Network analysis of psychological factors related to academic pressure faced by medical students in the central and highland regions of Vietnam.

Medical students experience extensive pressure during their undergraduate courses. Given the complex associations between psychological factors in association with academic pressure. We investigated the study with objectives: To examine psychological factors related to academic pressure by analysing interactions between 'study motivation', 'study environment', 'study conditions', 'teacher quality', 'training programme', 'management system', 'evaluation', and 'extracurricular activities' using a network analysis approach. A total of 878 medical students majoring in general medicine from the first, third, and fifth years of a six-year course at the largest medical university in central and highland regions of Vietnam were involved in this cross-sectional study. The approach used was convenient cluster sampling with a self-administered questionnaire by the participants. Network analysis for pairwise correlations between psychological factors was estimated . Important factors in the network analysis were calculated using centrality indices including node strength (S), closeness (C), and betweenness (B). The higher score of S, C, and B indicate the more importance of the node. The results obtained from the network analysis of eight psychological factors showed that 'teaching quality' was mostly connected with other factors overall, while the 'training programme' was seen in both genders and freshman students. 'Study conditions' and 'training programme' were mostly connected with other factors in junior and senior students, respectively. The strong pairwise correlation was confirmed: management system and evaluation activity, followed by study environment and study conditions, and teaching quality and training programme. Additionally, nodes with high centrality were shown to be 'management system' (S = 0.97, C = 0.019, B = 1), and 'training programme' (S = 0.96, C = 0.021, B = 4). Our study findings indicate that satisfaction with the training programme amongst eight psychological factors is the most important factor affecting academic pressure among medical students. The training programme is linked with teaching quality, whereas the management system is correlated with evaluation activity.

Genetic Susceptibility of ACE2 and TMPRSS2 in Six Common Cancers and Possible Impacts on COVID-19.

PURPOSE: Coronavirus disease 2019 (COVID-19) pandemic has spread worldwide rapidly and patients with cancer have been considered as a vulnerable group for this infection. This study aimed to examine the expressions of angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) in tumor tissues of six common cancer types. MATERIALS AND METHODS: The expression levels of ACE2 and TMPRSS2 in tumors and control samples were obtained from online databases. Survival prognosis and biological functions of these genes were investigated for each tumor type. RESULTS: There was the overexpression of ACE2 in colon and stomach adenocarcinomas compared to controls, meanwhile colon and prostate adenocarcinomas showed a significantly higher expression of TMPRSS2. Additionally, survival prognosis analysis has demonstrated that upregulation of ACE2 in liver hepatocellular carcinoma was associated with higher overall survival (hazard ratio, 0.65; p=0.016) and disease-free survival (hazard ratio, 0.66; p=0.007), while overexpression of TMPRSS2 was associated with a 26% reduced risk of death in lung adenocarcinoma (p=0.047) but 50% increased risk of death in breast invasive carcinoma (p=0.015). CONCLUSION: There is a need to take extra precautions for COVID-19 in patients with colorectal cancer, stomach cancer, and lung cancer. Further information on other types of cancer at different stages should be investigated.

Short-term exposure to ambient air pollution in association with COVID-19 of two clusters in South Korea.

OBJECTIVES: This study aimed to examine the association between six air pollutants and COVID-19 infection in two main clusters, which accounted for 83% of total confirmed cases in Korea. METHODS: We collected the data on daily confirmed cases between February 24, 2020 and September 12, 2020. Data on six air pollutants (PM2.5 , PM10 , O3 , NO2 , CO and SO2 ) and four meteorological factors (temperature, wind speed, humidity and air pressure) were obtained on seven days prior to the research period. The generalised additive model and the distributed lag nonlinear model were applied to generate the relative risks (RRs) and 95% confidence intervals (CIs) for the associations. Pooled estimates for clusters were obtained by applying a random-effects model. RESULTS: We found that NO2 concentration was positively associated with daily confirmed cases in both Seoul-Gyeonggi and Daegu-Gyeongbuk clusters, with RRs (95% CIs) of 1.22 (1.03-1.44) and 1.66 (1.25-2.19), respectively. However, SO2 concentration was observed to be associated with daily confirmed cases in the Seoul-Gyeonggi cluster only (RR = 1.30, 95% CI = 1.10-1.54), whereas PM2.5 and CO concentrations were observed to be associated with daily confirmed cases in the Daegu-Gyeongbuk cluster only (RR = 1.14, 95% CI = 1.02-1.27 and RR = 1.30, 95% CI = 1.15-1.48, respectively). CONCLUSIONS: Our data found that NO2 concentration was positively associated with daily confirmed cases in both clusters, whereas the effect of PM2.5 , CO and SO2 on COVID-19 infection in two clusters was different.

Ambient air pollution, meteorology, and COVID-19 infection in Korea.

The outbreak of novel pneumonia coronavirus disease has become a public health concern worldwide. Here, for the first time, the association between Korean meteorological factors and air pollutants and the COVID-19 infection was investigated. Data of air pollutants, meteorological factors, and daily COVID-19 confirmed cases of seven metropolitan cities and nine provinces were obtained from 3 February 2020 to 5 May 2020 during the first wave of pandemic across Korea. We applied the generalized additive model to investigate the temporal relationship. There was a significantly nonlinear association between daily temperature and COVID-19 confirmed cases. Each 1°C increase in temperature was associated with 9% (lag 0-14; OR = 1.09; 95% CI = 1.03-1.15) increase of COVID-19 confirmed cases when the temperature was below 8°C. A 0.01 ppm increase in NO2 (lag 0-7, lag 0.14, and lag 0-21) was significantly associated with increases of COVID-19 confirmed cases, with ORs (95% CIs) of 1.13 (1.02-1.25), 1.19 (1.09-1.30), and 1.30 (1.19-1.41), respectively. A 0.1 ppm increase in CO (lag 0-21) was associated with the increase in COVID-19 confirmed cases (OR = 1.10, 95% CI = 1.04-1.16). There was a positive association between per 0.001 ppm of SO2 concentration (lag 0, lag 0-7, and lag 0-14) and COVID-19 confirmed cases, with ORs (95% CIs) of 1.13 (1.04-1.22), 1.20 (1.11-1.31), and 1.15 (1.07-1.25), respectively. There were significantly temporal associations between temperature, NO2 , CO, and SO2 concentrations and daily COVID-19 confirmed cases in Korea.