The mediating role of psychological capital in the association between life satisfaction and depressive and anxiety symptoms among Chinese medical students during the COVID-19 pandemic: a cross-sectional study.

BACKGROUND: Although life satisfaction is a predictor of depressive and anxiety symptoms, the mechanisms underlying this association are poorly understood. This study examined how psychological capital (PsyCap), a positive psychological state, mediated the association between life satisfaction and depressive and anxiety symptoms among Chinese medical students during the COVID-19 pandemic. METHODS: A cross-sectional survey was conducted at three medical universities in China. A self-administered questionnaire was distributed to 583 students. Depressive symptoms, anxiety symptoms, life satisfaction, and PsyCap were measured anonymously. A hierarchical linear regression analysis was performed to explore the effects of life satisfaction on depressive and anxiety symptoms. Asymptotic and resampling strategies were used to examine how PsyCap mediates the association between life satisfaction and depressive and anxiety symptoms. RESULTS: Life satisfaction was positively associated with PsyCap and its four components. There were significant negative associations between life satisfaction, psychological capital, resilience, optimism, and depressive and anxiety symptoms among medical students. Self-efficacy was negatively associated with depressive and anxiety symptoms. Psychological capital (a×b = -0.3201, BCa 95% CI: -0.3899, -0.2446; a×b = -0.2749, BCa 95% CI: -0.3817, -0.1996), resilience (a×b = -0.2103, BCa 95% CI: -0.2727, -0.1580; a×b = -0.1871, BCa 95% CI: -0.2520, -0.1414), optimism (a×b = -0.2100, BCa 95% CI: -0.3388, -0.1150; a×b = -0.1998, BCa 95% CI: -0.3307, -0.0980), and self-efficacy (a×b = -0.0916, BCa 95% CI: 0.0048, 0.11629; a×b = 0.1352, BCa 95% CI: 0.0336, 0.2117) significantly mediated the association between life satisfaction and depressive and anxiety symptoms. LIMITATIONS: This was a cross-sectional study, and causal relationships between the variables could not be ascertained. Self-reported questionnaire instruments were used for data collection, which may have recall bias. CONCLUSIONS: Life satisfaction and PsyCap can be used as positive resources to reduce depressive and anxiety symptoms among third-year Chinese medical students during the COVID-19 pandemic. Psychological capital and its components (self-efficacy, resilience, and optimism) partially mediated the relationship between life satisfaction and depressive symptoms, and completely mediated the relationship between life satisfaction and anxiety symptoms. Therefore, improving life satisfaction and investing in psychological capital (especially self-efficacy, resilience, and optimism) should be included in the prevention and treatment of depressive and anxiety symptoms among third-year Chinese medical students. Additional attention is needed to pay for self-efficacy in such disadvantageous contexts.

Medical capacity investment for epidemic disease: The effects of policymaker's confidence and public trust.

Due to the server bed shortage, which has raised ethical dilemmas in the earliest days of the COVID-19 crisis, medical capacity investment has become a vital decision-making issue in the attempt to contain the epidemic. Furthermore, economic strength has failed to explain the significant performance difference across countries in combatting COVID-19. Unlike common diseases, epidemic diseases add substantial unpredictability, complexity, and uncertainty to decision-making. Knowledge miscalibration on epidemiological uncertainties by policymaker's over- and underconfidence can seriously impact policymaking. Ineffective risk communication may lead to conflicting and incoherent information transmission. As a result, public reactions and attitudes could be influenced by policymakers' confidence due to the level of public trust, which eventually affects the degree to which an epidemic spreads. To uncover the impacts of policymakers' confidence and public trust on the medical capacity investment, we establish epidemic diffusion models to characterize how transmission evolves with (and without) vaccination and frame the capacity investment problem as a newsvendor problem. Our results show that if the public fully trusts the public health experts, the policymaker's behavioral bias is always harmful, but its effect on cost increment is marginal. If a policymaker's behavior induces public reactions due to public trust, both the spread of the epidemic and the overall performance will be significantly affected, but such impacts are not always harmful. Decision bias may be beneficial when policymakers are pessimistic or highly overconfident. Having an opportunity to amend initially biased decisions can debias a particular topic but has a limited cost-saving effect.

Development of the Malay Language of understanding, attitude, practice and health literacy questionnaire on COVID-19 (MUAPHQ C-19): content validity & face validity analysis.

OBJECTIVE: This study aimed to assess the content and face validity index of the development of the understanding, attitude, practice and health literacy questionnaire on COVID-19 (MUAPHQ C-19) in the Malay language. METHODS: The development of the MUAPHQ C-19 was conducted in two stages. Stage I resulted in the generation of the instrument's items (development), and stage II resulted in the performance of the instrument's items (judgement and quantification). Six-panel experts related to the study field and ten general public participated to evaluate the validity of the MUAPHQ C-19. The content validity index (CVI), content validity ratio (CVR) and face validity index (FVI) were analysed using Microsoft Excel. RESULTS: There were 54 items and four domains, namely the understanding, attitude, practice and health literacy towards COVID-19, identified in the MUAPHQ C-19 (Version 1.0). The scale-level CVI (S-CVI/Ave) for every domain was above 0.9, which is considered acceptable. The CVR for all items was above 0.7, except for one item in the health literacy domain. Ten items were revised to improve the item's clarity, and two items were deleted due to the low CVR value and redundancy, respectively. The I-FVI exceeded the cut-off value of 0.83 except for five items from the attitude domain and four from the practice domains. Thus, seven of these items were revised to increase the clarity of items, while another two were deleted due to low I-FVI scores. Otherwise, the S-FVI/Ave for every domain exceeded the cut-off point of 0.9, which is considered acceptable. Thus, 50-item MUAPHQ C-19 (Version 3.0) was generated following the content and face validity analysis. CONCLUSIONS: The questionnaire development, content validity, and face validity process are lengthy and iterative. The assessment of the instruments' items by the content experts and the respondents is essential to guarantee the instrument's validity. Our content and face validity study has finalised the MUAPHQ C-19 version that is ready for the next phase of questionnaire validation, using Exploratory and Confirmatory Factor Analysis.

Aerosol exchange between pressure-equilibrium rooms induced by door motion and human movement.

It is now widely recognised that aerosol transport is major vector for transmission of diseases such as COVID-19, and quantification of aerosol transport in the built environment is critical to risk analysis and management. Understanding the effects of door motion and human movement on the dispersion of virus-laden aerosols under pressure-equilibrium conditions is of great significance to the evaluation of infection risks and development of mitigation strategies. This study uses novel numerical simulation techniques to quantify the impact of these motions upon aerosol transport and provides valuable insights into the wake dynamics of swinging doors and human movement. The results show that the wake flow of an opening swinging door delays aerosol escape, while that of a person walking out entrains aerosol out of the room. Aerosol escape caused by door motion mainly happens during the closing sequence which pushes the aerosols out. Parametric studies show that while an increased door swinging speed or human movement speed can enhance air exchange across the doorway, the cumulative aerosol exchange across the doorway is not clearly affected by the speeds.

SARS-CoV-2 N protein enhances the anti-apoptotic activity of MCL-1 to promote viral replication.

Viral infection in respiratory tract usually leads to cell death, impairing respiratory function to cause severe disease. However, the diversity of clinical manifestations of SARS-CoV-2 infection increases the complexity and difficulty of viral infection prevention, and especially the high-frequency asymptomatic infection increases the risk of virus transmission. Studying how SARS-CoV-2 affects apoptotic pathway may help to understand the pathological process of its infection. Here, we uncovered SARS-CoV-2 imployed a distinct anti-apoptotic mechanism via its N protein. We found SARS-CoV-2 virus-like particles (trVLP) suppressed cell apoptosis, but the trVLP lacking N protein didn't. Further study verified that N protein repressed cell apoptosis in cultured cells, human lung organoids and mice. Mechanistically, N protein specifically interacted with anti-apoptotic protein MCL-1, and recruited a deubiquitinating enzyme USP15 to remove the K63-linked ubiquitination of MCL-1, which stabilized this protein and promoted it to hijack Bak in mitochondria. Importantly, N protein promoted the replications of IAV, DENV and ZIKV, and exacerbated death of IAV-infected mice, all of which could be blocked by a MCL-1 specific inhibitor, S63845. Altogether, we identifed a distinct anti-apoptotic function of the N protein, through which it promoted viral replication. These may explain how SARS-CoV-2 effectively replicates in asymptomatic individuals without cuasing respiratory dysfunction, and indicate a risk of enhanced coinfection with other viruses. We anticipate that abrogating the N/MCL-1-dominated apoptosis repression is conducive to the treatments of SARS-CoV-2 infection as well as coinfections with other viruses.

Artificial intelligence in public health: the potential of epidemic early warning systems.

The use of artificial intelligence (AI) to generate automated early warnings in epidemic surveillance by harnessing vast open-source data with minimal human intervention has the potential to be both revolutionary and highly sustainable. AI can overcome the challenges faced by weak health systems by detecting epidemic signals much earlier than traditional surveillance. AI-based digital surveillance is an adjunct to-not a replacement of-traditional surveillance and can trigger early investigation, diagnostics and responses at the regional level. This narrative review focuses on the role of AI in epidemic surveillance and summarises several current epidemic intelligence systems including ProMED-mail, HealthMap, Epidemic Intelligence from Open Sources, BlueDot, Metabiota, the Global Biosurveillance Portal, Epitweetr and EPIWATCH. Not all of these systems are AI-based, and some are only accessible to paid users. Most systems have large volumes of unfiltered data; only a few can sort and filter data to provide users with curated intelligence. However, uptake of these systems by public health authorities, who have been slower to embrace AI than their clinical counterparts, is low. The widespread adoption of digital open-source surveillance and AI technology is needed for the prevention of serious epidemics.

Psychological state and its correlates of local college students in Wuhan during COVID-19 pandemic.

In 2020, the lockdown of Wuhan due to the outbreak of COVID-19 impacted various aspects of local college students' life and may further negatively affect their psychological state. This study was conducted among 652 Wuhan local college students during the quarantine of this city. We assessed their psychological state using Depression-Anxiety-Stress Scale 21 and evaluated their living condition including diet, schedule, recreational activities, social contact, academic life, and attention paid to pandemic news. Results showed that 16.87% of the students reported stress, 28.68% with anxiety, and 35.12% had depression. According to multivariate logistic regression analysis, having a medical background was associated with higher stress levels; students who had an irregular diet and schedule were more likely to develop stress, anxiety, and depression; students with their academic life affected had a higher prevalence of anxiety and depression. By studying local students in the hardest-hit area during the pandemic, our findings can provide references for the improvement of college students' mental health in the long term.

Collaborative Filtering Model of Graph Neural Network Based on Random Walk

This paper proposes a novel graph neural network recommendation method to alleviate the user cold-start problem caused by too few relevant items in personalized recommendation collaborative filtering. A deep feedforward neural network is constructed to transform the bipartite graph of user–item interactions into the spectral domain, using a random wandering method to discover potential correlation information between users and items. Then, a finite-order polynomial is used to optimize the convolution process and accelerate the convergence of the convolutional network, so that deep connections between users and items in the spectral domain can be discovered quickly. We conducted experiments on the classic dataset MovieLens-1M. The recall and precision were improved, and the results show that the method can improve the accuracy of recommendation results, tap the association information between users and items more effectively, and significantly alleviate the user cold-start problem.

Tissue injury and leukocyte changes in post-acute sequelae of SARS-CoV-2: review of 2833 post-acute patient outcomes per immune dysregulation and microbial translocation in long COVID.

A significant number of persons with coronavirus disease 2019 (COVID-19) experience persistent, recurrent, or new symptoms several months after the acute stage of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. This phenomenon, termed post-acute sequelae of SARS-CoV-2 (PASC) or long COVID, is associated with high viral titers during acute infection, a persistently hyperactivated immune system, tissue injury by NETosis-induced micro-thrombofibrosis (NETinjury), microbial translocation, complement deposition, fibrotic macrophages, the presence of autoantibodies, and lymphopenic immune environments. Here, we review the current literature on the immunological imbalances that occur during PASC. Specifically, we focus on data supporting common immunopathogenesis and tissue injury mechanisms shared across this highly heterogenous disorder, including NETosis, coagulopathy, and fibrosis. Mechanisms include changes in leukocyte subsets/functions, fibroblast activation, cytokine imbalances, lower cortisol, autoantibodies, co-pathogen reactivation, and residual immune activation driven by persistent viral antigens and/or microbial translocation. Taken together, we develop the premise that SARS-CoV-2 infection results in PASC as a consequence of acute and/or persistent single or multiple organ injury mediated by PASC determinants to include the degree of host responses (inflammation, NETinjury), residual viral antigen (persistent antigen), and exogenous factors (microbial translocation). Determinants of PASC may be amplified by comorbidities, age, and sex.

Risk Factors for COVID-19 Mortality in Malaysia.

Background: Understanding the risks of COVID-19 mortality helps in the planning and prevention of the disease. This study aimed to determine the risk factors for COVID-19 mortality in Malaysia. Methods: Secondary online data provided by the Ministry of Health, Malaysia and Malaysia's national COVID-19 immunisation programme were used: i) COVID-19 deaths data; ii) vaccination coverage data and iii) population estimate data. Quasi-Poisson regression was performed to determine the risk factors for COVID-19 mortality. Results: Four risk factors were identified: i) vaccination status (partial versus unvaccinated, incidence rate ratio [IRR]: 0.59; 95% CI: 0.54, 0.64; complete versus unvaccinated, IRR: 0.50; 95% CI: 0.45, 0.56; booster versus unvaccinated, IRR: 0.13; 95% CI: 0.05, 0.26); ii) age group (19 years old-59 years old versus above 60 years old, IRR: 0.90; 95% CI: 0.84, 0.97; 13 years old-18 years old versus above 60 years old, IRR: 0.09; 95% CI: 0.04, 0.19; 6 years old-12 years old versus above 60 years old, IRR: 0.09; 95% CI: 0.03, 0.22; below 5 years old versus above 60 years old, IRR: 0.11; 95% CI: 0.04, 0.23); iii) gender (male versus female, IRR: 1.23; 95% CI: 1.14, 1.32) and iv) comorbidity (yes versus no, IRR: 2.13; 95% CI: 1.96, 2.32). Conclusion: This study highlighted the risk factors for COVID-19 mortality and the benefit of COVID-19 vaccination, especially of booster vaccination, in reducing the risk of COVID-19 mortality in Malaysia.

Development of an enzyme-linked immunosorbent assay based on viral antigen capture by anti-spike glycoprotein monoclonal antibody for detecting immunoglobulin A antibodies against porcine epidemic diarrhea virus in milk.

BACKGROUND: Porcine epidemic diarrhea (PED), caused by PED virus (PEDV), is a severe enteric disease burdening the global swine industry in recent years. Especially, the mortality of PED in neonatal piglets approaches 100%. Maternal antibodies in milk, particularly immunoglobulin A (IgA) antibodies, are of great importance for protection neonatal suckling piglets against PEDV infection as passive lactogenic immunity. Therefore, appropriate detection methods are required for detecting PEDV IgA antibodies in milk. In the current study, we prepared monoclonal antibodies (mAbs) against PEDV spike (S) glycoprotein. An enzyme-linked immunosorbent assay (ELISA) was subsequently developed based on PEDV antigen capture by a specific anti-S mAb. RESULTS: The developed ELISA showed high sensitivity (the maximum dilution of milk samples up to 1:1280) and repeatability (coefficient of variation values < 10%) in detecting PEDV IgA antibody positive and negative milk samples. More importantly, the developed ELISA showed a high coincidence rate with a commercial ELISA kit for PEDV IgA antibody detection in clinical milk samples. CONCLUSIONS: The developed ELISA in the current study is applicable for PEDV IgA antibody detection in milk samples, which is beneficial for evaluating vaccination efficacies and neonate immune status against the virus.

Ultrasound-Guided Capsular Thyroid Injection Therapy With Dexamethasone and Lidocaine Mixture for Subacute Thyroiditis: A Single-Center Study.

OBJECTIVES: Subacute thyroiditis (SAT) is a self-limiting, inflammatory thyroid disease possibly caused by viral infection. In recent years, the incidence of SAT is increasing, especially during the pandemic of the COVID-19. This study aimed to evaluate the efficacy, safety, and recovery time of capsular thyroid injection therapy under ultrasound guidance for SAT. METHODS: A total of 73 patients with SAT were divided into two groups. Patients in group A (n = 48) received an ultrasound-guided capsular injection consisting of dexamethasone (DEX) and lidocaine in the thyroid lesion area, while patients in group B (n = 25) received oral prednisolone (PSL). The two groups were compared for pain relief and treatment duration, the recovery time of thyroid function, recurrence rates, hypothyroidism incidence, and drug-related side effects. RESULTS: The follow-up time was 1 year. In group A, the duration of pain relief, treatment, and recovery time of thyroid function were significantly shorter than that in group B (P < .05), and no statistically significant differences in recurrence rate or incidence of hypothyroidism were observed (P > .05). Weight gain was significantly higher in group A at the end of treatment (P < .001). CONCLUSIONS: Compared with oral PSL treatment, ultrasound-guided local injection of DEX and lidocaine into the capsular thyroid is a safe and effective procedure that can significantly reduce the treatment time of SAT.

Machine-learning assisted scheduling optimization and its application in quantum chemical calculations.

Easy and effective usage of computational resources is crucial for scientific calculations, both from the perspectives of timeliness and economic efficiency. This work proposes a bi-level optimization framework to optimize the computational sequences. Machine-learning (ML) assisted static load-balancing, and different dynamic load-balancing algorithms can be integrated. Consequently, the computational and scheduling engine of the ParaEngine is developed to invoke optimized quantum chemical (QC) calculations. Illustrated benchmark calculations include high-throughput drug suit, solvent model, P38 protein, and SARS-CoV-2 systems. The results show that the usage rate of given computational resources for high throughput and large-scale fragmentation QC calculations can primarily profit, and faster accomplishing computational tasks can be expected when employing high-performance computing (HPC) clusters.

A TLR7-nanoparticle adjuvant promotes a broad immune response against heterologous strains of influenza and SARS-CoV-2.

The ideal vaccine against viruses such as influenza and SARS-CoV-2 must provide a robust, durable and broad immune protection against multiple viral variants. However, antibody responses to current vaccines often lack robust cross-reactivity. Here we describe a polymeric Toll-like receptor 7 agonist nanoparticle (TLR7-NP) adjuvant, which enhances lymph node targeting, and leads to persistent activation of immune cells and broad immune responses. When mixed with alum-adsorbed antigens, this TLR7-NP adjuvant elicits cross-reactive antibodies for both dominant and subdominant epitopes and antigen-specific CD8+ T-cell responses in mice. This TLR7-NP-adjuvanted influenza subunit vaccine successfully protects mice against viral challenge of a different strain. This strategy also enhances the antibody response to a SARS-CoV-2 subunit vaccine against multiple viral variants that have emerged. Moreover, this TLR7-NP augments antigen-specific responses in human tonsil organoids. Overall, we describe a nanoparticle adjuvant to improve immune responses to viral antigens, with promising implications for developing broadly protective vaccines.

Investigating Derivatives of Tanshinone IIA Sulfonate Sodium and Chloroxine for Their Inhibition Activities against the SARS-CoV-2 Papain-like Protease.

SARS-CoV-2 has caused a global pandemic of COVID-19, posing a huge threat to public health. The SARS-CoV-2 papain-like cysteine protease (PLpro) plays a significant role in virus replication and host immune regulation, which is a promising antiviral drug target. Several potential inhibitors have been identified in vitro. However, the detailed mechanism of action and structure-activity relationship require further studies. Here, we investigated the structure-activity relationships of the series of derivatives of tanshinone IIA sulfonate sodium (TSS) and chloroxine based on biochemical analysis and molecular dynamics simulation. We found that compound 7, a derivative of chloroxine, can disrupt PLpro-ISG15 interaction and exhibits an antiviral effect for SARS-CoV-2 variants (wild type, delta, and omicron) at the low micromolar level. These studies confirmed that inhibiting PLpro-ISG15 interaction and, thus, restoring the host's innate immunity are effective methods for fighting against viral infection.

A vaccine delivery system promotes strong immune responses against SARS-CoV-2 variants.

Global coronavirus disease 2019 (COVID-19) pandemics highlight the need of developing vaccines with universal and durable protection against emerging SARS-CoV-2 variants. Here we developed an extended-release vaccine delivery system (GP-diABZI-RBD), consisting the original SARS-CoV-2 WA1 strain receptor-binding domain (RBD) as the antigen and diABZI stimulator of interferon genes (STING) agonist in conjunction with yeast ß-glucan particles (GP-diABZI) as the platform. GP-diABZI-RBD could activate STING pathway and inhibit SARS-CoV-2 replication. Compared to diABZI-RBD, intraperitoneal injection of GP-diABZI-RBD elicited robust cellular and humoral immune responses in mice. Using SARS-CoV-2 GFP/ΔN transcription and replication-competent virus-like particle system (trVLP), we demonstrated that GP-diABZI-RBD-prototype vaccine exhibited the strongest and durable humoral immune responses and antiviral protection; whereas GP-diABZI-RBD-Omicron displayed minimum neutralization responses against trVLP. By using pseudotype virus (PsVs) neutralization assay, we found that GP-diABZI-RBD-Prototype, GP-diABZI-RBD-Delta, and GP-diABZI-RBD-Gamma immunized mice sera could efficiently neutralize Delta and Gamma PsVs, but had weak protection against Omicron PsVs. In contrast, GP-diABZI-RBD-Omicron immunized mice sera displayed the strongest neutralization response to Omicron PsVs. Taken together, the results suggest that GP-diABZI can serve as a promising vaccine delivery system for enhancing durable humoral and cellular immunity against broad SARS-CoV-2 variants. Our study provides important scientific basis for developing SARS-CoV-2 VOC-specific vaccines.

ZBP1-Mediated Necroptosis: Mechanisms and Therapeutic Implications.

Cell death is a fundamental pathophysiological process in human disease. The discovery of necroptosis, a form of regulated necrosis that is induced by the activation of death receptors and formation of necrosome, represents a major breakthrough in the field of cell death in the past decade. Z-DNA-binding protein (ZBP1) is an interferon (IFN)-inducing protein, initially reported as a double-stranded DNA (dsDNA) sensor, which induces an innate inflammatory response. Recently, ZBP1 was identified as an important sensor of necroptosis during virus infection. It connects viral nucleic acid and receptor-interacting protein kinase 3 (RIPK3) via two domains and induces the formation of a necrosome. Recent studies have also reported that ZBP1 induces necroptosis in non-viral infections and mediates necrotic signal transduction by a unique mechanism. This review highlights the discovery of ZBP1 and its novel findings in necroptosis and provides an insight into its critical role in the crosstalk between different types of cell death, which may represent a new therapeutic option.

Learning burnout and its association with perceived stress, social support, and the Big Five personality traits in Chinese medical students during the COVID-19 pandemic: a cross-sectional study.

BACKGROUND: Owing to the coronavirus disease 2019, medical learning burnout has attracted increasing attention in educational research. It has a serious negative impact on medical students and their service quality. This could impair the professional development of medical students; weaken their personal and professional quality; and lead to problems such as increased medical errors and reduced patient care quality and satisfaction. This study aimed to examine the effects of perceived stress, social support, and the Big Five personality traits on learning burnout among medical students. METHODS: In November 2021, a cross-sectional survey was conducted at three medical universities in China. A self-administered questionnaire was distributed to 616 third- year students. Learning burnout, perceived stress, social support, and the Big Five personality traits (neuroticism, extroversion, openness, agreeableness, and conscientiousness) were anonymously measured. A total of 583 students were included in the final sample. Hierarchical linear regression was performed to explore the effects of perceived stress, social support, and Big Five personality traits on medical students' learning burnout. RESULTS: Perceived stress was positively associated with learning burnout (emotional exhaustion: ß = 0.577, p < 0.001; cynicism: ß = 0.543, p < 0.001; low professional efficacy: ß = 0.455, p < 0.001) whereas social support was negatively related with it (low professional efficacy: ß = -0.319, p < 0.001). Neuroticism had a positive effect on emotional burnout (ß = 0.152, p = 0.009). Extraversion (ß = -0.116, p = 0.006) and conscientiousness (ß = -0.363, p < 0.001) had a negative effect on low professional efficacy. Agreeableness negatively affected emotional exhaustion (ß = -0.181, p < 0.001) and cynicism (ß = -0.245, p < 0.001) and positively affected low professional efficacy (ß = 0.098, p = 0.008). The associated factors together accounted for an additional variance of learning burnout (emotional exhaustion: 39.0%; cynicism: 36.8%; low professional efficacy: 48.7%). CONCLUSIONS: Social support is a positive resource for fighting medical students' burnout. Perceived stress was the strongest indicator of learning burnout. In addition to reducing perceived stress, developing extraversion, agreeableness, and conscientiousness should be included in burnout prevention and treatment strategies, particularly for medical students.

Factors Affecting Willingness to Receive Online Counseling: The Mediating Role of Ethical Concerns.

Since the outbreak of the COVID-19 pandemic, traditional face-to-face counseling has gradually given way to online counseling. To improve the application value of online counseling and change the current situation of college students' lack of willingness to receive online counseling, this study explored factors that influence Chinese college students' willingness to receive online counseling (WROC). Based on data gathered from surveying 823 Chinese college students using self-report questionnaires, we clarified the relationships between the self-stigma of seeking help, ethical concerns about online counseling (ECOC), online interpersonal trust (OIT), and the willingness to receive online counseling (WROC). The results indicated that (1) self-stigma of seeking help and OIT negatively and positively predicted the WROC, respectively; (2) ethical concerns negatively predicted the WROC; and (3) ethical concerns mediated the relationship between self-stigma and WROC and between OIT and WROC. The results suggest that reducing the self-stigma surrounding help-seeking, perfecting the ethical norms of online counseling, and enhancing interpersonal trust can improve willingness to receive online counseling.

Integration of machine learning prediction and heuristic optimization for mask delivery in COVID-19.

The novel coronavirus pneumonia (COVID-19) has created huge demands for medical masks that need to be delivered to a lot of demand points to protect citizens. The efficiency of delivery is critical to the prevention and control of the epidemic. However, the huge demands for masks and massive number of demand points scattered make the problem highly complex. Moreover, the actual demands are often obtained late, and hence the time duration for solution calculation and mask delivery is often very limited. Based on our practical experience of medical mask delivery in response to COVID-19 in China, we present a hybrid machine learning and heuristic optimization method, which uses a deep learning model to predict the demand of each region, schedules first-echelon vehicles to pre-distribute the predicted number of masks from depot(s) to regional facilities in advance, reassigns demand points among different regions to balance the deviations of predicted demands from actual demands, and finally routes second-echelon vehicles to efficiently deliver masks to the demand points in each region. For the subproblems of demand point reassignment and two-batch routing whose complexities are significantly lower, we propose variable neighborhood tabu search heuristics to efficiently solve them. Application of the proposed method in emergency mask delivery in three megacities in China during the peak of COVID-19 demonstrated its significant performance advantages over other methods without pre-distribution or reassignment. We also discuss key success factors and lessons learned to facilitate the extension of our method to a wider range of problems.