collected from 2004 to 2018, and the trends for changes in seroprevalence, egg-positive rate and prevalence of human Schistosoma japonicum infection were analyzed using a Joinpoint regression model. RESULTS Both of the numbers of residents seropositive and egg-positive for S. japonicum infections appeared a tendency towards a decline in Hubei Province from 2004 to 2018, and the prevalence of human S. japonicum infections reduced from 6.85% in 2004 to 0 in 2018. Joinpoint regression analysis showed that the prevalence of human S. japonicum infections appeared an overall tendency towards a reduction in Hubei Province from 2004 to 2018 [average annual percent change (AAPC) = -24.1%, P < 0.01], and the trends for the reduction were both significant during the period from 2004 to 2006 [annual percent change (APC) = -35.1%, P < 0.01] and from 2006 to 2018 (APC = -22.1%, P < 0.01). The prevalence of human S. japonicum infections appeared a tendency towards a decline in islet (AAPC = -25.1%, P < 0.01), inner embankment (AAPC = -26.4%, P < 0.01) and hilly subtypes of schistosomiasis-endemic areas (AAPC = -32.5%, P < 0.01) of Hubei Province from 2004 to 2018, and the prevalence all appeared a tendency towards a decline during the infection control stage (from 2004 to 2008), the transmission control stage (from 2009 to 2013) and the transmission interruption stage (from 2014 to 2018) (AAPC = -28.0%, -24.4% and -63.8%, all P values < 0.01). The seroprevalence of human S. japonicum infections appeared an overall tendency towards a decline in Hubei Province from 2004 to 2018 (AAPC = -14.5%, P < 0.01), and the trends for the reduction were both significant during the period from 2004 to 2012 (APC = -8.4%, P < 0.01) and from 2012 to 2018 (APC = -22.1%, P < 0.01). In addition, the egg-positive rate of human S. japonicum infections appeared an overall tendency towards a decline in Hubei Province from 2004 to 2018 (AAPC = -30.6%, P < 0.05), and the trend for the reduction was significant during the period from 2007 to 2014 (APC = -15.5%, P < 0.01). CONCLUSIONS The prevalence of human schistosomiasis appeared a tendency towards a decline in Hubei Province from 2004 to 2018, and the islet and inner embankment subtypes of endemic areas are a high priority for schistosomiasis control during the stage moving towards elimination in Hubei Province.
Collapse
Affiliation(s)
- Y Xiao
- Hubei Center for Disease Control and Prevention, Wuhan, Hubei 430079, China
| | - C H Zhong
- Hubei Center for Disease Control and Prevention, Wuhan, Hubei 430079, China
| | - F H Wei
- Hubei Center for Disease Control and Prevention, Wuhan, Hubei 430079, China
| | - L F Dai
- Hubei Center for Disease Control and Prevention, Wuhan, Hubei 430079, China
| | - J J Yang
- Hubei Center for Disease Control and Prevention, Wuhan, Hubei 430079, China
| | - Y Y Chen
- Hubei Center for Disease Control and Prevention, Wuhan, Hubei 430079, China
| |
Collapse
|
18
|
Yu WH, Liu L, Yang JJ, Zhao H, Li XT. [Feasibility analysis of immediate implant placement in the maxillary molar region]. Zhonghua Kou Qiang Yi Xue Za Zhi 2022; 57:251-257. [PMID: 35280002 DOI: 10.3760/cma.j.cn112144-20210324-00139] [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 study the clinical outcomes and feasibility of immediate implantation after flap surgery and minimally invasive extraction in the maxillary molar area and to provide a reference for it. Methods: Forty-one patients (41 molars in total) with maxillary molars that could not be preserved, treated from June 2018 to June 2020 at the Department of Oral and Maxillofacial Surgery at the Affiliated Hospital of Qingdao University, were selected. There are 24 males and 17 females with the age of (49.7±1.8) years (range 18-66 years). Pre-operative cone-beam CT (CBCT) was taken for measurement and analysis. After flap surgery and minimally invasive tooth extraction, the inflammatory granulation tissues attached to the soft and hard tissues were completely scraped and clipped, followed by the preparation of the implants in the correct three-dimensional position. Torque value and implant stability quotient (ISQ) were recorded after implant placement and with non-submerged healing. CBCT examination was taken 6 months after surgery and ISQ value was checked before crown restoration. CBCT examination was also taken 1 year after the permanent restoration. The survival rate of 6 months after surgery, the success rate of 1 year after permanent restoration, and the size of jump gaps immediately after surgery, 6 months after surgery, 1 year after permanent restoration respectively, were performed. The ISQ values were compared immediately and 6 months after surgery. Results: A total of 41 implants were placed in 41 patients. Six months after surgery, the survival rate was 100% (41/41). Twelve months after permanent restoration, the success rate of the implant restoration was 100% (41/41). The torque value after implant implantation was (42.77±0.79) N·cm. The buccal and palatal jump gaps were (3.15±0.16) mm and (2.86±0.18) mm immediately after surgery, respectively. The mesial and distal jump gaps were (2.94±0.19) mm and (3.77±0.21) mm, respectively. CBCT showed that no jump gap around the implants at 6 months after surgery and 1 year after permanent restoration. The ISQ values at immediately and 6 months after surgery were (74.78±0.59) and (80.20±0.49) respectively, and the difference was statistically significant (t=-9.03, P<0.001). Conclusions: Immediate dental implantation in the correct three-dimensional position could achieve good osseointegration by means of flap surgery, minimally invasive extraction and thorough removal of inflammatory tissue on the surface of soft and hard tissues. The clinical outcomes were satisfactory.
Collapse
Affiliation(s)
- W H Yu
- Department of Oral and Maxillofacial Surgery, The Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - L Liu
- Department of Oral and Maxillofacial Surgery, The Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - J J Yang
- Department of Oral and Maxillofacial Surgery, The Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - H Zhao
- Department of Oral and Maxillofacial Surgery, The Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - X T Li
- Department of Oral and Maxillofacial Surgery, The Affiliated Hospital of Qingdao University, Qingdao 266000, China
| |
Collapse
|
19
|
Yu Q, Wang Q, Zhang Y, Chen C, Ryu H, Park N, Baek JE, Li K, Wu Y, Li D, Xu J, Liu M, Yang JJ, Zhang C, Lu C, Zhang P, Li X, Chen B, Ebeid IA, Fensel J, Min C, Zhai Y, Song M, Ding Y, Bu Y. Reply to issues about entitymetrics and paper-entity citation network. Scientometrics 2022. [DOI: 10.1007/s11192-022-04311-y] [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]
|
20
|
Wang K, Yang JJ, Liu ZN, Dou GH, Wang X, Shan DK, Chen YD. [A pretest model of obstructive coronary artery disease based on machine learning: from the C-Strat study]. Zhonghua Nei Ke Za Zhi 2022; 61:185-192. [PMID: 35090254 DOI: 10.3760/cma.j.cn112138-20210119-00049] [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 develop a pretest probability model of obstructive coronary artery disease with machine learning based on multi-site Chinese population data. Methods: Chinese regiStry in early deTection and Risk strAtificaTion of coronary plaques (C-Strat) study is a prospective multi-center cohort study, in which consecutive patients with suspected obstructive coronary artery disease and ≥64 detector row coronary computed tomography angioplasty (CCTA) evaluation were included. Data from the patients were randomly split into a training set (70%) and a test set (30%). More than 50% of coronary artery stenosis by CCTA was defined as positive outcome. A boosted ensemble algorithm (XGBoost), 10-fold cross-validation and Bayesian optimization were used to establish a new prediction model-CARDIACS(pretest probability model from Chinese registry in eARly Detection and rIsk stratificAtion of Coronary plaques Study), and a logistic regression was used to establish a model-LOGISTIC in training set. The test set was used for validation and comparison among CARDIACS, LOGISTIC, UDFM (updated Diamond-Forrester Model) and DFCASS(Diamond-Forrester and CASS). Results: The study population included 29 455 patients with age of (57.0±9.7) years and 44.8% women, of whom 19.1% (5 622/29 455) had obstructive coronary artery disease. For CARDIACS, the age, the reason for visit and the body mass index (BMI) were the most important predictive variables. In the independent test set, the area under the curve (AUC) of CARDIACS was 0.72 (95%CI 0.70-0.73), which was significantly superior to that of LOGISTIC (AUC 0.69, 95%CI 0.68-0.71, P=0.015), UDFM (AUC 0.64, 95%CI 0.62-0.65, P<0.001) and DFCASS (AUC 0.66, 95%CI 0.64-0.67, P<0.001), respectively. Conclusion: Based on Chinese population, the study developed a new pretest probability model--CARDIACS, which was superior to the traditional models. CARDIACS is expected to assist in the clinical decision-making for patients with stable chest pain.
Collapse
Affiliation(s)
- K Wang
- Department of Cardiology, First Medical Center,Chinese PLA General Hospital, Beijing 100853, China
| | - J J Yang
- Department of Cardiology, First Medical Center,Chinese PLA General Hospital, Beijing 100853, China
| | - Z N Liu
- Department of Cardiology, First Medical Center,Chinese PLA General Hospital, Beijing 100853, China
| | - G H Dou
- Department of Cardiology, First Medical Center,Chinese PLA General Hospital, Beijing 100853, China
| | - X Wang
- Department of Cardiology, First Medical Center,Chinese PLA General Hospital, Beijing 100853, China
| | - D K Shan
- Department of Cardiology, First Medical Center,Chinese PLA General Hospital, Beijing 100853, China
| | - Y D Chen
- Department of Cardiology, First Medical Center,Chinese PLA General Hospital, Beijing 100853, China
| |
Collapse
|
21
|
Yang JJ, Gessner CR, Duerksen JL, Biber D, Binder JL, Ozturk M, Foote B, McEntire R, Stirling K, Ding Y, Wild DJ. Knowledge graph analytics platform with LINCS and IDG for Parkinson's disease target illumination. BMC Bioinformatics 2022; 23:37. [PMID: 35021991 PMCID: PMC8756622 DOI: 10.1186/s12859-021-04530-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 12/13/2021] [Indexed: 11/12/2022] Open
Abstract
Background LINCS, "Library of Integrated Network-based Cellular Signatures", and IDG, "Illuminating the Druggable Genome", are both NIH projects and consortia that have generated rich datasets for the study of the molecular basis of human health and disease. LINCS L1000 expression signatures provide unbiased systems/omics experimental evidence. IDG provides compiled and curated knowledge for illumination and prioritization of novel drug target hypotheses. Together, these resources can support a powerful new approach to identifying novel drug targets for complex diseases, such as Parkinson's disease (PD), which continues to inflict severe harm on human health, and resist traditional research approaches. Results Integrating LINCS and IDG, we built the Knowledge Graph Analytics Platform (KGAP) to support an important use case: identification and prioritization of drug target hypotheses for associated diseases. The KGAP approach includes strong semantics interpretable by domain scientists and a robust, high performance implementation of a graph database and related analytical methods. Illustrating the value of our approach, we investigated results from queries relevant to PD. Approved PD drug indications from IDG’s resource DrugCentral were used as starting points for evidence paths exploring chemogenomic space via LINCS expression signatures for associated genes, evaluated as target hypotheses by integration with IDG. The KG-analytic scoring function was validated against a gold standard dataset of genes associated with PD as elucidated, published mechanism-of-action drug targets, also from DrugCentral. IDG's resource TIN-X was used to rank and filter KGAP results for novel PD targets, and one, SYNGR3 (Synaptogyrin-3), was manually investigated further as a case study and plausible new drug target for PD. Conclusions The synergy of LINCS and IDG, via KG methods, empowers graph analytics methods for the investigation of the molecular basis of complex diseases, and specifically for identification and prioritization of novel drug targets. The KGAP approach enables downstream applications via integration with resources similarly aligned with modern KG methodology. The generality of the approach indicates that KGAP is applicable to many disease areas, in addition to PD, the focus of this paper. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04530-9.
Collapse
|
22
|
Yang JJ, Grissa D, Lambert CG, Bologa CG, Mathias SL, Waller A, Wild DJ, Jensen LJ, Oprea TI. TIGA: target illumination GWAS analytics. Bioinformatics 2021; 37:3865-3873. [PMID: 34086846 PMCID: PMC11025677 DOI: 10.1093/bioinformatics/btab427] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 05/12/2021] [Accepted: 06/03/2021] [Indexed: 12/31/2022] Open
Abstract
MOTIVATION Genome-wide association studies can reveal important genotype-phenotype associations; however, data quality and interpretability issues must be addressed. For drug discovery scientists seeking to prioritize targets based on the available evidence, these issues go beyond the single study. RESULTS Here, we describe rational ranking, filtering and interpretation of inferred gene-trait associations and data aggregation across studies by leveraging existing curation and harmonization efforts. Each gene-trait association is evaluated for confidence, with scores derived solely from aggregated statistics, linking a protein-coding gene and phenotype. We propose a method for assessing confidence in gene-trait associations from evidence aggregated across studies, including a bibliometric assessment of scientific consensus based on the iCite relative citation ratio, and meanRank scores, to aggregate multivariate evidence.This method, intended for drug target hypothesis generation, scoring and ranking, has been implemented as an analytical pipeline, available as open source, with public datasets of results, and a web application designed for usability by drug discovery scientists. AVAILABILITY AND IMPLEMENTATION Web application, datasets and source code via https://unmtid-shinyapps.net/tiga/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Jeremy J Yang
- Division of Translational Informatics, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
- Integrative Data Science Laboratory, School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN 47408, USA
| | - Dhouha Grissa
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| | - Christophe G Lambert
- Division of Translational Informatics, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Cristian G Bologa
- Division of Translational Informatics, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Stephen L Mathias
- Division of Translational Informatics, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Anna Waller
- Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - David J Wild
- Integrative Data Science Laboratory, School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN 47408, USA
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| | - Tudor I Oprea
- Division of Translational Informatics, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| |
Collapse
|
23
|
Yang Z, Asare E, Yang Y, Yang JJ, Yang HM, Wang ZY. Dietary supplementation of betaine promotes lipolysis by regulating fatty acid metabolism in geese. Poult Sci 2021; 100:101460. [PMID: 34564022 PMCID: PMC8484806 DOI: 10.1016/j.psj.2021.101460] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 07/29/2021] [Accepted: 08/23/2021] [Indexed: 11/26/2022] Open
Abstract
Supplementation of betaine in the diet appears to regulate fatty acid metabolism and decrease fat deposition. This study aims to identify the effects of dietary supplementation of betaine on zootechnical performance, fatty acid synthesis, abdominal fat deposition, and morphology. Three hundred healthy, male, one-day-old Jiangnan White geese of similar body weight were randomly divided into 5 groups, with 6 replicates per treatment and 10 geese per replicate, and given the following amounts of supplementary betaine: 0 (group A), 600 mg/kg (group B), 1,200 mg/kg (group C), 1,800 mg/kg (group D), or 2,400 mg/kg (group E). Feed intake (FI), body weight (BW), abdominal fat and sebum thickness, clinical blood parameters, hepatic enzyme activity, and abdominal fat morphology were monitored during the experiment. All geese had free access to feed and water throughout the study. Our results indicate that supplementation of betaine increased zootechnical performance at 21 and 42 d of age. The percentage of abdominal fat and sebum thickness of geese at 63 d of age decreased linearly with the addition of betaine (P < 0.05). The triglyceride (TG) and total cholesterol (TCHOL) content of serum decreased with the increased level of betaine when measured at 63 d of age (P<0.05). Hormone sensitive lipase (HSL) increased with the level of betaine (P<0.05). However, dietary betaine appeared to decrease the activity of fatty acid synthase (FAS) in the geese at 42 d and 63 d of age (P<0.05). The percentage of total area of lipid droplet decreased with the increased level of betaine supplementation. In conclusion, dietary supplementation of betaine increased lipolysis and decreased fat deposition in the finishing period of geese via reducing feed intake. However, the precise mode-of-action is yet unclear and warrants further research.
Collapse
Affiliation(s)
- Z Yang
- Joint International Research Laboratory of Agriculture and Agri-Product Safety of Ministry of Education of China, Yangzhou University, Yangzhou, Jiangsu Province, 225009, P. R. China.
| | - E Asare
- College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu Province, 225009, P. R. China
| | - Y Yang
- College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu Province, 225009, P. R. China
| | - J J Yang
- College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu Province, 225009, P. R. China
| | - H M Yang
- College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu Province, 225009, P. R. China
| | - Z Y Wang
- Joint International Research Laboratory of Agriculture and Agri-Product Safety of Ministry of Education of China, Yangzhou University, Yangzhou, Jiangsu Province, 225009, P. R. China; College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu Province, 225009, P. R. China
| |
Collapse
|
24
|
Yang JJ, Cheng LY, Xu W. [Study on changes of voice characteristics after adenotonsillectomy or adenoidectomy in children]. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2021; 56:724-729. [PMID: 34344099 DOI: 10.3760/cma.j.cn115330-20200813-00672] [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: 11/05/2022]
Abstract
Objective: To study voice changes in children after adenotonsillectomy or adenoidectomy and the relationship with the vocal tract structure. Methods: Fifty patients were recruited in this study prospectively, aged from 4 to 12 years old with the median age of 6. They were underwent adenotonsillectomy or adenoidectomy in Beijing Tongren Hospital, Capital Medical University from July 2019 to August 2020. In the cases, there are 31 males and 19 females. Thirty-six patients underwent adenotonsillectomy and 14 patients underwent adenoidectomy alone. Twenty-two children (13 males, 9 females) with Ⅰ degree of bilateral tonsils without adenoid hypertrophy and no snoring were selected as normal controls. Adenoid and tonsil sizes were evaluated. Subjective changes of voice were recorded after surgery. Moreover, voice data including fundamental frequency(F0), jitter, shimmer, noise to harmonic ratio(NHR), maximum phonation time(MPT), formant frequencies(F1-F5) and bandwidths(B1-B5) of vowel/a/and/i/were analyzed before, 3 days and 1 month after surgery respectively.SPSS 23.0 was used for statistical analysis. Results: Thirty-six patients(72.0%,36/50) complained of postoperative voice changes. The incidence was inversely correlated with age. In children aged 4-6, 7-9, and 10-12, the incidence was 83.3%(25/30), 63.6%(7/11) and 44.4%(4/9) respectively. Voice changes appeared more common in children underwent adenotonsillectomy(77.8%,28/36) than in those underwent adenoidectomy alone(57.1%,8/14), but there was no statistical difference. After operation, for vowel/a/, MPT(Z=2.18,P=0.041) and F2(t=2.13,P=0.040) increased, B2(Z=2.04,P=0.041) and B4(Z=2.00,P=0.046) decreased. For vowel/i/, F2(t=2.035,P=0.050) and F4(t=4.44,P=0.0001) increased, B2(Z=2.36,P=0.019) decreased. Other acoustic parameters were not significantly different from those before surgery. The F2(r=-0.392, P =0.032) of vowel/a/and F2(r=-0.279, P=0.048) and F4 (r=-0.401, P =0.028) of vowel/i/after adenotonsillectomy were significantly higher than those of adenoidectomy alone. Half of patients with postopertive voice changes can recover spontaneously 1 month after surgery. Conclusions: Voice changes in children underwent adenotonsillectomy or adenoidectomy might be related to their changes in formants and bandwidths. The effect of adenotonsillectomy on voice was more significant compared with that of adenoidectomy alone. The acoustic parameters did not change significantly after surgery except MPT.
Collapse
Affiliation(s)
- J J Yang
- Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China
| | - L Y Cheng
- Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China
| | - W Xu
- Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China
| |
Collapse
|
25
|
Chen YY, Liu JB, Zhong CH, Xiao Y, Wei FH, Yang JJ, Zhang WH, Liu S. [Establishment of an indicator system for schistosomiasis transmission risk assessment after transmission interruption in Hubei Province based on the Delphi method]. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi 2021; 33:240-247. [PMID: 34286524 DOI: 10.16250/j.32.1374.2020254] [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: 11/27/2022]
Abstract
OBJECTIVE To establish an indicator system for assessment of schistosomiasis transmission risk after transmission interruption in Hubei Province, so as to provide insights into the precise control of schistosomiasis. METHODS The indicator system was preliminarily established based on data collection, literature review, expert interviews. Two rounds of expert consultation were performed. The indicator system was screened based on the importance, operability, sensitivity and comprehensive score of the indicators, and the weights of each indicator were calculated. The credibility of the Delphi method was evaluated by calculating the active coefficient of the experts, degree of expert authority and coordination levels of experts' opinions. RESULTS An indicator system for assessment of schistosomiasis transmission risk was preliminarily established, including 3 primary indicators, 12 secondary indicators and 44 tertiary indicators. A Delphi consultation was performed among 17 experts participating in schistosomiasis control, management and research. Following two rounds of consultation, a risk assessment indicator system was finally constructed, including 3 primary indicators, 10 secondary indicators and 35 tertiary indicators. Among the primary indicators, the variable with the highest normalized weight was the current status of schistosomiasis (0.420 2), followed by social factors (0.397 3) and natural environments (0.182 5). Among the secondary indicators, those with high combined weights included risk monitoring (0.142 3), current snail status (0.140 1), and current prevalence of human and livestock infections (0.137 8). Among the tertiary indicators, those with high combined weights included the positive rate of wild feces (0.049 8), the prevalence of snail infections (0.047 4), and the area of snail habitats submerged by floods (0.046 8). During the two-round consultation, the active coefficients of the experts were 85.00% and 100.00%, the degree of expert authority was both 0.75 and greater, and the coordination levels of experts' opinions were 0.405 to 0.521 and 0.592 to 0.695 (all P values < 0.05). CONCLUSIONS An indicator system for assessment of schistosomiasis transmission risk is successfully established after transmission interruption in Hubei Province based on the Delphi method, which provides insights into the identification of the schistosomiasis transmission risk and the targets for schistosomiasis control in Hubei Province.
Collapse
Affiliation(s)
- Y Y Chen
- Hubei Center for Disease Control and Prevention, Wuhan 430079, China
| | - J B Liu
- Hubei Center for Disease Control and Prevention, Wuhan 430079, China
| | - C H Zhong
- Hubei Center for Disease Control and Prevention, Wuhan 430079, China
| | - Y Xiao
- Hubei Center for Disease Control and Prevention, Wuhan 430079, China
| | - F H Wei
- Hubei Center for Disease Control and Prevention, Wuhan 430079, China
| | - J J Yang
- Hubei Center for Disease Control and Prevention, Wuhan 430079, China
| | - W H Zhang
- Hubei Center for Disease Control and Prevention, Wuhan 430079, China
| | - S Liu
- Hubei Center for Disease Control and Prevention, Wuhan 430079, China
| |
Collapse
|
26
|
Sheils T, Mathias SL, Siramshetty VB, Bocci G, Bologa CG, Yang JJ, Waller A, Southall N, Nguyen DT, Oprea TI. How to Illuminate the Druggable Genome Using Pharos. ACTA ACUST UNITED AC 2021; 69:e92. [PMID: 31898878 DOI: 10.1002/cpbi.92] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Pharos is an integrated web-based informatics platform for the analysis of data aggregated by the Illuminating the Druggable Genome (IDG) Knowledge Management Center, an NIH Common Fund initiative. The current version of Pharos (as of October 2019) spans 20,244 proteins in the human proteome, 19,880 disease and phenotype associations, and 226,829 ChEMBL compounds. This resource not only collates and analyzes data from over 60 high-quality resources to generate these types, but also uses text indexing to find less apparent connections between targets, and has recently begun to collaborate with institutions that generate data and resources. Proteins are ranked according to a knowledge-based classification system, which can help researchers to identify less studied "dark" targets that could be potentially further illuminated. This is an important process for both drug discovery and target validation, as more knowledge can accelerate target identification, and previously understudied proteins can serve as novel targets in drug discovery. Two basic protocols illustrate the levels of detail available for targets and several methods of finding targets of interest. An Alternate Protocol illustrates the difference in available knowledge between less and more studied targets. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Search for a target and view details Alternate Protocol: Search for dark target and view details Basic Protocol 2: Filter a target list to get refined results.
Collapse
Affiliation(s)
- Timothy Sheils
- National Center for Advancing Translational Sciences, Rockville, Maryland
| | - Stephen L Mathias
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, New Mexico
| | | | - Giovanni Bocci
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, New Mexico
| | - Cristian G Bologa
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, New Mexico
| | - Jeremy J Yang
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, New Mexico
| | - Anna Waller
- Department of Pathology, University of New Mexico School of Medicine, Albuquerque, New Mexico
| | - Noel Southall
- National Center for Advancing Translational Sciences, Rockville, Maryland
| | - Dac-Trung Nguyen
- National Center for Advancing Translational Sciences, Rockville, Maryland
| | - Tudor I Oprea
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, New Mexico.,UNM Comprehensive Cancer Center, Albuquerque, New Mexico.,Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden.,Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| |
Collapse
|
27
|
Yang JJ, Huang B, Chen XD, Cai LB. [Gastrointestinal leiomyosarcoma with osteoclastic giant cells: report of a case]. Zhonghua Bing Li Xue Za Zhi 2021; 50:527-529. [PMID: 33915666 DOI: 10.3760/cma.j.cn112151-20200914-00712] [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 J Yang
- Department of Pathology, the First People's Hospital, Xiaoshan District of Hangzhou, Zhejiang Province, Hangzhou 311200, China
| | - B Huang
- Department of Pathology, the First People's Hospital, Xiaoshan District of Hangzhou, Zhejiang Province, Hangzhou 311200, China
| | - X D Chen
- Department of Pathology, the First People's Hospital, Xiaoshan District of Hangzhou, Zhejiang Province, Hangzhou 311200, China
| | - L B Cai
- Department of Pathology, the First People's Hospital, Xiaoshan District of Hangzhou, Zhejiang Province, Hangzhou 311200, China
| |
Collapse
|
28
|
KC GB, Bocci G, Verma S, Hassan MM, Holmes J, Yang JJ, Sirimulla S, Oprea TI. A machine learning platform to estimate anti-SARS-CoV-2 activities. NAT MACH INTELL 2021. [DOI: 10.1038/s42256-021-00335-w] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
|
29
|
Ji-Xu A, Lei DK, Nguyen KA, Yang JJ, Erickson MK, Cheng K, Worswick S, Maloney NJ. The burden of immune-mediated skin disease in inpatients with HIV/AIDS. Br J Dermatol 2021; 185:648-650. [PMID: 33887064 DOI: 10.1111/bjd.20401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 03/27/2021] [Accepted: 04/13/2021] [Indexed: 11/26/2022]
Affiliation(s)
- A Ji-Xu
- Department of Dermatology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - D K Lei
- Department of Dermatology, University of Chicago Pritzker School of Medicine, Chicago, IL, USA
| | - K A Nguyen
- Division of Dermatology, Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - J J Yang
- Division of Dermatology, Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - M K Erickson
- Department of Dermatology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - K Cheng
- Division of Dermatology, Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - S Worswick
- Department of Dermatology, Keck Medical School at the University of Southern California, Los Angeles, CA, USA
| | - N J Maloney
- Department of Dermatology, Stanford University, Palo Alto, CA, USA
| |
Collapse
|
30
|
Avram S, Bologa CG, Holmes J, Bocci G, Wilson TB, Nguyen DT, Curpan R, Halip L, Bora A, Yang JJ, Knockel J, Sirimulla S, Ursu O, Oprea TI. DrugCentral 2021 supports drug discovery and repositioning. Nucleic Acids Res 2021; 49:D1160-D1169. [PMID: 33151287 PMCID: PMC7779058 DOI: 10.1093/nar/gkaa997] [Citation(s) in RCA: 90] [Impact Index Per Article: 30.0] [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: 09/17/2020] [Revised: 10/09/2020] [Accepted: 10/14/2020] [Indexed: 12/18/2022] Open
Abstract
DrugCentral is a public resource (http://drugcentral.org) that serves the scientific community by providing up-to-date drug information, as described in previous papers. The current release includes 109 newly approved (October 2018 through March 2020) active pharmaceutical ingredients in the US, Europe, Japan and other countries; and two molecular entities (e.g. mefuparib) of interest for COVID19. New additions include a set of pharmacokinetic properties for ∼1000 drugs, and a sex-based separation of side effects, processed from FAERS (FDA Adverse Event Reporting System); as well as a drug repositioning prioritization scheme based on the market availability and intellectual property rights forFDA approved drugs. In the context of the COVID19 pandemic, we also incorporated REDIAL-2020, a machine learning platform that estimates anti-SARS-CoV-2 activities, as well as the 'drugs in news' feature offers a brief enumeration of the most interesting drugs at the present moment. The full database dump and data files are available for download from the DrugCentral web portal.
Collapse
Affiliation(s)
- Sorin Avram
- Department of Computational Chemistry, “Coriolan Dragulescu’’ Institute of Chemistry, 24 Mihai Viteazu Blvd, Timişoara, Timiş, 300223, România
| | - Cristian G Bologa
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
- UNM Comprehensive Cancer Center, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Jayme Holmes
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Giovanni Bocci
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Thomas B Wilson
- College of Pharmacy, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Dac-Trung Nguyen
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Ramona Curpan
- Department of Computational Chemistry, “Coriolan Dragulescu’’ Institute of Chemistry, 24 Mihai Viteazu Blvd, Timişoara, Timiş, 300223, România
| | - Liliana Halip
- Department of Computational Chemistry, “Coriolan Dragulescu’’ Institute of Chemistry, 24 Mihai Viteazu Blvd, Timişoara, Timiş, 300223, România
| | - Alina Bora
- Department of Computational Chemistry, “Coriolan Dragulescu’’ Institute of Chemistry, 24 Mihai Viteazu Blvd, Timişoara, Timiş, 300223, România
| | - Jeremy J Yang
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Jeffrey Knockel
- Department of Computer Science, University of New Mexico, Albuquerque, NM 87131, USA
| | - Suman Sirimulla
- Department of Pharmaceutical Sciences, School of Pharmacy, The University of Texas at El Paso, TX 79902, USA
| | - Oleg Ursu
- Computational and Structural Chemistry, Merck & Co., Inc., 2000 Galloping Hill Road, Kenilworth, NJ 07033, USA
| | - Tudor I Oprea
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
- Computational and Structural Chemistry, Merck & Co., Inc., 2000 Galloping Hill Road, Kenilworth, NJ 07033, USA
- Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, 40530 Gothenburg, Sweden
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| |
Collapse
|
31
|
Sheils TK, Mathias SL, Kelleher KJ, Siramshetty VB, Nguyen DT, Bologa CG, Jensen LJ, Vidović D, Koleti A, Schürer SC, Waller A, Yang JJ, Holmes J, Bocci G, Southall N, Dharkar P, Mathé E, Simeonov A, Oprea TI. TCRD and Pharos 2021: mining the human proteome for disease biology. Nucleic Acids Res 2021; 49:D1334-D1346. [PMID: 33156327 PMCID: PMC7778974 DOI: 10.1093/nar/gkaa993] [Citation(s) in RCA: 84] [Impact Index Per Article: 28.0] [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: 09/15/2020] [Revised: 10/09/2020] [Accepted: 10/14/2020] [Indexed: 12/13/2022] Open
Abstract
In 2014, the National Institutes of Health (NIH) initiated the Illuminating the Druggable Genome (IDG) program to identify and improve our understanding of poorly characterized proteins that can potentially be modulated using small molecules or biologics. Two resources produced from these efforts are: The Target Central Resource Database (TCRD) (http://juniper.health.unm.edu/tcrd/) and Pharos (https://pharos.nih.gov/), a web interface to browse the TCRD. The ultimate goal of these resources is to highlight and facilitate research into currently understudied proteins, by aggregating a multitude of data sources, and ranking targets based on the amount of data available, and presenting data in machine learning ready format. Since the 2017 release, both TCRD and Pharos have produced two major releases, which have incorporated or expanded an additional 25 data sources. Recently incorporated data types include human and viral-human protein-protein interactions, protein-disease and protein-phenotype associations, and drug-induced gene signatures, among others. These aggregated data have enabled us to generate new visualizations and content sections in Pharos, in order to empower users to find new areas of study in the druggable genome.
Collapse
Affiliation(s)
- Timothy K Sheils
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Stephen L Mathias
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Keith J Kelleher
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Vishal B Siramshetty
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Dac-Trung Nguyen
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Cristian G Bologa
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Dušica Vidović
- Institute for Data Science and Computing, University of Miami, Coral Gables, FL 33146, USA
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Amar Koleti
- Institute for Data Science and Computing, University of Miami, Coral Gables, FL 33146, USA
| | - Stephan C Schürer
- Institute for Data Science and Computing, University of Miami, Coral Gables, FL 33146, USA
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Anna Waller
- UNM Center for Molecular Discovery, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Jeremy J Yang
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Jayme Holmes
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Giovanni Bocci
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Noel Southall
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Poorva Dharkar
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Ewy Mathé
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Anton Simeonov
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Tudor I Oprea
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
- UNM Comprehensive Cancer Center, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
- Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, 40530 Gothenburg, Sweden
| |
Collapse
|
32
|
Chen Y, Liu L, Xing YY, Li Q, Zhao GH, Lu YY, Yang JJ. Down-regulation of miR-365 suppresses cerebral ischemia injury by targeting IGF1R. J BIOL REG HOMEOS AG 2020; 34:1857-1862. [PMID: 33103413 DOI: 10.23812/20-321-l] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Y Chen
- Department of Geriatrics, Shandong Provincial Third Hospital, Jinan, China
| | - L Liu
- Department of Neurology, Shandong Provincial Third Hospital, Jinan, China
| | - Y Y Xing
- Department of Neurology, Shandong Provincial Third Hospital, Jinan, China
| | - Q Li
- Department of Neurology, Shandong Provincial Third Hospital, Jinan, China
| | - G H Zhao
- Department of Neurology, Shandong Provincial Third Hospital, Jinan, China
| | - Y Y Lu
- Department of Neurology, Shandong Provincial Third Hospital, Jinan, China
| | - J J Yang
- Department of General practice, Shandong Provincial Third Hospital, Jinan, China
| |
Collapse
|
33
|
Ling KJ, Wang YZ, Zhang H, Zhang XY, Yang JJ, Luo CY, Song B, Zhang WX, Deng L, Chen GL, Li YD, Hu QY, Chen Y, Wang X, Zhang J, Ding JX, Ren T, Kang S, Hua KQ, Xiang Y, Cheng WW, Liang ZQ. [Oncologic outcomes of early stage cervical cancer performed operation by different laparoscopic surgical procedures: analysis of clinical data from mutiple centers]. Zhonghua Fu Chan Ke Za Zhi 2020; 55:617-623. [PMID: 32957750 DOI: 10.3760/cma.j.cn112141-20200803-00623] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [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 oncologic outcomes of different laparoscopic radical hysterectomy. Methods: From January 2011 to December 2014, the laparoscopic operation cases of cervical cancer at stage Ⅰb1, Ⅰb2, Ⅱa1 and Ⅱa2, including the histologic subtypes of squamous-cell carcinoma, adenocarcinoma and adenosquamous carcinoma, were collected in five clinical centers. The data were divided into two groups according to the surgical procedures, that is, modified laparoscopic-vaginal radical hysterectomy (mLVRH) and total laparoscopic radical hysterectomy (TLRH). The overall survival rate (OS), disease-free survival rate (DFS) at 5 years were retrospectively analyzed in this study. Results: There were 674 cases in total, including 377 cases of mLVRH, 297 cases of TLRH. (1) The OS at 5 years: the mLVRH was 96.1% and the TLRH was 92.0%, and the mLVRH was higher than that of TLRH (P=0.010). Stratify analysis, including stage of disease (Ⅰb1 and Ⅱa1), histologic subtypes (squamous-cell carcinoma, adenocarcinoma), lymph node metastasis, revealed that, ① Stage of disease: in stage Ⅰb1, the OS at five years of mLVRH was higher than that in TLRH group (98.6% vs 93.6%, P=0.012). In stage Ⅱa1, there was significant difference between the two groups, the OS at five years of mLVRH and TLRH were 93.6% and 77.6% (P=0.007). ② Histologic subtypes: for the OS at five years of squamous-cell carcinoma, mLVRH and TLRH were 96.1% and 92.3%, and there was significant difference (P=0.046); for adenocarcinoma, the OS at five years were 91.0% and 88.6%, and there was no difference between two groups (P=0.230). ③ Lymph node metastasis: the mLVRH and TLRH with lymph node metastasis, the OS at five years were 98.6% and 96.4%; the mLVRH and TLRH without lymph node metastasis, the OS at five years were 89.3% and 80.8%. There were no significant differences between the two groups,respectively (P=0.156, P=0.093). (2) The DFS at 5 years: there was no significant difference between mLVRH and TLRH (94.1% vs 90.9%, P=0.220). Stratify analysis for stage of disease, the mLVRH group was higher than that in the TLRH group in stage Ⅰb1 (97.0% vs 92.8%, P=0.039). However, for stage Ⅱa1, there was no significant difference between mLVRH and TLRH group (88.2% vs 75.8%, P=0.074). Conclusions: The results of this retrospective study indicated that different laparoscopy surgical procedures had diverse oncologic outcomes. The OS at 5 years of the mLVRH is superior to the TLRH. The DFS at 5 years in Ⅰb1 stage, the mLVRH is higher than the TLRH. Therefore, the modified laparoscopy is still an alternative surgery for early cervical cancer patients when following the principle of no-tumor-exposure.
Collapse
Affiliation(s)
- K J Ling
- Department of Obstetrics and Gynecology, the First Hospital Affiliated to Army Medical University, Chongqing 400038, China
| | - Y Z Wang
- Department of Obstetrics and Gynecology, the First Hospital Affiliated to Army Medical University, Chongqing 400038, China
| | - H Zhang
- Department of Gynecology, the Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China
| | - X Y Zhang
- Department of Gynecology, the Obstetrics and Gynecology Hospital of Fudan University, Shanghai 200011, China
| | - J J Yang
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - C Y Luo
- Department of Gynecology, the Frist Affiliated Hospital with Nanjing Medical University, Nanjing 210029, China
| | - B Song
- Department of Obstetrics and Gynecology, the First Hospital Affiliated to Army Medical University, Chongqing 400038, China
| | - W X Zhang
- Department of Obstetrics and Gynecology, the First Hospital Affiliated to Army Medical University, Chongqing 400038, China
| | - L Deng
- Department of Obstetrics and Gynecology, the First Hospital Affiliated to Army Medical University, Chongqing 400038, China
| | - G L Chen
- Department of Obstetrics and Gynecology, the First Hospital Affiliated to Army Medical University, Chongqing 400038, China
| | - Y D Li
- Department of Obstetrics and Gynecology, the First Hospital Affiliated to Army Medical University, Chongqing 400038, China
| | - Q Y Hu
- Department of Obstetrics and Gynecology, the First Hospital Affiliated to Army Medical University, Chongqing 400038, China
| | - Y Chen
- Department of Obstetrics and Gynecology, the First Hospital Affiliated to Army Medical University, Chongqing 400038, China
| | - X Wang
- Department of Obstetrics and Gynecology, the First Hospital Affiliated to Army Medical University, Chongqing 400038, China
| | - J Zhang
- Department of Gynecology, the Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China
| | - J X Ding
- Department of Gynecology, the Obstetrics and Gynecology Hospital of Fudan University, Shanghai 200011, China
| | - T Ren
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - S Kang
- Department of Gynecology, the Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China
| | - K Q Hua
- Department of Gynecology, the Obstetrics and Gynecology Hospital of Fudan University, Shanghai 200011, China
| | - Y Xiang
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - W W Cheng
- Department of Gynecology, the Frist Affiliated Hospital with Nanjing Medical University, Nanjing 210029, China
| | - Z Q Liang
- Department of Obstetrics and Gynecology, the First Hospital Affiliated to Army Medical University, Chongqing 400038, China
| |
Collapse
|
34
|
Govinda KC, Bocci G, Verma S, Hassan M, Holmes J, Yang JJ, Sirimulla S, Oprea TI. REDIAL-2020: A suite of machine learning models to estimate Anti-SARS-CoV-2 activities. ChemRxiv 2020:12915779. [PMID: 33200119 PMCID: PMC7668752 DOI: 10.26434/chemrxiv.12915779] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Revised: 09/16/2020] [Indexed: 11/09/2022]
Abstract
Strategies for drug discovery and repositioning are an urgent need with respect to COVID-19. We developed "REDIAL-2020", a suite of machine learning models for estimating small molecule activity from molecular structure, for a range of SARS-CoV-2 related assays. Each classifier is based on three distinct types of descriptors (fingerprint, physicochemical, and pharmacophore) for parallel model development. These models were trained using high throughput screening data from the NCATS COVID19 portal (https://opendata.ncats.nih.gov/covid19/index.html), with multiple categorical machine learning algorithms. The "best models" are combined in an ensemble consensus predictor that outperforms single models where external validation is available. This suite of machine learning models is available through the DrugCentral web portal (http://drugcentral.org/Redial). Acceptable input formats are: drug name, PubChem CID, or SMILES; the output is an estimate of anti-SARS-CoV-2 activities. The web application reports estimated activity across three areas (viral entry, viral replication, and live virus infectivity) spanning six independent models, followed by a similarity search that displays the most similar molecules to the query among experimentally determined data. The ML models have 60% to 74% external predictivity, based on three separate datasets. Complementing the NCATS COVID19 portal, REDIAL-2020 can serve as a rapid online tool for identifying active molecules for COVID-19 treatment. The source code and specific models are available through Github (https://github.com/sirimullalab/redial-2020), or via Docker Hub (https://hub.docker.com/r/sirimullalab/redial-2020) for users preferring a containerized version.
Collapse
Affiliation(s)
- KC Govinda
- Computational Science Program, The University of Texas at El Paso, Texas 79968, USA
- Department of Pharmaceutical Sciences, School of Pharmacy, The University of Texas at El Paso, Texas 79902, USA
| | - Giovanni Bocci
- Translational Informatics Division, Department of Internal Medicine; University of New Mexico Health Sciences Center, Albuquerque, NM, USA
| | - Srijan Verma
- Department of Pharmaceutical Sciences, School of Pharmacy, The University of Texas at El Paso, Texas 79902, USA
- Department of Pharmacy, Birla Institute of Technology and Science, Pilani, Pilani Campus, Rajasthan, 333031, India
| | - Mahmudulla Hassan
- Department of Computer Science, The University of Texas at El Paso, Texas 79968, USA
| | - Jayme Holmes
- Translational Informatics Division, Department of Internal Medicine; University of New Mexico Health Sciences Center, Albuquerque, NM, USA
| | - Jeremy J. Yang
- Translational Informatics Division, Department of Internal Medicine; University of New Mexico Health Sciences Center, Albuquerque, NM, USA
| | - Suman Sirimulla
- Computational Science Program, The University of Texas at El Paso, Texas 79968, USA
- Department of Pharmaceutical Sciences, School of Pharmacy, The University of Texas at El Paso, Texas 79902, USA
- Department of Computer Science, The University of Texas at El Paso, Texas 79968, USA
| | - Tudor I. Oprea
- Translational Informatics Division, Department of Internal Medicine; University of New Mexico Health Sciences Center, Albuquerque, NM, USA
- Autophagy Inflammation and Metabolism Center of Biomedical Research Excellence, University of New Mexico Health Sciences Center, Albuquerque, NM, USA
- Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| |
Collapse
|
35
|
Cheng HY, Yang JJ, Zhao J, Ren T, Feng FZ, Wan XR, Xiang Y. [Preliminary study of PD-1 inhibitor in the treatment of drug-resistant recurrent gestational trophoblastic neoplasia]. Zhonghua Fu Chan Ke Za Zhi 2020; 55:390-394. [PMID: 32842245 DOI: 10.3760/cma.j.cn112141-20191121-00636] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [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 investigate the therapeutic effect of programmed cell death receptor 1 (PD-1) inhibitor in drug-resistant recurrent gestational trophoblastic neoplasia (GTN). Methods: Clinicopathological features, previous treatments, PD-1 inhibitor treatment and prognosis of 8 patients with drug-resistant recurrent GTN treated with PD-1 inhibitor pembrolizumab, in Peking Union Medical College Hospital of Chinese Academy of Medical Sciences from August 2018 to June 2019 were collected and retrospectively analyzed. Results: (1) Clinicopathological features: the average age of onset of 8 GTN patients was 32.9 years old (31-39 years old); pathological types: choriocarcinoma in 7 cases, epithelioid trophoblastic tumor in 1 case. International Federation of Gynecology and Obstetrics (FIGO) stages: stage Ⅲ in 5 cases, stage Ⅳ in 3 cases; FIGO score: 4 patients with 7-12 points (high-risk group) and 4 patients with ≥13 points (ultra high-risk group). All the 8 patients had lung metastasis, 2 patients with brain metastasis, 1 patient with kidney and 1 patient with intestinal metastasis. (2) Previous treatments: ① Chemotherapy: 8 patients with GTN received an average of 21.1 courses (5-30 courses) of chemotherapy; the main route was systemic intravenous chemotherapy. One patient received intrathecal methotrexate chemotherapy due to brain metastasis. ② Surgery: 7 of 8 patients with GTN received surgical treatment, including 5 cases of pelvic surgury, 6 cases of pulmonary lobectomy and 1 case of right hemicolectomy. ③ Radiotherapy: 2 of 8 patients with GTN received radiotherapy, among which 1 patient received radiotherapy for lung for 8 times due to lung metastasis, and the other one received radiotherapy for lung, right sacroiliac joint and skull for a total of 22 times. (3) PD-1 inhibitor treatment: 8 patients with GTN received PD-1 inhibitor treatment with a mean course of 9 (2-12 courses). Six patients appeared Ⅰ-Ⅱ grade of immune related adverse events (AE), and no severe AE occurred. (4) Prognosis: after 2-3 courses of PD-1 inhibitor treatment, serum β-hCG level came to normalization in 4 patients. They were followed up for 2-7 months without any recurrence after 0-9 courses of consolidation treatment. One patient received 12 courses of PD-1 inhibitor treatment. The serum β-hCG level normalized after the 6th courses but increased 1 months later, and then received bevacizumab treatment due to the progression of the disease. The remaining 3 patients received other chemotherapy regiments due to disease progression during PD-1 inhibitor treatment. Conclusions: PD-1 could be used as a remedial treatment for drug-resistant recurrent GTN, with a high effective rate and relatively mild AE. However, more cases need to be accumulated clinically and efficacy should be comprehensively evaluated in combination with pathology and immunohistochemical examination.
Collapse
Affiliation(s)
- H Y Cheng
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - J J Yang
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - J Zhao
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - T Ren
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - F Z Feng
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - X R Wan
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Y Xiang
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China
| |
Collapse
|
36
|
Sass PM, Ge W, Yan J, Obeysekera D, Yang JJ, Wu W. Magnetic Imaging of Domain Walls in the Antiferromagnetic Topological Insulator MnBi 2Te 4. Nano Lett 2020; 20:2609-2614. [PMID: 32119560 DOI: 10.1021/acs.nanolett.0c00114] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The control of domain walls or spin textures is crucial for spintronic applications of antiferromagnets. Despite many efforts, it has been challenging to directly visualize antiferromagnetic domains or domain walls with nanoscale resolution, especially in magnetic field. Here, we report magnetic imaging of domain walls in several uniaxial antiferromagnets, the topological insulator MnBi2Te4 family, using cryogenic magnetic force microscopy (MFM). Our MFM results reveal higher magnetic susceptibility inside the domain walls than in domains. Domain walls in these antiferromagnets form randomly with strong thermal and magnetic field dependence. The direct visualization of these domain walls and domain structures in the magnetic field will not only facilitate the exploration of intrinsic topological phenomena in antiferromagnetic topological insulators but will also open a new path toward control and manipulation of domain walls or spin textures in functional antiferromagnets.
Collapse
Affiliation(s)
- Paul M Sass
- Department of Physics and Astronomy, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Wenbo Ge
- Department of Physics and Astronomy, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Jiaqiang Yan
- Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - D Obeysekera
- Department of Physics, New Jersey Institute of Technology, Newark, 07102 United States
| | - J J Yang
- Department of Physics, New Jersey Institute of Technology, Newark, 07102 United States
| | - Weida Wu
- Department of Physics and Astronomy, Rutgers University, Piscataway, New Jersey 08854, United States
| |
Collapse
|
37
|
Gou FX, Zhang XS, Yao JX, Yu DS, Wei KF, Zhang H, Yang XT, Yang JJ, Liu HX, Cheng Y, Jiang XJ, Zheng YH, Wu B, Liu XF, Li H. [Epidemiological characteristics of COVID-19 in Gansu province]. Zhonghua Liu Xing Bing Xue Za Zhi 2020; 41:E032. [PMID: 32234127 DOI: 10.3760/cma.j.cn112338-20200229-00216] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To understand the epidemiological characteristics of COVID-19 cases in different epidemic stages in Gansu province. Methods: Epidemiological investigation was conducted to collect the information of confirmed COVID-19 cases, including demographic, epidemiological and clinical information. Results: As of 25 February 2020, a total of 91 confirmed COVID-19 cases had been reported in Gansu. The epidemic of COVID-19 in Gansu can be divided as three different stages, i.e. imported case stage, imported-case plus indigenous case stage, and indigenous case stage. A total of 63 cases were clustered cases (69.23%), 3 cases were medical staff infected with non-occupational exposure. The initial symptoms included fever (54.95%, 50/91), cough (52.75%, 48/91), or fatigue (28.57%, 26/91), the proportion of each symptom showed a decreasing trend along with the three epidemic stages, but only the differences in proportions of fever (trend χ2=2.20, P<0.05) and fatigue (trend χ2=3.18, P<0.05) among the three epidemic stages were statistically significant. The cases with critical severe symptoms accounted for 42.85% (6/14), 23.73% (14/59) and 16.67% (3/18), respectively, in three epidemic stages, showed a decreasing trend (H=6.45, P<0.05). Also, the incubation period prolonged along with the epidemic stage (F=51.65, P<0.01), but the intervals between disease onset and hospital visit (F=5.32, P<0.01), disease onset and diagnosis (F=5.25, P<0.01) became shorter along with the epidemic stage. Additionally, the basic reproduction number (R0) had decreased from 2.61 in imported case stage to 0.66 in indigenous case stage. Conclusions: The COVID-19 epidemic in Gansu was caused by the imported cases, and about 2/3 cases were clustered ones. No medical worker was observed to be infected by occupational exposure. With the progression of COVID-19 epidemic in Gansu, the change in initial symptom and incubation period suggests, the early screening cannot only depend on body temperature monitoring.
Collapse
Affiliation(s)
- F X Gou
- Gansu Provincial Center for Disease Control and Prevention, Lanzhou 730000, China
| | - X S Zhang
- Gansu Provincial Center for Disease Control and Prevention, Lanzhou 730000, China
| | - J X Yao
- Gansu Provincial Center for Disease Control and Prevention, Lanzhou 730000, China
| | - D S Yu
- Gansu Provincial Center for Disease Control and Prevention, Lanzhou 730000, China
| | - K F Wei
- Gansu Provincial Center for Disease Control and Prevention, Lanzhou 730000, China
| | - H Zhang
- Gansu Provincial Center for Disease Control and Prevention, Lanzhou 730000, China
| | - X T Yang
- Gansu Provincial Center for Disease Control and Prevention, Lanzhou 730000, China
| | - J J Yang
- Gansu Provincial Center for Disease Control and Prevention, Lanzhou 730000, China
| | - H X Liu
- Gansu Provincial Center for Disease Control and Prevention, Lanzhou 730000, China
| | - Y Cheng
- Gansu Provincial Center for Disease Control and Prevention, Lanzhou 730000, China
| | - X J Jiang
- Gansu Provincial Center for Disease Control and Prevention, Lanzhou 730000, China
| | - Y H Zheng
- Gansu Provincial Center for Disease Control and Prevention, Lanzhou 730000, China
| | - B Wu
- Gansu Provincial Center for Disease Control and Prevention, Lanzhou 730000, China
| | - X F Liu
- Gansu Provincial Center for Disease Control and Prevention, Lanzhou 730000, China
| | - H Li
- Gansu Provincial Center for Disease Control and Prevention, Lanzhou 730000, China
| |
Collapse
|
38
|
Yang JJ, Han Y, Mah CH, Wanjaya E, Peng B, Xu TF, Liu M, Huan T, Fang ML. Streamlined MRM method transfer between instruments assisted with HRMS matching and retention-time prediction. Anal Chim Acta 2019; 1100:88-96. [PMID: 31987156 DOI: 10.1016/j.aca.2019.12.002] [Citation(s) in RCA: 5] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 11/17/2019] [Accepted: 12/01/2019] [Indexed: 01/16/2023]
Abstract
Multiple reaction monitoring (MRM) mode using liquid-chromatography tandem mass spectrometry (e.g., LC-QqQ-MS/MS) has been extensively employed in the small molecule analysis with trace levels in complex samples owing to its high sensitivity. However, most of the reported MRM methods are developed using authentic standards, which are often costly yet not readily available. To address this question, a practical platform for the MRM method transfer between different LC-QqQ-MS/MS instruments, assisted by the high-resolution mass spectrometry (LC-HRMS) and retention time (RT) prediction, has been developed in this study. The reported platform can take advantage of both the high sensitivity of LC-MRM method and ion transition pairs from the previous publications. LC-HRMS can provide the accurate mass measurement of the compounds, though high-quality MS/MS fragments are usually difficult to obtain for chemicals at trace levels. Retention time matching and peaks matching between both instrumental platforms rule out isobaric candidates. With an additional retention time prediction filter from quantitative structure retention relationship (QSRR) model based on random forest feature selection (Pearson r2 = 0.63), identification of small molecules is achieved at a high confidence level without using authentic standards. The developed platform has been validated with robustness by examining spiked environmental chemicals in sludge water samples, biological urine, and cell extracts.
Collapse
Affiliation(s)
- J J Yang
- School of Civil and Environmental Engineering, Nanyang Technological University, 639798, Singapore; Environmental Chemistry and Materials Centre, Nanyang Environment and Water Research Institute, Nanyang Technological University, 637141, Singapore
| | - Y Han
- Nanyang Environment and Water Research Institute, Nanyang Technological University, 637141, Singapore
| | - C H Mah
- Natural Sciences and Science Education, National Institute of Education, Nanyang Technological University, 637616, Singapore
| | - E Wanjaya
- Nanyang Environment and Water Research Institute, Nanyang Technological University, 637141, Singapore
| | - B Peng
- School of Civil and Environmental Engineering, Nanyang Technological University, 639798, Singapore; Nanyang Environment and Water Research Institute, Nanyang Technological University, 637141, Singapore
| | - T F Xu
- School of Civil and Environmental Engineering, Nanyang Technological University, 639798, Singapore; Nanyang Environment and Water Research Institute, Nanyang Technological University, 637141, Singapore
| | - M Liu
- School of Civil and Environmental Engineering, Nanyang Technological University, 639798, Singapore; Nanyang Environment and Water Research Institute, Nanyang Technological University, 637141, Singapore
| | - T Huan
- Department of Chemistry, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, BC, V6T 1Z1, Canada
| | - M L Fang
- School of Civil and Environmental Engineering, Nanyang Technological University, 639798, Singapore; Nanyang Environment and Water Research Institute, Nanyang Technological University, 637141, Singapore.
| |
Collapse
|
39
|
Li YS, Jiang BY, Yang JJ, Zhang XC, Zhang Z, Ye JY, Zhong WZ, Tu HY, Chen HJ, Wang Z, Xu CR, Wang BC, Du HJ, Chuai S, Han-Zhang H, Su J, Zhou Q, Yang XN, Guo WB, Yan HH, Liu YH, Yan LX, Huang B, Zheng MM, Wu YL. Unique genetic profiles from cerebrospinal fluid cell-free DNA in leptomeningeal metastases of EGFR-mutant non-small-cell lung cancer: a new medium of liquid biopsy. Ann Oncol 2019; 29:945-952. [PMID: 29346604 DOI: 10.1093/annonc/mdy009] [Citation(s) in RCA: 180] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Background Leptomeningeal metastases (LM) are more frequent in non-small-cell lung cancer (NSCLC) with epidermal growth factor receptor (EGFR) mutations. Due to limited access to leptomeningeal lesions, the purpose of this study was to explore the potential role of cerebrospinal fluid (CSF) as a source of liquid biopsy in patients with LM. Patients and methods Primary tumor, CSF, and plasma in NSCLC with LM were tested by next-generation sequencing. In total, 45 patients with suspected LM underwent lumbar puncture, and those with EGFR mutations diagnosed with LM were enrolled. Results A total of 28 patients were enrolled in this cohort; CSF and plasma were available in 26 patients, respectively. Driver genes were detected in 100% (26/26), 84.6% (22/26), and 73.1% (19/26) of samples comprising CSF cell-free DNA (cfDNA), CSF precipitates, and plasma, respectively; 92.3% (24/26) of patients had much higher allele fractions in CSF cfDNA than the other two media. Unique genetic profiles were captured in CSF cfDNA compared with those in plasma and primary tissue. Multiple copy number variations (CNVs) were mainly identified in CSF cfDNA, and MET copy number gain identified in 47.8% (11/23) of patients was the most frequent one, while other CNVs included ERBB2, KRAS, ALK, and MYC. Moreover, loss of heterozygosity (LOH) of TP53 was identified in 73.1% (19/26) CSF cfDNA, which was much higher than that in plasma (2/26, 7.7%; P < 0.001). There was a trend towards a higher frequency of concomitant resistance mutations in patients with TP53 LOH than those without (70.6% versus 33.3%; P = 0.162). EGFR T790M was identified in CSF cfDNA of 30.4% (7/23) of patients who experienced TKI progression. Conclusion CSF cfDNA could reveal the unique genetic profiles of LM and should be considered as the most representative liquid biopsy medium for LM in EGFR-mutant NSCLC.
Collapse
Affiliation(s)
- Y S Li
- Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cance, Guangdong Lung Cancer Institute, Guangdong General Hospital & Guangdong Academy of Medical Sciences, Guangzhou, China
| | - B Y Jiang
- Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cance, Guangdong Lung Cancer Institute, Guangdong General Hospital & Guangdong Academy of Medical Sciences, Guangzhou, China
| | - J J Yang
- Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cance, Guangdong Lung Cancer Institute, Guangdong General Hospital & Guangdong Academy of Medical Sciences, Guangzhou, China
| | - X C Zhang
- Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cance, Guangdong Lung Cancer Institute, Guangdong General Hospital & Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Z Zhang
- Burning Rock Biotech, Guangzhou, China
| | - J Y Ye
- Burning Rock Biotech, Guangzhou, China
| | - W Z Zhong
- Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cance, Guangdong Lung Cancer Institute, Guangdong General Hospital & Guangdong Academy of Medical Sciences, Guangzhou, China
| | - H Y Tu
- Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cance, Guangdong Lung Cancer Institute, Guangdong General Hospital & Guangdong Academy of Medical Sciences, Guangzhou, China
| | - H J Chen
- Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cance, Guangdong Lung Cancer Institute, Guangdong General Hospital & Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Z Wang
- Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cance, Guangdong Lung Cancer Institute, Guangdong General Hospital & Guangdong Academy of Medical Sciences, Guangzhou, China
| | - C R Xu
- Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cance, Guangdong Lung Cancer Institute, Guangdong General Hospital & Guangdong Academy of Medical Sciences, Guangzhou, China
| | - B C Wang
- Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cance, Guangdong Lung Cancer Institute, Guangdong General Hospital & Guangdong Academy of Medical Sciences, Guangzhou, China
| | - H J Du
- Department of Pulmonology, General Hospital of Guangzhou Military Command, Guangzhou, China
| | - S Chuai
- Burning Rock Biotech, Guangzhou, China
| | | | - J Su
- Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cance, Guangdong Lung Cancer Institute, Guangdong General Hospital & Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Q Zhou
- Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cance, Guangdong Lung Cancer Institute, Guangdong General Hospital & Guangdong Academy of Medical Sciences, Guangzhou, China
| | - X N Yang
- Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cance, Guangdong Lung Cancer Institute, Guangdong General Hospital & Guangdong Academy of Medical Sciences, Guangzhou, China
| | - W B Guo
- Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cance, Guangdong Lung Cancer Institute, Guangdong General Hospital & Guangdong Academy of Medical Sciences, Guangzhou, China
| | - H H Yan
- Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cance, Guangdong Lung Cancer Institute, Guangdong General Hospital & Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Y H Liu
- Department of Pathology, Guangdong General Hospital & Guangdong Academy of Medical Sciences, Guangzhou, China
| | - L X Yan
- Department of Pathology, Guangdong General Hospital & Guangdong Academy of Medical Sciences, Guangzhou, China
| | - B Huang
- Department of Radiology, Guangdong General Hospital & Guangdong Academy of Medical Sciences, Guangzhou, China
| | - M M Zheng
- Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cance, Guangdong Lung Cancer Institute, Guangdong General Hospital & Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Y L Wu
- Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cance, Guangdong Lung Cancer Institute, Guangdong General Hospital & Guangdong Academy of Medical Sciences, Guangzhou, China.
| |
Collapse
|
40
|
Yang JJ, Sang W, Xu KL. [Research progress of CAR-T cell therapy for relapsed/refractory diffuse large B-cell lymphoma]. Zhonghua Nei Ke Za Zhi 2019; 58:849-852. [PMID: 31665867 DOI: 10.3760/cma.j.issn.0578-1426.2019.11.017] [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 J Yang
- Department of Hematology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou 221002, China
| | | | | |
Collapse
|
41
|
Wang GZ, He XH, Wang Y, Xu LC, Huang HZ, Wang YH, Shen Z, Qu XD, Ding XY, Yang JJ, Li WT. Clinical practice guideline for image-guided multimode tumour ablation therapy in hepatic malignant tumours. ACTA ACUST UNITED AC 2019; 26:e658-e664. [PMID: 31708659 DOI: 10.3747/co.26.5423] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Multimode tumour ablation therapy is a treatment method that combines cryoablation with radiofrequency ablation, guided by medical imaging technology and based on precise planning, targeting, monitoring, and control of the thermal energy delivered, with the aim of achieving a whole-body antitumour immune response to malignant tumours. To develop standardized criteria for the application of multimode tumour ablation therapy to malignant hepatic tumours, to facilitate actualization of the criteria in various hospitals, and to ensure therapeutic efficacy and safety, the Society of Interventional Therapy of the Chinese Anti-Cancer Association and the Solid Tumor Theranostics Committee of the Shanghai Anti-Cancer Association assembled experts who specialize in oncology to discuss this treatment method and to arrive at a clinical practice consensus guideline for the indications, contraindications, and techniques of multimode tumour ablation therapy for malignant hepatic tumours.
Collapse
Affiliation(s)
- G Z Wang
- Department of Interventional Oncology, Fudan University Shanghai Cancer Center, Shanghai, P.R.C
| | - X H He
- Department of Interventional Oncology, Fudan University Shanghai Cancer Center, Shanghai, P.R.C
| | - Y Wang
- Department of Interventional Oncology, Fudan University Shanghai Cancer Center, Shanghai, P.R.C
| | - L C Xu
- Department of Interventional Oncology, Fudan University Shanghai Cancer Center, Shanghai, P.R.C
| | - H Z Huang
- Department of Interventional Oncology, Fudan University Shanghai Cancer Center, Shanghai, P.R.C
| | - Y H Wang
- Department of Interventional Oncology, Fudan University Shanghai Cancer Center, Shanghai, P.R.C
| | - Z Shen
- Department of Oncology, Affiliated Shanghai 6th People's Hospital, Shanghai Jiaotong University, Shanghai, P.R.C
| | - X D Qu
- Department of Interventional Radiology, Zhongshan Hospital, Fudan University, Shanghai, P.R.C
| | - X Y Ding
- Department of Interventional Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, P.R.C
| | - J J Yang
- Department of Interventional Radiology, Changhai Hospital, Naval Medical University, Shanghai, P.R.C
| | - W T Li
- Department of Interventional Oncology, Fudan University Shanghai Cancer Center, Shanghai, P.R.C
| |
Collapse
|
42
|
Song T, Liu JY, Yang JJ. NKAP plays an oncogenic function partly through AKT signaling pathway in hepatocellular carcinoma. Neoplasma 2019; 66:792-800. [PMID: 31305121 DOI: 10.4149/neo_2018_181212n957] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Accepted: 06/05/2019] [Indexed: 11/08/2022]
Abstract
NF-kB activating protein (NKAP) is a highly conserved protein involved in transcriptional repression, immune cell development, maturation, T cell acquisition of functional competency and maintenance of hematopoiesis. Here we first explore the function of NKAP in hepatocellular carcinoma (HCC). We found that NKAP was highly expressed in HCC tissues and associated with a poor patient survival. CCK8 assay showed that NKAP knockdown significantly decreased cell viability of HuH7 and Hep3B HCC cell lines. Cell invasion, tested by transwell assays, was significantly inhibited by NKAP knockdown in HuH7 and Hep3B cells (P<0.05). Percentage of cell apoptosis was significantly increased by NKAP knockdown in HuH7 cells (6.5% to 12.5%) and in Hep3B cells (8.3% to 27.3%). Furthermore, western blot results indicated that NKAP silence upregulated the expression of pro-apoptotic proteins Bax and Caspase3-P17 while downregulated anti-apoptotic protein Bcl2. Finaly, AKT signaling pathway was evaluated to reveal the underlying mechanism of NKAP in HCC cells. It was suggested that NKAP knockdown decreased the phosphorylation level of AKT and the expression of its downstream members p70S6K and Cyclin D1. Furthermore, we demonstrated that NKAP knockdown also played an oncogenic role in human gastric cancer AGS and MKN45 cells. In conclusion, for the first time our study reveals that NKAP promotes the proliferation and invasion in HCC cell lines at least partly through AKT signaling pathway.
Collapse
Affiliation(s)
- T Song
- Department of Medical Imaging, Changhai Hospital of Second Military Medical University of Chinese PLA, Shanghai, China
| | - J Y Liu
- Department of Interventional Radiology, Changhai Hospital of Second Military Medical University of Chinese PLA, Shanghai, China
| | - J J Yang
- Department of Interventional Radiology, Changhai Hospital of Second Military Medical University of Chinese PLA, Shanghai, China
| |
Collapse
|
43
|
Wen FF, Xu Z, Liu LP, Yang JJ, Ding SD. [Effect of dopamine on intracerebral glutamate uptake ability in rats with minimal hepatic encephalopathy and the pathogenesis of minimal hepatic encephalopathy]. Zhonghua Gan Zang Bing Za Zhi 2019; 26:48-53. [PMID: 29804362 DOI: 10.3760/cma.j.issn.1007-3418.2018.01.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [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 investigate the effect of dopamine (DA) on the glutamate (Glu) uptake ability of neural cells, as well as its effect on cognitive impairment in rats with minimal hepatic encephalopathy (MHE) via related pathways. Methods: A total of 45 Sprague-Dawley rats were randomly divided into control group, MHE model group, and DA intervention model group, with 15 rats in each group. The rats in the MHE model group were given intraperitoneal injection of thioacetamide (TAA), those in DA intervention model group were given intraventricular injection of DA, and those in the control group were given intraperitoneal injection of physiological saline, with a frequency of twice a week for 8 weeks. Cerebral microdialysis was used to measure the change in the content of Glu in the brain in MHE rats and rats with DA intervention; RT-PCR and Western blotting were used to measure the relative mRNA and protein expression of trace amine-associated receptor 1 (TAAR1) and excitatory amino acid transporter 2 (EAAT2); the changes in the expression of EAAT2 and extracellular Glu level were measured after intracerebroventricular injection of TAAR1 siRNA and TAAR1 plasmid in MHE rats and rats with DA intervention. One- way analyses of variance for comparison among different groups were performed, categorical data between groups were compared using nonparametric tests. Results: Compared with the control group, the MHE model group had significant increases in the content of DA in liver tissue, plasma, and brain tissue (4.90 ± 0.13 ng/g vs 1.20 ± 0.13 ng/g, P < 0.05; 16.32 ± 1.01 pmol/ml vs 5.50 ± 0.82 pmol/ml, P < 0.05; 732.45 ± 78.85 ng/g vs 387.00 ± 23.36 ng/g, P < 0.05). There was a significant increase in the extracellular Glu level within 40-120 minutes after intracerebral injection of DA in the DA intervention model group. Compared with the control group, the MHE model group and the DA intervention model group had a significant increase in the relative protein expression of TAAR1 (3.72 ± 0.50/4.18 ± 0.43 vs 0.96 ± 0.40, both P < 0.05) and a significant reduction in the expression of EAAT2 (0.46 ± 0.16/0.51 ± 0.20 vs 0.92 ± 0.11, P = 0.013 and 0.036). Compared with the model group treated with empty vector, the MHE model group and the DA intervention model group had a significant increase in the relative protein expression of EAAT2 after TAAR1 siRNA intervention (0.86±0.142 vs 0.56 ± 0.060, P = 0.028; 0.99 ± 0.056 vs 0.43 ± 0.098, P = 0.0010) and a significant reduction in the extracellular Glu level in the brain at 60-120 minutes after injection, while after TAAR1 plasmid intervention, the MHE model group and the DA intervention model group had a significant reduction in the relative protein expression of EAAT2 (0.20 ± 0.040 vs 0.48 ± 0.08, P = 0.006; 0.24 ± 0.05 vs 0.54 ± 0.07, P = 0.004) and a significant increase in the extracellular Glu level in brain at 60-100 minutes after injection. Conclusion: DA interacts with TAAR1 in brain tissue to induce extracellular accumulation of Glu, thus leading to the disorder of the TAAR1-EAAT2 signaling pathway in brain tissue and ultimately injuring the cognitive function of MHE rats.
Collapse
Affiliation(s)
- F F Wen
- Zhejiang Provincial Key Laboratory of Aging and Neurological Disease Research, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Z Xu
- Neurosurgery Department, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - L P Liu
- Surgery Laboratory, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - J J Yang
- Neurosurgery Department, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - S D Ding
- Zhejiang Provincial Key Laboratory of Aging and Neurological Disease Research, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| |
Collapse
|
44
|
Ursu O, Holmes J, Bologa CG, Yang JJ, Mathias SL, Stathias V, Nguyen DT, Schürer S, Oprea T. DrugCentral 2018: an update. Nucleic Acids Res 2019; 47:D963-D970. [PMID: 30371892 PMCID: PMC6323925 DOI: 10.1093/nar/gky963] [Citation(s) in RCA: 79] [Impact Index Per Article: 15.8] [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: 09/12/2018] [Revised: 10/02/2018] [Accepted: 10/26/2018] [Indexed: 01/21/2023] Open
Abstract
DrugCentral is a drug information resource (http://drugcentral.org) open to the public since 2016 and previously described in the 2017 Nucleic Acids Research Database issue. Since the 2016 release, 103 new approved drugs were updated. The following new data sources have been included: Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS), FDA Orange Book information, L1000 gene perturbation profile distance/similarity matrices and estimated protonation constants. New and existing entries have been updated with the latest information from scientific literature, drug labels and external databases. The web interface has been updated to display and query new data. The full database dump and data files are available for download from the DrugCentral website.
Collapse
Affiliation(s)
- Oleg Ursu
- Translational Informatics Division, Department of Internal Medicine, The University of New Mexico Health Science Center, Albuquerque, NM 87131, USA
| | - Jayme Holmes
- Translational Informatics Division, Department of Internal Medicine, The University of New Mexico Health Science Center, Albuquerque, NM 87131, USA
| | - Cristian G Bologa
- Translational Informatics Division, Department of Internal Medicine, The University of New Mexico Health Science Center, Albuquerque, NM 87131, USA
| | - Jeremy J Yang
- Translational Informatics Division, Department of Internal Medicine, The University of New Mexico Health Science Center, Albuquerque, NM 87131, USA
| | - Stephen L Mathias
- Translational Informatics Division, Department of Internal Medicine, The University of New Mexico Health Science Center, Albuquerque, NM 87131, USA
| | - Vasileios Stathias
- Center for Computational Science, Miller School of Medicine, University of Miami, Coral Gables, FL 33146, USA
| | - Dac-Trung Nguyen
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Stephan Schürer
- Center for Computational Science, Miller School of Medicine, University of Miami, Coral Gables, FL 33146, USA
| | - Tudor Oprea
- Translational Informatics Division, Department of Internal Medicine, The University of New Mexico Health Science Center, Albuquerque, NM 87131, USA
| |
Collapse
|
45
|
Wang ZY, Zhang W, Yang JJ, Song DK, Wei JX, Gao S. Association of thymosin β4 expression with clinicopathological parameters and clinical outcomes of bladder cancer patients. Neoplasma 2019; 63:991-998. [PMID: 27596300 DOI: 10.4149/neo_2016_619] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The clinical significance of thymosin β4 (Tβ4) expression in bladder transitional cell carcinoma (BTCC) remains unclear. The present study assessed the relationship between the expression of Tβ4 protein and the clinicopathological features, as well as the prognosis of bladder cancer patients. Tβ4 protein expression in 24 normal bladder and 138 primary BTCC tissue specimens was detected by immunohistochemistry, and the association of this expression with BTCC clinicopathological features and recurrence as well as patient survival was analyzed. Tβ4 expression was significantly stronger in BTCC patients than in normal volunteers. The expression of Tβ4 was significantly associated with differentiation capability, tumor stage and lymph node metastasis (P = 0.025, 0.043, and 0.039, respectively). Moreover, Tβ4 expression was positively correlated with integrin-linked kinase (ILK) and β-catenin expression (P = 0.042, 0.031, respectively) and inversely correlated with E-cadherin expression (P = 0.022). In the present cohort of bladder cancer patients, Tβ4 expression was found to be a predictor of poor survival (P < 0.05); however, high Tβ4 expression exhibited unfavorable prognostic value for recurrence. These data suggested that Tβ4 is correlated with the pathogenesis of BTCC. In addition, the patients with higher Tβ4 expression had a shorter survival.
Collapse
|
46
|
Xue W, Yang JJ, Zhao J, Ren T, Feng FZ, Wan XR, Xiang Y. [Impact of chemotherapy on ovarian function and quality of life of patients with gestational trophoblastic neoplasia]. Zhonghua Fu Chan Ke Za Zhi 2018; 53:377-383. [PMID: 29961279 DOI: 10.3760/cma.j.issn.0529-567x.2018.06.004] [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: Using a questionnaire to evaluate different regimens of chemotherapy on ovarian function and quality of life of patients with gestational trophoblastic neoplasia (GTN) . Methods: At least 6 months after completion of chemotherapy, 200 patients with GTN treated in Peking Union Medical College Hospital from January 2010 to June 2017 were randomly selected to fill up the questionnaire. The questionnaire items were included the patient's menstrual cycles, sexual life, gestational issues and common health. The patients were divided into 3 groups by chemotherapy regimens: actinomycin D (Act-D) group, floxuridine+Act-D+vincristine (FAV) or floxuridine+Act-D+etoposide+vincristine (FAEV) group (FAV-FAEV group) , and etoposide+methotrexate+Act-D (EMA) /vincristine+cyclophosphamide (CO) or EMA/ etoposide+cisplatin (EP) group (EMA/CO-EMA/EP group) . Chi-square test was used with a significance level of P-value less than 0.05. Results: One hundred and seventy-three (86.5%,173/200) of the patients completed the questionnaire. Forty three point two percent (43.2%, 19/44) in the EMA/CO-EMA/EP group had a normal menstrual cycle, which were significantly lower than those of Act-D group (84.6%,22/26) and FAV-FAEV group (71.2%, 37/52; all P<0.05) . Amenorrhea rate was also significantly higher in EMA/CO-EMA/EP group (25.0%, 11/44) than in Act-D group (0) and FAV-FAEV group (17.3%, 9/52; all P<0.05) . The sexual life parameters were comparable among 3 groups. Ten out of thirty-two patients conceived after chemotherapy, 2 had miscarriages and 8 had full-term delivery of healthy babies. The common health and labor capacity were significantly decreased after chemotherapy (all P<0.05) . Conclusions: EMA/CO or EMA/EP regimen have a worse impact on ovarian function than Act-D and FAV or FAEV regimen. Gynecologic oncologist should be concerned about the ovarian function and quality of life of GTN patients.
Collapse
Affiliation(s)
- W Xue
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing 100730, China
| | | | | | | | | | | | | |
Collapse
|
47
|
Jiang F, Yang Y, Ji ML, Yang JJ, Zhao J, Ren T, Feng FZ, Wan XR, Xiang Y. [Clinical outcome of patients with gestational trophoblastic neoplasia receiving primary treatment at Peking Union Medical College Hospital: a 30-year retrospective cohort study]. Zhonghua Fu Chan Ke Za Zhi 2018; 53:364-370. [PMID: 29961277 DOI: 10.3760/cma.j.issn.0529-567x.2018.06.002] [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 summarize and analyze the clinical outcomes of gestational trophoblastic neoplasia (GTN) patients receiving primary treatment at Peking Union Medical College Hospital from 1985 to 2015, and investigate the changes in treatment efficacy between the first and the second 15 years. Methods: Clinical data of GTN patient receiving primary chemotherapy at Peking Union Medical College Hospital from January 1985 to December 2015 were retrospectively analyzed. It further compared the therapeutic results and chemotherapy cycles given to GTN patients, according to International Federation of Gynecology and Obstetrics (FIGO, 2000) prognostic score system, who were classified to different stages and low- or high-risk groups. Results: In total, 1 711 GTN patients were included in this study. Comparing the 1985-2000 group and the 2001-2015 group, the results showed that: (1) while the overall complete remission (CR) rate was 93.7% (1 603/1 711) , the CR rate of 2001-2015 group was significantly higher than that of 1985-2000 group [98.4% (1 155/1 174) vs 83.4% (448/537) , χ(2)=139.353, P<0.01]. This difference was significant between stage Ⅲ and Ⅳ patients, but nonexistent between stage Ⅰ and Ⅱ patients, including low- and high-risk groups. (2) The relapse rate of patients who had been in CR was 2.7% (43/1 603) , with no significant differences between the groups of 1985-2001 and 2001-2015 [3.6% (16/448) vs 2.3% (27/1 155) , χ(2)=6.867, P=0.142]. (3) The overall mortality rate was 2.6% (44/1 711) , which significantly decreased in 2001-2015 group compared to 1985-2000 group [1.6% (19/1 174) vs 4.7% (25/537) , χ(2)=13.830, P<0.01]. This difference appeared only in high-risk patients with stage Ⅲ disease (χ(2)=9.505, P<0.01) . (4) Fluorouracil was gradually replaced by floxridine in chemotherapy regimens. The total cycles of chemotherapy regimens given to low-risk patients with stage Ⅲ disease significantly decreased in 2001-2015 group, but no statistical difference was shown with patients at other stages. Moreover, the cycles of consolidation treatment were significantly reduced in patients with stage Ⅲ patients. Conclusions: GTN patients could obtain satisfactory curative results after appropriate and standard treatment. Peking Union Medical College Hospital has achieved better curative effect in the latest 15 years than before.
Collapse
Affiliation(s)
- F Jiang
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing 100730, China
| | | | | | | | | | | | | | | | | |
Collapse
|
48
|
Oprea TI, Bologa CG, Brunak S, Campbell A, Gan GN, Gaulton A, Gomez SM, Guha R, Hersey A, Holmes J, Jadhav A, Jensen LJ, Johnson GL, Karlson A, Leach AR, Ma’ayan A, Malovannaya A, Mani S, Mathias SL, McManus MT, Meehan TF, von Mering C, Muthas D, Nguyen DT, Overington JP, Papadatos G, Qin J, Reich C, Roth BL, Schürer SC, Simeonov A, Sklar LA, Southall N, Tomita S, Tudose I, Ursu O, Vidovic D, Waller A, Westergaard D, Yang JJ, Zahoránszky-Köhalmi G. Unexplored therapeutic opportunities in the human genome. Nat Rev Drug Discov 2018; 17:317-332. [PMID: 29472638 PMCID: PMC6339563 DOI: 10.1038/nrd.2018.14] [Citation(s) in RCA: 204] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
A large proportion of biomedical research and the development of therapeutics is focused on a small fraction of the human genome. In a strategic effort to map the knowledge gaps around proteins encoded by the human genome and to promote the exploration of currently understudied, but potentially druggable, proteins, the US National Institutes of Health launched the Illuminating the Druggable Genome (IDG) initiative in 2014. In this article, we discuss how the systematic collection and processing of a wide array of genomic, proteomic, chemical and disease-related resource data by the IDG Knowledge Management Center have enabled the development of evidence-based criteria for tracking the target development level (TDL) of human proteins, which indicates a substantial knowledge deficit for approximately one out of three proteins in the human proteome. We then present spotlights on the TDL categories as well as key drug target classes, including G protein-coupled receptors, protein kinases and ion channels, which illustrate the nature of the unexplored opportunities for biomedical research and therapeutic development.
Collapse
Affiliation(s)
- Tudor I. Oprea
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
- UNM Comprehensive Cancer Center, Albuquerque, NM, USA
- Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Cristian G. Bologa
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | | | - Anna Gaulton
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Shawn M. Gomez
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, USA
- Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Rajarshi Guha
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, USA
| | - Anne Hersey
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Jayme Holmes
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Ajit Jadhav
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, USA
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Gary L. Johnson
- Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Anneli Karlson
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
- Present addresses: SciBite Limited, BioData Innovation Centre, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Andrew R. Leach
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Avi Ma’ayan
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Subramani Mani
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Stephen L. Mathias
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | | | - Terrence F. Meehan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | | | - Daniel Muthas
- Respiratory, Inflammation and Autoimmunity Diseases, Innovative Medicines and Early Development Biotech Unit, AstraZeneca R&D Gothenburg, Mölndal, Sweden
| | - Dac-Trung Nguyen
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, USA
| | - John P. Overington
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
- Medicines Discovery Catapult, Alderley Edge, UK
| | - George Papadatos
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
- GlaxoSmithKline, Stevenage, UK
| | - Jun Qin
- Baylor College of Medicine, Houston, TX, USA
| | | | - Bryan L. Roth
- Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Stephan C. Schürer
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Anton Simeonov
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, USA
| | - Larry A. Sklar
- UNM Comprehensive Cancer Center, Albuquerque, NM, USA
- Center for Molecular Discovery, University of New Mexico Cancer Center, University of New Mexico, Albuquerque, NM, USA
- Department of Pathology, University of New Mexico, Albuquerque, NM, USA
| | - Noel Southall
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, USA
| | - Susumu Tomita
- Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Ilinca Tudose
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
- Google Germany GmbH, München, Germany
| | - Oleg Ursu
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Dušica Vidovic
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Anna Waller
- Center for Molecular Discovery, University of New Mexico Cancer Center, University of New Mexico, Albuquerque, NM, USA
| | - David Westergaard
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jeremy J. Yang
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Gergely Zahoránszky-Köhalmi
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
- NIH-NCATS, Rockville, MD, USA
| |
Collapse
|
49
|
Cannon DC, Yang JJ, Mathias SL, Ursu O, Mani S, Waller A, Schürer SC, Jensen LJ, Sklar LA, Bologa CG, Oprea TI. TIN-X: target importance and novelty explorer. Bioinformatics 2018; 33:2601-2603. [PMID: 28398460 PMCID: PMC5870731 DOI: 10.1093/bioinformatics/btx200] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2017] [Accepted: 04/06/2017] [Indexed: 11/14/2022] Open
Abstract
Motivation The increasing amount of peer-reviewed manuscripts requires the development of specific mining tools to facilitate the visual exploration of evidence linking diseases and proteins. Results We developed TIN-X, the Target Importance and Novelty eXplorer, to visualize the association between proteins and diseases, based on text mining data processed from scientific literature. In the current implementation, TIN-X supports exploration of data for G-protein coupled receptors, kinases, ion channels, and nuclear receptors. TIN-X supports browsing and navigating across proteins and diseases based on ontology classes, and displays a scatter plot with two proposed new bibliometric statistics: Importance and Novelty. Availability and Implementation http://www.newdrugtargets.org. Contact cbologa@salud.unm.edu.
Collapse
Affiliation(s)
- Daniel C Cannon
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA
| | - Jeremy J Yang
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA
| | - Stephen L Mathias
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA
| | - Oleg Ursu
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA
| | - Subramani Mani
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA
| | - Anna Waller
- UNM Center for Molecular Discovery, University of New Mexico Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM 87131, USA
| | - Stephan C Schürer
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N 2200, Denmark
| | - Larry A Sklar
- UNM Center for Molecular Discovery, University of New Mexico Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM 87131, USA.,Department of Pathology, University of New Mexico, NM 87131, USA
| | - Cristian G Bologa
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA
| | - Tudor I Oprea
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA
| |
Collapse
|
50
|
Oprea TI, Bologa CG, Brunak S, Campbell A, Gan GN, Gaulton A, Gomez SM, Guha R, Hersey A, Holmes J, Jadhav A, Jensen LJ, Johnson GL, Karlson A, Leach AR, Ma'ayan A, Malovannaya A, Mani S, Mathias SL, McManus MT, Meehan TF, von Mering C, Muthas D, Nguyen DT, Overington JP, Papadatos G, Qin J, Reich C, Roth BL, Schürer SC, Simeonov A, Sklar LA, Southall N, Tomita S, Tudose I, Ursu O, Vidovic D, Waller A, Westergaard D, Yang JJ, Zahoránszky-Köhalmi G. Unexplored therapeutic opportunities in the human genome. Nat Rev Drug Discov 2018; 17:377. [PMID: 29567993 DOI: 10.1038/nrd.2018.52] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
This corrects the article DOI: 10.1038/nrd.2018.14.
Collapse
|