Jin Ma

Prof. Jin Ma


Research Focus & Metrics

Academic Career Length

18Years (2009-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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24 February 2020 in Cell Discovery
Verified
Journal Article
Open access
PDF file
HWHuwen Wang
Huwen Wang
ZWZezhou Wang
Zezhou Wang
YDYinqiao Dong
Yinqiao Dong
RCRuijie Chang
Ruijie Chang
CXChen Xu
Chen Xu
JMJin Ma
Jin Ma
An outbreak of clusters of viral pneumonia due to a novel coronavirus (2019-nCoV/SARS-CoV-2) happened in Wuhan, Hubei Province in China in December 2019. Since the outbreak, several groups reported estimated R0 of Coronavirus Disease 2019 (COVID-19) and generated valuable prediction for the early phase of this outbreak. After implementation of strict prevention and control measures in China, new estimation is needed. An infectious disease dynamics SEIR (Susceptible, Exposed, Infectious, and Removed) model was applied to estimate the epidemic trend in Wuhan, China under two assumptions of Rt. In the first assumption, Rt was assumed to maintain over 1. The estimated number of infections would continue to increase throughout February without any indication of dropping with Rt = 1.9, 2.6, or 3.1. The number of infections would reach 11,044, 70,258, and 227,989, respectively, by 29 February 2020. In the second assumption, Rt was assumed to gradually decrease at different phases from high level of transmission (Rt = 3.1, 2.6, and 1.9) to below 1 (Rt = 0.9 or 0.5) owing to increasingly implemented public health intervention. Several phases were divided by the dates when various levels of prevention and control measures were taken in effect in Wuhan. The estimated number of infections would reach the peak in late February, which is 58,077–84,520 or 55,869–81,393. Whether or not the peak of the number of infections would occur in February 2020 may be an important index for evaluating the sufficiency of the current measures taken in China. Regardless of the occurrence of the peak, the currently strict measures in Wuhan should be continuously implemented and necessary strict public health measures should be applied in other locations in China with high number of COVID-19 cases, in order to reduce Rt to an ideal level and control the infection.

25 March 2020 in Frontiers of Medicine
Verified
Journal Article
Closed access
PDF file
SZShuxian Zhang
Shuxian Zhang
ZWZezhou Wang
Zezhou Wang
RCRuijie Chang
Ruijie Chang
HWHuwen Wang
Huwen Wang
CXChen Xu
Chen Xu
JMJin Ma
Jin Ma

XYXue Yang
Xue Yang
XJXuewen Jiang
Xuewen Jiang
Phoenix K. H. MoPhoenix K. H. Mo
Phoenix K. H. Mo
JMJin Ma
Jin Ma
LMLe Ma
Le Ma
Joseph Tak-fai LauJoseph Tak-fai Lau
Joseph Tak-fai Lau
This study investigated the prevalence and interpersonal correlates of Internet gaming disorders (IGD) among Chinese adolescents. A cross-sectional survey was conducted in two cities (Shanghai and Xi’an) in China. A total of 2666 (Meanage = 12.77 ± 0.75) year-one students from eight middle schools completed a self-reported questionnaire. It tested their levels of IGD, parental psychological control, negative interpersonal events (physical/verbal abuse by parents, verbal abuse by teachers, peer/online bullying), social support from parents/peers, and positive relationships with parents/peers. Results showed that 346 participants (13.0%) were classified as having IGD. Gender, city, single-parent family, family socio-economic status, and mother’s education level were significantly associated with the risk of IGD. Logistic regression analyses with and without controlling for the significant background variables showed that the studied interpersonal variables were significantly associated with IGD, respectively. Forward stepwise logistic regression showed that the significant correlates of IGD included parental psychological control, physical/verbal abuse by parents, verbal abuse by teachers, and peer/online bullying. Results highlight the importance of addressing interpersonal risk factors to reduce adolescent IGD. Limitations and implications of this study are discussed.

23 September 2020 in BMC Psychiatry
Verified
Journal Article
Open access
PDF file
YYYuelin Yu
Yuelin Yu
XYXue Yang
Xue Yang
SWSuping Wang
Suping Wang
HWHuwen Wang
Huwen Wang
RCRuijie Chang
Ruijie Chang
Joseph Tak-fai LauJoseph Tak-fai Lau
Joseph Tak-fai Lau
Background: High prevalence and strong relationships among suicidal ideation, Internet gaming disorder (IGD), insomnia, and depression have been reported for adolescents worldwide, but the mechanism underlying these psychological problems remains unclear. This cross-sectional study explored the mediating effect of insomnia and depression on the association between IGD and suicidal ideation. Methods: Participants were 1066 adolescents (median age = 13.0 years) with Internet games exposure in the previous 12 months from junior high schools in Shanghai, China. Questionnaire measures of suicidal ideation, IGD, insomnia, depressive symptoms, and background characteristics were obtained. Path analysis was conducted to test the multiple mediating roles of insomnia and depression. Results: Suicidal ideation, IGD, insomnia, and depression prevalence was 27.2, 13.6, 9.2, and 17.0%, respectively. A serial multiple mediation model was generated. The mediation effect of insomnia and depression on the pathway from IGD to suicidal ideation was 45.5% (direct effect: standardized estimate [Std. estimate] = 0.186; total indirect effect: Std. estimate = 0.155). The association between IGD and depression was partially mediated by insomnia (direct effect: Std. estimate = 0.211; indirect effect: Std. estimate = 0.135). The proposed model fit the data well. Conclusions: Insomnia and depression may serially mediate the association between IGD and suicidal ideation. IGD was positively associated with insomnia, then with depression, which in turn positively contributed to suicidal ideation. We suggest greater monitoring of Internet use and prevention of insomnia and depression to mitigate the risk of suicidal ideation among Chinese adolescents.

6 May 2014 in BMC Infectious Diseases
Verified
Journal Article
Open access
PDF file
ZYZhitao YANG
Zhitao YANG
LWLin Wu
Lin Wu
XLXiao-Ying Liu
Xiao-Ying Liu
MZMin Zhou
Min Zhou
JLJie Li
Jie Li
OLOlivier Lortholary
Olivier Lortholary
Background: Yeasts, mostly Candida, are important causes of bloodstream infections (BSI), responsible for significant mortality and morbidity among hospitalized patients. The epidemiology and species distribution vary from different regions. The goals of this study were to report the current epidemiology of Candida BSI in a Shanghai Teaching Hospital and estimate the impact of appropriate antifungal therapy on the outcome. Methods: From January 2008 to December 2012, all consecutive patients who developed Candida BSI at Ruijin University Hospital were enrolled. Underlying diseases, clinical severity, species distribution, antifungal therapy and its impact on the outcome were analyzed. Results: A total of 121 episodes of Candida BSI were identified, with an incidence of 0.32 episodes/1,000 admissions (0.21 in 2008 and 0.42 in 2012) The proportion of candidemia caused by non-albicans species (62.8%), including C. parapsilosis (19.8%), C. tropicalis (14.9%), C. glabrata (7.4%), C. guilliermondii (5.8%), C. sake (5.0%) was higher than that of candidemia caused by C. albicans (37.2%). The overall crude 28-day mortality was 28.1% and significantly reduced with appropriate empiric antifungal therapy administered within 5 days (P = 0.006). Advanced age (OR 1.04; P = 0.014), neutropenia < 500/mm3 (OR 17.44; P < 0.001) were independent risk factors for 28-day mortality, while appropriate empiric antifungal therapy (OR 0.369; P = 0.035) was protective against 28-day mortality. Conclusion: The epidemiology of candidemia in Shanghai differed from that observed in Western countries. Appropriate empiric antifungal therapy influenced the short-term survival.

1 October 2021 in Journal of Adolescence
Verified
Journal Article
Closed access
YLYujie Liu
Yujie Liu
RGRuijie Gong
Ruijie Gong
YYYuelin Yu
Yuelin Yu
CXChen Xu
Chen Xu
XYXiaoyue Yu
Xiaoyue Yu
JMJin Ma
Jin Ma