[Sedentary behavior, screen time and mental health of college students: a Meta-analysis].

Objective: To evaluate the effects of sedentary behavior/screen time on mental health of college students by Meta-analysis based on the results of literature retrieval and provide theoretical basis for the improvement of college students' mental health. Methods: The original research literatures about sedentary behavior (including screen time) and college students' mental health published as of 14 July 2022 were retrieved from PubMed, Embase, Cochrane Library, CNKI, VIP and Wanfang data. Data were extracted from the included studies and scored by one author in accordance with the proposed programme, and quality score was reviewed by another author. The literature that met the inclusion criteria was systematically reviewed and Meta-analysis was carried out by software Stata 14.2 based on the data from the literatures. Results: A total of 36 studies met the inclusion criteria, including 35 observational studies and 1 interventional study. There are 4 papers about the effects of sedentary behavior and 9 papers about the effects of screening time on depression in college students and 4 papers about the effects of sedentary behavior/screening time on anxiety in college students were used for a Meta-analysis, and the other studies were also analyzed. The Meta-analysis on the effects of sedentary behavior on depression in college students showed that there was a significant positive correlation between higher level sedentary behavior and increased risk for depression (OR=1.07,95%CI:1.05-1.10). Subgroup analysis indicated that there was no significant correlation between higher level sedentary behavior and depression (OR=1.74, 95%CI:0.93-3.25) in the unadjusted confounding factor model, but there was significance positive correlation after adjusting confounding factors (OR=2.15, 95%CI:1.18-3.90). Meta-analysis on the effects of screen time on depression in college students showed that longer screen time were significantly positively correlated with higher depression level (OR=1.03, 95%CI: 1.02-1.05). The results of subgroup analysis showed that in both unadjusted confounding factor model and adjusted confounding factor model, longer screen time was significantly positively correlated with depression (OR=1.27, 95%CI: 1.13-1.42; OR=1.45, 95%CI: 1.18-1.79) , respectively. Meta-analysis on the effects of sedentary behavior on anxiety showed that longer screen time was significantly positively correlated with increased anxiety risk (OR=1.44, 95%CI: 1.31-1.58). The results of subgroup analysis showed that in both unadjusted confounding factor model and adjusted confounding factor model, there was a significant positive correlation between longer screen time and anxiety (OR=1.47, 95%CI: 1.31-1.65; OR=1.38, 95%CI:1.17-1.62). The analysis for the literatures which were not eligible for Meta-analysis found that sedentary behavior/screen time was significantly associated with stress and other mental health in college students. Conclusions: Sedentary behavior or screen time is significantly negatively correlated with college students' mental health, in particular, resulting in depression and anxiety. These effects might be be different between weekdays and weekend days.

[Analysis of HIV-1 genetic subtype and pretreatment drug resistance among men who have sex with men infected with HIV-1 from 19 cities of 6 provinces in China].

Objective: To investigate the distribution of HIV-1 genetic subtypes and pretreatment drug resistance (PDR) among men who have sex with men (MSM) from 19 cities of 6 provinces in China. Methods: From April to November 2019, 574 plasma samples of ART-naive HIV-1 infected MSM were collected from 19 cities in Hebei, Shandong, Jiangsu, Zhejiang, Fujian, and Guangdong provinces, total ribonucleic acid (RNA) was extracted and amplified the HIV-1 pol gene region by nested polymerase chain reaction (PCR) after reverse transcription. Then sequences were used to construct a phylogenetic tree to determine genetic subtypes and submitted to the Stanford drug resistance database for drug resistance analysis. Results: A total of 479 samples were successfully amplified by PCR. The HIV-1 genetic subtypes included CRF01_AE, CRF07_BC, B, CRF55_01B, CRF59_01B, CRF65_cpx, CRF103_01B, CRF67_01B, CRF68_01B and unrecognized subtype, which accounted for 43.4%, 36.3%, 6.3%, 5.9%, 0.8%, 0.8%, 0.4%, 0.4%, 0.2% and 5.5%, respectively. The distribution of genetic subtypes among provinces is statistically different (χ2=44.141, P<0.001). The overall PDR rate was 4.6% (22/479), the drug resistance rate of non-nucleoside reverse transcriptase inhibitors, nucleoside reverse transcriptase inhibitors, and protease inhibitors were 3.5% (17/479), 0.8% (4/479) and 0.2% (1/479), respectively. The PDR rate of recent infections was significantly higher than that of long-term infections (χ2=4.634, P=0.031). Conclusions: The HIV-1 genetic subtypes among MSM infected with HIV-1 from 19 cities of 6 provinces in China are diverse, and the distribution of subtypes is different among provinces. The overall PDR rate is low, while the PDR rate of recent infections was significantly higher than that of long-term infections, suggesting the surveillance of PDR in recent infections should be strengthened.

Glycolysis recycling of rigid waste polyurethane foam from refrigerators.

Rapid growth of rigid waste polyurethane (WPUR) foam from refrigerators attracts the attention all over the world. In this study, glycolysis was chosen to treat WPUR from scrapped refrigerators collected in Shanghai, China. Glycolysis reagents and catalysts were selected. The results indicated that the glycolysis efficiency of ethylene glycol (EG) was higher than that of diethylene glycol, and the catalytic efficiency of alkali metal salts (NaOH) was more excellent than that of triethanolamine and organic salts of alkali metal (NaAc). When EG was 100%WPUR as a glycolysis reagent and NaOH was 1%WPUR as a catalyst at a constant temperature of 197.85°C for 2 h, the glycolysis product had the highest glycolysis conversion rate. In order to maximize the recycling of WPUR, regenerative Polyurethane was performed by adding 10% distilled mixed polyol, which conformed to the QB/T 26689-2011 requirements.