The psychological condition of medical students may be influenced by the 2019 novel coronavirus (COVID-19) outbreak. This study investigated the prevalence and influencing factors of depressive symptoms, poor sleep quality and poor diet in students at Kunming Medical University during the early part of the COVID-19 outbreak. A cross-sectional study was used from a questionnaire survey in February 2020. Of a total of 1,026 study participants, the prevalence of depressive symptoms, poor sleep quality, and poor diet was, respectively, 22.4, 33.2, and 17.4%. Male students and students with a low degree of focus on COVID-19 had a high risk of depressive symptoms. A high percentage of females and students in the fifth grade, as well as students with high levels of concern about the negative impact of COVID-19 on their education or employment, comprised those with poor sleep quality. Students in the fifth grade and students with high levels of concern about the negative impact of COVID-19 on their education or employment were more likely to report poor diet. This study suggests the importance of monitoring medical students' depressive state during the COVID-19 outbreak, and universities are encouraged to institute policies and programs to provide educational counseling and psychological support to help students to cope with these problems.

Objective Facial masks are an essential personal protective measure to fight the COVID-19 pandemic. However, the mask adoption rate in the US is still less than optimal. This study aims to understand the beliefs held by individuals who oppose the use of facial masks, and the evidence that they use to support these beliefs, to inform the development of targeted public health communication strategies. Materials and Methods We analyzed a total of 771,268 US-based tweets between January to October 2020. We developed machine-learning classifiers to identify and categorize relevant tweets, followed by a qualitative content analysis of a subset of the tweets to understand the rationale of those opposed mask wearing. Results We identified 267,152 tweets that contained personal opinions about wearing facial masks to prevent the spread of COVID-19. While the majority of the tweets supported mask wearing, the proportion of anti-mask tweets stayed constant at about 10% level throughout the study period. Common reasons for opposition included physical discomfort and negative effects, lack of effectiveness, and being unnecessary or inappropriate for certain people or under certain circumstances. The opposing tweets were significantly less likely to cite external sources of information such as public health agencies’ websites to support the arguments. Discussion and Conclusion Combining machine learning and qualitative content analysis is an effective strategy for identifying public attitudes toward mask wearing and the reasons for opposition. The results may inform better communication strategies to improve the public perception of wearing masks and, in particular, to specifically address common anti-mask beliefs.

Fatty acid composition of fungi is analysed through the gas chromatography technique. With specific activity a novel enzyme Delta6-fatty acid desaturase was screened and isolated from Rhizopus nigricans. In this study R. nigricans was identified as a fungal species that produced plentiful gamma-linolenic acid. A 1,475 bp full-length cDNA, designated as RnD6D here, with high homology to fungal Delta6-fatty acid desaturase genes was isolated from R. nigricans using reverse transcription polymerase chain reaction and rapid amplification of cDNA ends methods. Sequence analysis indicated that this cDNA sequence had an open reading frame of 1,380 bp encoding a deduced polypeptide of 459 amino acids. Bioinformatics analysis characterized the putative RnD6D protein as a typical membrane-bound desaturase, including three conserved histidine-rich motifs, hydropathy profile and a cytochrome b5-like domain in the N-terminus. The corresponding genomic sequence of RnD6D was 1,689 bp with 4 introns, which was at least one intron more than other fungal Delta6-fatty acid desaturase genes. To elucidate the function of this novel putative desaturase, the coding sequence was expressed in Saccharomyces cerevisiae strain INVScl. A novel peak corresponding to gamma-linolenic acid methyl ester standards was detected with the same retention time, which was absent in the cell transformed with empty vector. The result demonstrated that the coding produced Delta6-fatty acid desaturase activity of RnD6D which led to the accumulation of gamma-linolenic acid. The functionally active RnD6D gene cloned here defined a novel Delta6-fatty acid desaturase from R. nigricans.

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Objective Sentiment analysis is a popular tool for analyzing health-related social media content. However, existing studies exhibit numerous methodological issues and inconsistencies with respect to research design and results reporting, which could lead to biased data, imprecise or incorrect conclusions, or incomparable results across studies. This article reports a systematic analysis of the literature with respect to such issues. The objective was to develop a standardized protocol for improving the research validity and comparability of results in future relevant studies. Materials and Methods We developed the Protocol of Analysis of senTiment in Health (PATH) based on a systematic review that analyzed common research design choices and how such choices were made, or reported, among eligible studies published 2010-2019. Results Of 409 articles screened, 89 met the inclusion criteria. A total of 16 distinctive research design choices were identified, 9 of which have significant methodological or reporting inconsistencies among the articles reviewed, ranging from how relevance of study data was determined to how the sentiment analysis tool selected was validated. Based on this result, we developed the PATH protocol that encompasses all these distinctive design choices and highlights the ones for which careful consideration and detailed reporting are particularly warranted. Conclusions A substantial degree of methodological and reporting inconsistencies exist in the extant literature that applied sentiment analysis to analyzing health-related social media data. The PATH protocol developed through this research may contribute to mitigating such issues in future relevant studies.