Psychological resilience and cognitive reappraisal mediate the effects of coping style on the mental health of children.

Introduction: This study explored the effects of coping style and two potential intermediately factors (cognitive reappraisal and psychological resilience) on the mental health of middle school students during the normalization of epidemic prevention and control in China. Methods: Answers on questionnaires designed to assess coping style, cognitive reappraisal, psychological resilience, and mental health among 743 middle school students (386 boys, 357 girls, 241 first graders, 235 second graders, and 267 third graders) were analyzed using structural equation modeling. Results: The results showed that coping style, cognitive reappraisal, and psychological resilience directly predicted mental health. The negative effect of a negative coping style on mental health was significantly stronger than the positive effect of a positive coping style. Coping style affected mental health through the independent mediating effects of cognitive reappraisal and psychological resilience and through their chain mediation. Discussion: The use of positive coping styles by most students led to greater cognitive reappraisal, strengthened psychological resilience, and thus few mental health problems. These findings provide empirical evidence and may guide educators in the prevention and intervention of mental health problems among middle school students.

Single-cell transcriptomics reveal a unique memory-like NK cell subset that accumulates with ageing and correlates with disease severity in COVID-19.

BACKGROUND: Natural killer (NK) cells are innate lymphoid cells that mediate antitumour and antiviral responses. However, very little is known about how ageing influences human NK cells, especially at the single-cell level. METHODS: We applied single-cell sequencing (scRNA-seq) to human lymphocytes and NK cells from 4 young and 4 elderly individuals and then analysed the transcriptome data using Seurat. We detected the proportion and phenotype of NK cell subsets in peripheral blood samples from a total of 62 young and 52 elderly healthy donors by flow cytometry. We also used flow cytometry to examine the effector functions of NK cell subsets upon IFN-α/IL-12+IL-15/K562/IL-2 stimulation in vitro in peripheral blood samples from a total of 64 young and 63 elderly healthy donors. We finally studied and integrated single-cell transcriptomes of NK cells from 15 young and 41 elderly COVID-19 patients with those from 12 young and 6 elderly healthy control individuals to investigate the impacts of ageing on NK cell subsets in COVID-19 disease. RESULTS: We discovered a memory-like NK subpopulation (NK2) exhibiting the largest distribution change between elderly and young individuals among lymphocytes. Notably, we discovered a unique NK subset that was predominantly CD52+ NK2 cells (NK2.1). These memory-like NK2.1 cells accumulated with age, exhibited proinflammatory characteristics, and displayed a type I interferon response state. Integrative analyses of a large-cohort COVID-19 dataset and our datasets revealed that NK2.1 cells from elderly COVID-19 patients are enriched for type I interferon signalling, which is positively correlated with disease severity in COVID-19. CONCLUSIONS: We identified a unique memory-like NK cell subset that accumulates with ageing and correlates with disease severity in COVID-19. Our results identify memory-like NK2.1 cells as a potential target for developing immunotherapies for infectious diseases and for addressing age-related dysfunctions of the immune system.

Persistent SARS-CoV-2-positive over 4 months in a COVID-19 patient with CHB.

In recent months, the novel coronavirus disease 2019 (COVID-19) pandemic has become a major public health crisis with takeover more than 1 million lives worldwide. The long-lasting existence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has not yet been reported. Herein, we report a case of SARS-CoV-2 infection with intermittent viral polymerase chain reaction (PCR)-positive for >4 months after clinical rehabilitation. A 35-year-old male was diagnosed with COVID-19 pneumonia with fever but without other specific symptoms. The treatment with lopinavir-ritonavir, oxygen inhalation, and other symptomatic supportive treatment facilitated recovery, and the patient was discharged. However, his viral PCR test was continually positive in oropharyngeal swabs for >4 months after that. At the end of June 2020, he was still under quarantine and observation. The contribution of current antivirus therapy might be limited. The prognosis of COVID-19 patients might be irrelevant to the virus status. Thus, further investigation to evaluate the contagiousness of convalescent patients and the mechanism underlying the persistent existence of SARS-CoV-2 after recovery is essential. A new strategy of disease control, especially extending the follow-up period for recovered COVID-19 patients, is necessary to adapt to the current situation of pandemic.

An up-to-date overview of computational polypharmacology in modern drug discovery.

INTRODUCTION: In recent years, computational polypharmacology has gained significant attention to study the promiscuous nature of drugs. Despite tremendous challenges, community-wide efforts have led to a variety of novel approaches for predicting drug polypharmacology. In particular, some rapid advances using machine learning and artificial intelligence have been reported with great success. AREAS COVERED: In this article, the authors provide a comprehensive update on the current state-of-the-art polypharmacology approaches and their applications, focusing on those reports published after our 2017 review article. The authors particularly discuss some novel, groundbreaking concepts, and methods that have been developed recently and applied to drug polypharmacology studies. EXPERT OPINION: Polypharmacology is evolving and novel concepts are being introduced to counter the current challenges in the field. However, major hurdles remain including incompleteness of high-quality experimental data, lack of in vitro and in vivo assays to characterize multi-targeting agents, shortage of robust computational methods, and challenges to identify the best target combinations and design effective multi-targeting agents. Fortunately, numerous national/international efforts including multi-omics and artificial intelligence initiatives as well as most recent collaborations on addressing the COVID-19 pandemic have shown significant promise to propel the field of polypharmacology forward.