Does growth mindset benefit mental health in Asia? Evidence from Chinese students

Growth mindset has shown its unique potential in countering the growing prevalence of mental distress in the general population. However, the role culture plays in this process remains somewhat unanswered. In the current prospective study, we tested if early growth mindset of Chinese university students predicts less mental distress later, and how cultural values (i.e., individualism–collectivism, traditionality–modernity) affect the process. We found that growth mindset was prospectively predictive of mental conditions, and the positive effect of growth mindset was more salient among students endorsing lower collectivistic and higher modern cultural values. Our findings added evidence to the potential benefits of growth mindsets in an Asian context and highlighted the role of cultural values.

Optimization of potential non-covalent inhibitors for the SARS-CoV-2 main protease inspected by a descriptor of the subpocket occupancy.

The main protease is regarded as an essential drug target for treating Coronavirus Disease 2019. In the present study, 13 marketed drugs were investigated to explore the possible binding mechanism, utilizing molecular docking, molecular dynamics simulation, and MM-PB(GB)SA binding energy calculations. Our results suggest that fusidic acid, polydatin, SEN-1269, AZD6482, and UNC-2327 have high binding affinities of more than 23 kcal mol-1. A descriptor was defined for the energetic occupancy of the subpocket, and it was found that S4 had a low occupancy of less than 10% on average. The molecular optimization of ADZ6482 via reinforcement learning algorithms was carried out to screen out three lead compounds, in which slight structural changes give more considerable binding energies and an occupancy of the S4 subpocket of up to 43%. The energetic occupancy could be a useful descriptor for evaluating the local binding affinity for drug design.

Disparities in the unmet mental health needs between LGBTQ+ and non-LGBTQ+ populations during COVID-19 in the United States from 21 July 2021 to 9 May 2022.

Background: Evidence highlighted the likelihood of unmet mental health needs (UMHNs) among LGBTQ+ than non-LGBTQ+ populations during COVID-19. However, there lacks evidence to accurately answer to what extent the gap was in UMHN between LGBTQ+ and non-LGBTQ+ populations. We aim to evaluate the difference in UMHN between LGBTQ+ and non-LGBTQ+ during COVID-19. Methods: Cross-sectional data from Household Pulse Survey between 21 July 2021 and 9 May 2022 were analyzed. LGBTQ+ was defined based on self-reported sex at birth, gender, and sexual orientation identity. UMHN was assessed by a self-reported question. Multivariable logistic regressions generated adjusted odds ratios (AODs) of UMHN, both on overall and subgroups, controlling for a variety of socio-demographic and economic-affordability confounders. Findings: 81267 LGBTQ+ and 722638 non-LGBTQ+ were studied. The difference in UMHN between LGBTQ+ and non-LGBTQ+ (as reference) varied from 4.9% (95% CI 1.2-8.7%) in Hawaii to 16.0% (95% CI 12.2-19.7%) in Utah. In multivariable models, compared with non-LGBTQ+ populations, LGBTQ+ had a higher likelihood to report UMHN (AOR = 2.27, 95% CI 2.18-2.39), with the highest likelihood identified in transgender (AOR = 3.63, 95% CI 2.97-4.39); compared with LGBTQ+ aged 65+, LGBTQ+ aged 18-25 had a higher likelihood to report UMHN (AOR = 1.34, 95% CI 1.03-1.75); compared with White LGBTQ+ populations, Black and Hispanic LGBTQ+ had a lower likelihood to report UMHN (AOR = 0.72, 95% CI 0.63-0.82; AOR = 0.85, 95% CI 0.75-0.97, respectively). Interpretation: During the COVID-19, LGBTQ+ had a substantial additional risk of UMHN than non-LGBTQ+. Disparities among age groups, subtypes of LGBTQ+, and geographic variance were also identified.

Municipal Ethnic Composition and Disparities in COVID-19 Infections in New Jersey: A Blinder-Oaxaca Decomposition Analysis.

COVID-19 has disproportionally impacted Latinx and Black communities in the US. Our study aimed to extend the understanding of ethnic disparities in COVID-19 case rates by using a unique dataset of municipal case rates across New Jersey (NJ) during the first 17 months of the pandemic. We examined the extent to which there were municipal-level ethnic disparities in COVID-19 infection rates during three distinct spikes in case rates over this period. Furthermore, we used the Blinder-Oaxaca decomposition analysis to identify municipal-level exposure and vulnerability factors that contributed to ethnic disparities and how the contributions of these factors changed across the three initial waves of infection. Two clear results emerged. First, in NJ, the COVID-19 infection risk disproportionally affected Latinx communities across all three waves during the first 17 months of the pandemic. Second, the exposure and vulnerability factors that most strongly contributed to higher rates of infection in Latinx and Black communities changed over time as the virus, alongside medical and societal responses to it, also changed. These findings suggest that understanding and addressing ethnicity-based COVID-19 disparities will require sustained attention to the systemic and structural factors that disproportionately place historically marginalized ethnic communities at greater risk of contracting COVID-19.

Distributional Predictability and Quantile Connectedness of New Energy, Steam Coal, and High-Tech in China

From a novel quantile perspective, this paper employs nonparametric quantile causality and quantile connectedness to investigate distributional predictability and spillover effects among new energy, steam coal, and high-tech under normal and tail conditions. We first identify the quantile causality: there is a unidirectional causality between the quantile orders 0.1 and 0.4 from technology high-tech to new energy, indicating that the stock price of technology companies has a predictive power of the stock prices of new energy companies when the latter is relatively low. Next, in terms of quantile connectedness, while the risk shocks to the system do not propagate strongly around the median, there are strong spillover effects in both tails. Moreover, high-tech and new energy contribute most of the system's spillovers, and high-tech is the main net shock transmitter to all other variables. We further find that the strength of spillovers may depend on events such as China's stock market rout of 2015 and the COVID-19 pandemic.

Research on the Public Opinion Guidance Mechanism of Major Public Health Incidents

Public opinion guidance plays a crucial role in the management of major public health incidents, and thus, exploring its mechanism is conducive to the comprehensive governance of social security. This study conducts a case study on the anti-pandemic public opinion guidance and analyzes the public opinion representation and the internal mechanism of public opinion guidance in the context of the COVID-19 in China. The findings suggest that the public opinion on the COVID-19 manifested a three-stage progressive and stable tendency and witnessed the strength of China, specifically, benefiting from the systematic and complete integration and release mechanism for anti-pandemic information, the three-dimensional mechanism for the dissemination of knowledge related to pandemic prevention and health, the innovative disclosure mechanism for precise information, and diversified channels for international public opinion guidance. The guidance mechanism proposed in this study provides significant suggestions for the public opinion guidance of global major public health incidents in future.

The Influence of Family Function on State Anxiety of Chinese College Students During the Epidemic of COVID-19.

The Coronavirus Disease 2019 (COVID-19) may affect mental health. There is little research about the influence of family function on the state anxiety of college students in the context of the global pandemic. The study aimed to clarify that generalized anxiety disorder (GAD) and trait anxiety had mediating effects in family function on the state anxiety of Chinese college students following the "stay-at-home" order during the outbreak of COVID-19. This cross-sectional study was conducted online with 1,039 respondents. We analyzed demographics, The State-Trait Anxiety Inventory (ST-AI) and Chinese Family Function Scale (FAD-18), Generalized Anxiety Disorder-7 (GAD-7), and used path analysis to discuss the influence of family function on state and trait anxiety. The results showed that female students' state and trait anxiety was higher than that of male students (P < 0.05). Medical students' state and trait anxiety was lower than that of literature students (P < 0.05). The GAD of the male was lower compared with the female. The score of family function has significant differences in gender, age, education, and region (P < 0.05). Gender, family function, state anxiety, trait anxiety, and GAD relate to others (r = 0.07∼0.85, p < 0.05). The results of fit indices for measurement invariance models showed that the impact of family function among GAD, state and trait anxiety across gender is significantly different (each step p < 0.05). GAD and trait anxiety had a complete meditating effect between family function and state anxiety (the proportion of standard indirect mediating effect was 24.94% in females and 36.79% in males). A healthy family function may alleviate GAD and anxiety of college students during the COVID-19 pandemic.

New Jersey COVID-19 municipal dataset.

Although data about COVID-19 cases and deaths in the United States are readily available at the county-level, datasets on smaller geographic areas are limited. County-level data have been used to identify geospatial patterns of COVID-19 spread and, in conjunction with sociodemographic variables, have helped identify population health disparities concerning COVID-19 in the US. Municipality-level data are essential for advancing more targeted and nuanced understanding of geographic-based risk and resilience associated with COVID-19. We created a dataset that tracks COVID-19 cases and deaths by municipalities in the state of New Jersey (NJ), US, from April 22, 2020 to December 31, 2020. Data were drawn primarily from official county and municipality websites. The dataset is a spreadsheet containing cumulative case counts and case rates in each municipaly on three target dates, representing the peak of the first wave, the summer trough after the first wave, and the outbreak of the second wave in NJ. This dataset is valuable for four main reasons. First, the dataset is unique, because New Jersey's Health Department does not release COVID-19 data for the 77% (433/565) of municipalities with populations smaller than 20,000 individuals. Second, especially when combined with other data sources, such as publicly available sociodemographic data, this dataset can be used to advance epidemiological research on geographic differences in COVID-19, as well as to inform decision-making concerning the allocation of resources in response to the pandemic (e.g., strategies for targeted vaccine outreach campaigns). Third, county-level data mask important variations across municipalities, so municipality-level data permit a more nuanced exploration of health disparities related to local demographics, socioeconomic conditions, and access to resources and services. New Jersey is a good state to explore these patterns, because it is the most densely-populated and racially/ethnically diverse state in the US. Fourth, New Jersey was one of the few locations in the US with a high prevalence of COVID-19 during the first wave of the pandemic in the US. Thus, this dataset permits exploration of whether sociodemographic variables predicted COVID-19 differently as time progressed. To summarize, this unique municipality-level dataset in a diverse state with high COVID-19 cases is valuable for scholars and policy analysts to explore social and environmental factors related to the prevalence and transmission of COVID-19 in the US.

SARS-CoV-2 spike protein dictates syncytium-mediated lymphocyte elimination.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus is highly contagious and causes lymphocytopenia, but the underlying mechanisms are poorly understood. We demonstrate here that heterotypic cell-in-cell structures with lymphocytes inside multinucleate syncytia are prevalent in the lung tissues of coronavirus disease 2019 (COVID-19) patients. These unique cellular structures are a direct result of SARS-CoV-2 infection, as the expression of the SARS-CoV-2 spike glycoprotein is sufficient to induce a rapid (~45.1 nm/s) membrane fusion to produce syncytium, which could readily internalize multiple lines of lymphocytes to form typical cell-in-cell structures, remarkably leading to the death of internalized cells. This membrane fusion is dictated by a bi-arginine motif within the polybasic S1/S2 cleavage site, which is frequently present in the surface glycoprotein of most highly contagious viruses. Moreover, candidate anti-viral drugs could efficiently inhibit spike glycoprotein processing, membrane fusion, and cell-in-cell formation. Together, we delineate a molecular and cellular rationale for SARS-CoV-2 pathogenesis and identify novel targets for COVID-19 therapy.

Meaning making helps cope with COVID-19: A longitudinal study.

Meaning making is a useful coping strategy in negative situations. We investigated whether making meaning in negative experiences (MINE) would help people cope with COVID-19. We conducted a three-wave longitudinal study (N = 2364) three months before, during, and after the COVID-19 outbreak in China. Results showed that participants reported increased tendency of MINE during the COVID-19 outbreak than three months before the outbreak. Moreover, both initial MINE and the increased MINE predicted less psychological distress including depression, anxiety and stress, during and three months after the outbreak. Perceived benefits and costs of the COVID-19 mediated the long-term effect of MINE. These findings not only provide novel evidence for meaning making model but also shed light on the underlying mechanism, suggesting an effective strategy to cope with stressful events such as the ongoing COVID-19 pandemic.

Impact of salt substitute and stepwise reduction of salt supply on blood pressure in residents in senior residential facilities: Design and rationale of the DECIDE-Salt trial.

BACKGROUND: High sodium intake has been considered as the leading dietary risk factor for deaths and disability-adjusted life-years among older adults. High-quality randomized trials to evaluate the effects of practical sodium reduction strategies are needed. METHODS: The study is a cluster randomized trial with a 2 × 2 factorial design conducted in 48 senior residential facilities in northern China. These facilities are randomly assigned (1:1:1:1) to 1 of 4 groups: stepwise salt supply control (SSSC) in which 5%-10% of the study salt supply in the institutional kitchens will be reduced every 3 months, replacing normal salt with salt substitute (SS); SSSC only; SS only; or neither SSSC nor SS. The interventions last for 2 years with follow-up every 6 months. The primary outcome is the change in systolic blood pressure from baseline to 24 months. Secondary outcomes include the incidence of hyperkalemia, hyponatremia, cardiovascular events, and death. CURRENT STATUS: The study has recruited and randomized 48 senior residential facilities with 1,606 participants. Mean age at baseline was 71 years, and 76% are male. Both types of salt intervention were initiated in the study facilities between January and April 2018. CONCLUSION: The study is well placed to define the effects of 2 practical and scalable sodium reduction strategies for blood pressure reduction and will provide important new data about safety of these strategies among older adults in China.