Comparative analysis of clinical and immunological profiles across Omicron BA.5.2 subvariants using next-generation sequencing in a Chinese cohort.

Objective: The Omicron BA.5.2 variant of SARS-CoV-2 has undergone several evolutionary adaptations, leading to multiple subvariants. Rapid and accurate characterization of these subvariants is essential for effective treatment, particularly in critically ill patients. This study leverages Next-Generation Sequencing (NGS) to elucidate the clinical and immunological features across different Omicron BA.5.2 subvariants. Methods: We enrolled 28 patients infected with the Omicron variant, hospitalized in Zhangjiajie People's Hospital, Hunan, China, between January 20, 2023, and March 31, 2023. Throat swabs were collected upon admission for NGS-based identification of Omicron subvariants. Clinical data, including qSOFA scores and key laboratory tests, were collated. A detailed analysis of lymphocyte subsets was conducted to ascertain the extent of immune cell damage and disease severity. Results: Patients were infected with various Omicron subvariants, including BA.5.2.48, BA.5.2.49, BA.5.2.6, BF.7.14, DY.1, DY.2, DY.3, and DY.4. Despite having 43 identical mutation sites, each subvariant exhibited unique marker mutations. Critically ill patients demonstrated significant depletion in total lymphocyte count, T cells, CD4, CD8, B cells, and NK cells (P < 0.05). However, there were no significant differences in clinical and immunological markers across the subvariants. Conclusion: This study reveals that critically ill patients infected with different Omicron BA.5.2 subvariants experience similar levels of cellular immune dysfunction and inflammatory response. Four mutations - ORF1a:K3353R, ORF1a:L3667F, ORF1b:S997P, S:T883I showed correlation with immunological responses although this conclusion suffers from the small sample size. Our findings underscore the utility of NGS in the comprehensive assessment of infectious diseases, contributing to more effective clinical decision-making.

Medical cost of environmental pollution: evidence from the Chinese Social Survey.

Environmental pollution impairs residents' health, while the pursuit of health is highly correlated to medical costs. Understanding how environmental pollution affects medical costs is closely linked to the welfare of society. Based on theoretical analysis, this paper uses data from 5112 households of the Chinese Social Survey (CSS) in 2019, constructs a composite indicator to quantify environmental pollution using respondents' evaluations, and empirically investigates the causal effect of environmental pollution on household medical cost and the mechanism. The conclusions are shown as follows. First, environmental pollution can increase household medical costs, and this estimation result still holds after dealing with the endogeneity problem and other robustness tests. Second, there is heterogeneity in the impact of environmental pollution on household medical costs, households in the upper socioeconomic class, with heavy pension burdens or with strong health insurance coverage are more sensitive to environmental pollution and incur relatively higher household medical costs. Third, environmental pollution reduces residents' satisfaction with their spiritual life, which adversely affects their physical and mental health and can increase household medical costs. Residents' satisfaction with their spiritual life is an important mechanism for environmental pollution to affect household health care expenditures. Therefore, governments should enhance the enforcement of environmental protection and governance, strengthen the awareness of green issues and health education, and increase the supply of facilities for leisure and sports, thus reducing medical costs due to environmental pollution and easing the medical burden of residents.

CNV-FB: A Feature bagging strategy-based approach to detect copy number variants from NGS data.

Copy number variation (CNV), as a type of genomic structural variation, accounts for a large proportion of structural variation and is related to the pathogenesis and susceptibility to some human diseases, playing an important role in the development and change of human diseases. The development of next-generation sequencing technology (NGS) provides strong support for the design of CNV detection algorithms. Although a large number of methods have been developed to detect CNVs using NGS data, it is still considered a difficult problem to detect CNVs with low purity and coverage. In this paper, a new calculation method CNV-FB is proposed to detect CNVs from NGS data. The core idea of CNV-FB is to randomly sample the read depth values of the genome fragment, and then each sample is individually detected for outliers, and finally combined into a final outlier score. The CNV-FB method was applied to simulation data and real data experiments and compared with the other five methods of the same type. The results show that the CNV-FB method has a better detection effect than other methods. Therefore, the CNV-FB method may be an effective algorithm for detecting genomic mutations.

The impact of self-regulated learning strategies on academic performance for online learning during COVID-19.

The COVID-19 pandemic led higher education institutions to transition to online learning. The present study was designed to investigate students' self-regulated learning strategies on academic performance in online learning. We analyzed the differences in college students' self-regulated learning (SRL) strategies according to their grade point average (GPA). The study included 1,163 students at a distance education university in China. Two online questionnaires were used to determine online SRL strategies. GPA scores were obtained from the university exam database to determine academic performance. The analysis showed that there are great differences between different self-regulated strategies and between different students when accepting the online learning. The analysis also showed that self-evaluation, metacognitive self-regulation, and effort regulation were positive predictors of academic progress, besides, self-evaluation and effort regulation had mutual influence effect on the improvement of GPA in online learning. These data will help teachers, education policymakers, and education administrators adopt and implement online learning services to improve students' academic performance.

The Effectiveness Assessment of Agricultural Subsidy Policies on Food Security: Evidence from China's Poverty-Stricken Villages.

This paper builds a theoretical model based on a representative peasant household in the neoclassical model, comprehensively considers three types of farmer households in China, and evaluates the effects of the agricultural subsidy policy under equilibrium conditions. Based on the two bottom lines of guaranteeing China's grain security and ensuring no large-scale return to poverty, this paper uses 2010, 2012, and 2014 tracking survey data from the Mutual Aid Fund for Poverty-Stricken Villages in China to construct an econometric model to evaluate agricultural subsidy effectiveness from the three aspects of farmers' total sown area, total grain output, and total income. The research draws the following conclusions: (1) Agricultural subsidies can significantly increase the sown area, grain production, and total income of farmers in poverty-stricken areas, which is conducive to improving the farmers' comprehensive capacity for grain production as well as income, and this conclusion remains valid after performing a series of robustness tests and solving endogeneity problems. (2) The effects of the agricultural subsidy policy are affected by natural conditions, economic development levels, and functional orientation of grain production in different regions, and they have divergent influences on farmers' total sown area, total output, and total income. (3) Agricultural subsidies boost farmers' willingness to cultivate grain, reduce land abandonment, and increase the total sown area, total output, and total grain income. The willingness to cultivate grain is an important mechanism that affects the effectiveness of the agricultural subsidy policy.