The Buffering Role of Self-compassion in the Association Between Loneliness with Depressive Symptoms: A Cross-Sectional Survey Study Among Older Adults Living in Residential Care Homes During COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic is an ongoing geriatric health emergency with a substantial increase in the prevalence of medical and mental health issues, particularly among older adults living in residential care homes. The knowledge of the risk and protective factors related to the psychological impact of the COVID-19 pandemic on older adults living in residential care homes is based on limited data. This study aimed to investigate whether loneliness mediates the effects of fear generated by a pandemic on depression. Additionally, we hypothesized that self-compassion moderates the effect of loneliness on depression. A sample comprised 323 older adults (females: n = 141, males: n = 182) with mean age = 74.98 years (standard deviation = 6.59, age 65-90) completed a survey comprising the Fear of COVID-19 Scale, De Jung Gierveld Loneliness Scale, the nine-item Patient Health Questionnaire, and the Self-compassion Scale. The results revealed that the total effect of fear on depression was statistically significant, with a medium effect size (Cohen's f 2 = .14) and this association was partially mediated by loneliness (ß = .11, SE = .04, P < .001, t = 2.91, 95% CI 0.04-0.19). The self-compassion also moderated the loneliness effect on depression. The findings of this study support COVID-19 evidence, indicating that a greater level of fear generated by the pandemic is linked to depression and loneliness. The findings support the notion that self-compassion mitigates the adverse effects of stressful events in older adults. Customized self-compassion programs may be effective loneliness-mitigating interventions for older adults living in residential care homes. Supplementary Information: The online version contains supplementary material available at 10.1007/s11469-023-01014-0.

The Buffering Role of Self-compassion in the Association Between Loneliness with Depressive Symptoms: A Cross-Sectional Survey Study Among Older Adults Living in Residential Care Homes During COVID-19

The coronavirus disease 2019 (COVID-19) pandemic is an ongoing geriatric health emergency with a substantial increase in the prevalence of medical and mental health issues, particularly among older adults living in residential care homes. The knowledge of the risk and protective factors related to the psychological impact of the COVID-19 pandemic on older adults living in residential care homes is based on limited data. This study aimed to investigate whether loneliness mediates the effects of fear generated by a pandemic on depression. Additionally, we hypothesized that self-compassion moderates the effect of loneliness on depression. A sample comprised 323 older adults (females: n = 141, males: n = 182) with mean age = 74.98 years (standard deviation = 6.59, age 65–90) completed a survey comprising the Fear of COVID-19 Scale, De Jung Gierveld Loneliness Scale, the nine-item Patient Health Questionnaire, and the Self-compassion Scale. The results revealed that the total effect of fear on depression was statistically significant, with a medium effect size (Cohen's f2 = .14) and this association was partially mediated by loneliness (β = .11, SE = .04, P < .001, t = 2.91, 95% CI 0.04–0.19). The self-compassion also moderated the loneliness effect on depression. The findings of this study support COVID-19 evidence, indicating that a greater level of fear generated by the pandemic is linked to depression and loneliness. The findings support the notion that self-compassion mitigates the adverse effects of stressful events in older adults. Customized self-compassion programs may be effective loneliness-mitigating interventions for older adults living in residential care homes. Supplementary Information The online version contains supplementary material available at 10.1007/s11469-023-01014-0.

Discovery of Potential Therapeutic Drugs for COVID-19 Through Logistic Matrix Factorization With Kernel Diffusion.

Coronavirus disease 2019 (COVID-19) is rapidly spreading. Researchers around the world are dedicated to finding the treatment clues for COVID-19. Drug repositioning, as a rapid and cost-effective way for finding therapeutic options from available FDA-approved drugs, has been applied to drug discovery for COVID-19. In this study, we develop a novel drug repositioning method (VDA-KLMF) to prioritize possible anti-SARS-CoV-2 drugs integrating virus sequences, drug chemical structures, known Virus-Drug Associations, and Logistic Matrix Factorization with Kernel diffusion. First, Gaussian kernels of viruses and drugs are built based on known VDAs and nearest neighbors. Second, sequence similarity kernel of viruses and chemical structure similarity kernel of drugs are constructed based on biological features and an identity matrix. Third, Gaussian kernel and similarity kernel are diffused. Forth, a logistic matrix factorization model with kernel diffusion is proposed to identify potential anti-SARS-CoV-2 drugs. Finally, molecular dockings between the inferred antiviral drugs and the junction of SARS-CoV-2 spike protein-ACE2 interface are implemented to investigate the binding abilities between them. VDA-KLMF is compared with two state-of-the-art VDA prediction models (VDA-KATZ and VDA-RWR) and three classical association prediction methods (NGRHMDA, LRLSHMDA, and NRLMF) based on 5-fold cross validations on viruses, drugs, and VDAs on three datasets. It obtains the best recalls, AUCs, and AUPRs, significantly outperforming other five methods under the three different cross validations. We observe that four chemical agents coming together on any two datasets, that is, remdesivir, ribavirin, nitazoxanide, and emetine, may be the clues of treatment for COVID-19. The docking results suggest that the key residues K353 and G496 may affect the binding energies and dynamics between the inferred anti-SARS-CoV-2 chemical agents and the junction of the spike protein-ACE2 interface. Integrating various biological data, Gaussian kernel, similarity kernel, and logistic matrix factorization with kernel diffusion, this work demonstrates that a few chemical agents may assist in drug discovery for COVID-19.

Clinical characteristics and predictors of mortality in young adults with severe COVID-19: a retrospective observational study.

BACKGROUND AND OBJECTIVE: Little is yet known whether pathogenesis of COVID-19 is different between young and elder patients. Our study aimed to investigate the clinical characteristics and provide predictors of mortality for young adults with severe COVID-19. METHODS: A total of 77 young adults with confirmed severe COVID-19 were recruited retrospectively at Tongji Hospital. Clinical characteristics, laboratory findings, treatment and outcomes were obtained from electronic medical records. The prognostic effects of variables were analyzed using logistic regression model. RESULTS: In this retrospective cohort, non-survivors showed higher incidence of dyspnea and co-existing laboratory abnormalities, compared with young survivals in severe COVID-19. Multivariate logistic regression analysis showed that lymphopenia, elevated level of d-dimer, hypersensitive cardiac troponin I (hs-CTnI) and high sensitivity C-reactive protein (hs-CRP) were independent predictors of mortality in young adults with severe COVID-19. Further analysis showed that severely young adults with two or more factors abnormalities above would be more prone to death. The similar predictive effect of above four factors had been observed in all-age patients with severe COVID-19. CONCLUSION: Lymphopenia, elevated level of d-dimer, hs-CTnI and hs-CRP predicted clinical outcomes of young adults with severe COVID-19.