Institution
Chongqing Medical University
Education•Chongqing, China•
About: Chongqing Medical University is a education organization based out in Chongqing, China. It is known for research contribution in the topics: Apoptosis & Cell growth. The organization has 30777 authors who have published 18939 publications receiving 273523 citations. The organization is also known as: Chongqing University of Medical Sciences.
Topics: Apoptosis, Cell growth, Medicine, Cancer, Population
Papers published on a yearly basis
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University of Washington1, Sapienza University of Rome2, Mekelle University3, University of Texas at San Antonio4, King Saud bin Abdulaziz University for Health Sciences5, Debre markos University6, Emory University7, University of Oxford8, University of Cartagena9, United Nations Population Fund10, University of Birmingham11, Stanford University12, Aga Khan University13, University of Melbourne14, National Taiwan University15, University of Cambridge16, University of California, San Diego17, Public Health Foundation of India18, Public Health England19, University of Peradeniya20, Harvard University21, National Institutes of Health22, Tehran University of Medical Sciences23, Auckland University of Technology24, University of Sheffield25, University of Western Australia26, Karolinska Institutet27, Birzeit University28, Brandeis University29, American Cancer Society30, Ochsner Medical Center31, Yonsei University32, University of Bristol33, Heidelberg University34, Vanderbilt University35, South African Medical Research Council36, Jordan University of Science and Technology37, New Generation University College38, Northeastern University39, Simmons College40, Norwegian Institute of Public Health41, Boston University42, Chinese Center for Disease Control and Prevention43, University of Bari44, University of São Paulo45, University of Otago46, University of Crete47, International Centre for Diarrhoeal Disease Research, Bangladesh48, Fred Hutchinson Cancer Research Center49, Teikyo University50, Bhabha Atomic Research Centre51, University of Tokyo52, Finnish Institute of Occupational Health53, Heriot-Watt University54, University of Alabama at Birmingham55, Griffith University56, National Center for Disease Control and Public Health57, University of California, Irvine58, Johns Hopkins University59, New York University60, University of Queensland61, Universidade Federal de Minas Gerais62, National Research University – Higher School of Economics63, University of Bergen64, Columbia University65, Shandong University66, University of North Carolina at Chapel Hill67, Fujita Health University68, Korea University69, Chongqing Medical University70, Zhejiang University71
TL;DR: The global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013 is estimated using a spatiotemporal Gaussian process regression model to estimate prevalence with 95% uncertainty intervals (UIs).
9,180 citations
TL;DR: In the Global Burden of Disease Study 2013 (GBD 2013) as discussed by the authors, the authors used the GBD 2010 methods with some refinements to improve accuracy applied to an updated database of vital registration, survey, and census data.
5,792 citations
TL;DR: It is suggested that SARS-CoV2-specific IgG or IgM seroconversion occurs within 20 days post symptom onset and may be helpful for the diagnosis of suspected patients with negative RT–PCR results and for the identification of asymptomatic infections.
Abstract: We report acute antibody responses to SARS-CoV-2 in 285 patients with COVID-19. Within 19 days after symptom onset, 100% of patients tested positive for antiviral immunoglobulin-G (IgG). Seroconversion for IgG and IgM occurred simultaneously or sequentially. Both IgG and IgM titers plateaued within 6 days after seroconversion. Serological testing may be helpful for the diagnosis of suspected patients with negative RT-PCR results and for the identification of asymptomatic infections.
2,473 citations
TL;DR: A cohort of asymptomatic patients infected with SARS-CoV-2 had significantly lower levels of virus-specific IgG antibodies compared to a cohort of age- and sex-matched symptomatic infected patients.
Abstract: The clinical features and immune responses of asymptomatic individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have not been well described We studied 37 asymptomatic individuals in the Wanzhou District who were diagnosed with RT-PCR-confirmed SARS-CoV-2 infections but without any relevant clinical symptoms in the preceding 14 d and during hospitalization Asymptomatic individuals were admitted to the government-designated Wanzhou People's Hospital for centralized isolation in accordance with policy1 The median duration of viral shedding in the asymptomatic group was 19 d (interquartile range (IQR), 15-26 d) The asymptomatic group had a significantly longer duration of viral shedding than the symptomatic group (log-rank P = 0028) The virus-specific IgG levels in the asymptomatic group (median S/CO, 34; IQR, 16-107) were significantly lower (P = 0005) relative to the symptomatic group (median S/CO, 205; IQR, 58-382) in the acute phase Of asymptomatic individuals, 933% (28/30) and 811% (30/37) had reduction in IgG and neutralizing antibody levels, respectively, during the early convalescent phase, as compared to 968% (30/31) and 622% (23/37) of symptomatic patients Forty percent of asymptomatic individuals became seronegative and 129% of the symptomatic group became negative for IgG in the early convalescent phase In addition, asymptomatic individuals exhibited lower levels of 18 pro- and anti-inflammatory cytokines These data suggest that asymptomatic individuals had a weaker immune response to SARS-CoV-2 infection The reduction in IgG and neutralizing antibody levels in the early convalescent phase might have implications for immunity strategy and serological surveys
2,463 citations
TL;DR: It is demonstrated that dysbiosis of the gut microbiome may have a causal role in the development of depressive-like behaviors, in a pathway that is mediated through the host’s metabolism.
Abstract: Major depressive disorder (MDD) is the result of complex gene-environment interactions. According to the World Health Organization, MDD is the leading cause of disability worldwide, and it is a major contributor to the overall global burden of disease. However, the definitive environmental mechanisms underlying the pathophysiology of MDD remain elusive. The gut microbiome is an increasingly recognized environmental factor that can shape the brain through the microbiota-gut-brain axis. We show here that the absence of gut microbiota in germ-free (GF) mice resulted in decreased immobility time in the forced swimming test relative to conventionally raised healthy control mice. Moreover, from clinical sampling, the gut microbiotic compositions of MDD patients and healthy controls were significantly different with MDD patients characterized by significant changes in the relative abundance of Firmicutes, Actinobacteria and Bacteroidetes. Fecal microbiota transplantation of GF mice with 'depression microbiota' derived from MDD patients resulted in depression-like behaviors compared with colonization with 'healthy microbiota' derived from healthy control individuals. Mice harboring 'depression microbiota' primarily exhibited disturbances of microbial genes and host metabolites involved in carbohydrate and amino acid metabolism. This study demonstrates that dysbiosis of the gut microbiome may have a causal role in the development of depressive-like behaviors, in a pathway that is mediated through the host's metabolism.
1,224 citations
Authors
Showing all 30838 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| Jing Wang | 184 | 4046 | 202769 |
| Gang Chen | 167 | 3372 | 149819 |
| Hua Zhang | 163 | 1503 | 116769 |
| Yu Huang | 136 | 1492 | 89209 |
| Chao Zhang | 127 | 3119 | 84711 |
| Qian Wang | 108 | 2148 | 65557 |
| Qi Li | 102 | 1563 | 46762 |
| Jian Huang | 97 | 1189 | 40362 |
| Wei Liu | 96 | 1538 | 42459 |
| Huangxian Ju | 94 | 688 | 34199 |
| Qian Liu | 90 | 610 | 33341 |
| John H. Zhang | 84 | 895 | 29976 |
| Tao Pan | 84 | 295 | 23859 |
| Wei Tang | 84 | 671 | 27175 |
| Tao Jiang | 82 | 940 | 27018 |