Psychological interventions for loneliness and social isolation among older adults during medical pandemics: a systematic review and meta-analysis.

OBJECTIVES: There is little research conducted to systematically synthesize the evidence on psychological interventions for social isolation and loneliness among older adults during medical pandemics. This systematic review aims to address this information gap and provides guidance for planning and implementing interventions to prevent and reduce loneliness and social isolation for older adults, especially during medical pandemics. METHODS: Four electronic databases (EMBASE, PsychoInfo, Medline and Web of Science) and grey literature from 1 January 2000 to 13 September 2022 were searched for eligible studies on loneliness and social isolation. Data extraction and methodological quality assessment on key study characteristics were conducted independently by two researchers. Both qualitative synthesis and meta-analysis were used. RESULTS: The initial search yielded 3,116 titles. Of the 215 full texts reviewed, 12 intervention articles targeting loneliness during the COVID-19 pandemic met the inclusion criteria. No studies were found concerning intervention with respect to social isolation. Overall, interventions targeting social skills and the elimination of negativities effectively alleviated the feelings of loneliness in the older population. However, they had only short-term effects. CONCLUSION: This review systematically summarised the key characteristics and the effectiveness of existing interventions addressing loneliness in older adults during the COVID-19 pandemic. Future interventions should focus on social skills and eliminating negativities and be tailored to the needs and characteristics of older people. Repeated larger-scale randomized controlled trials and long-term effectiveness evaluations on this topic are warranted.

How to manage comorbidities in people with dementia: A scoping review.

BACKGROUND: People with dementia experience a high prevalence of comorbidities that seriously affect patient outcomes. The aim of this study was to map the evidence and components related to comorbidity management, including interventions to facilitate and support the practice of management. METHODS: A scoping review was conducted. In June 2022, PubMed, Web of Science, Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), The National Institute of Health and Care Excellence (NICE), Open grey, and the Cochrane Library were searched to identify relevant literature. The inclusion criteria were outlined to identify studies on comorbidity management in people with dementia. RESULTS: We found 43 items that met the inclusion criteria. The majority of the studies were published since 2010. Most research focused on medication management, health care service use and provision, and comorbidity-related monitoring and management; there were a small number of studies that involved decision-making. Only 6 studies developed interventions to support dementia care, which included comorbidity management. Studies involving the comorbidity management process were mainly based on qualitative methods, which make it difficult to quantify the impact of these processes on comorbidity management. CONCLUSIONS: Given the serious impact of dementia on managing comorbidities, there is a need to develop systematic interventions targeting the management of comorbidities.

Prevalence of loneliness and social isolation among older adults during the COVID-19 pandemic: A systematic review and meta-analysis.

OBJECTIVES: Pandemics and their public health control measures have generally substantially increased the level of loneliness and social isolation in the general population. Because of the circumstances of aging, older adults are more likely to experience social isolation and loneliness during pandemics. However, no systematic review has been conducted or published on the prevalence of loneliness and/or social isolation among the older population. This systematic review and meta-analysis aims to provide up-to-date pooled estimates of the prevalence of social isolation and loneliness among older adults during the COVID-19 pandemic and other pandemics in the last two decades. DESIGN: EMBASE, PsychoINFO, Medline, and Web of Science were searched for relevant studies from January 1, 2000 to November 31, 2021 published in a variety of languages. Only studies conducted during the COVID-19 pandemic were selected in the review. RESULTS: A total of 30 studies including 28,050 participants met the inclusion criteria. Overall, the pooled period prevalence of loneliness among older adults was 28.6% (95% CI: 22.9-35.0%) and 31.2% for social isolation (95% CI: 20.2-44.9%). Prevalence estimates were significantly higher for those studies conducted post 3-month from the start of the COVID-19 pandemic compared to those conducted within the first 3 months of the pandemic. CONCLUSIONS: This review identifies the need for good quality longitudinal studies to examine the long-term impact of pandemics on loneliness and social isolation among older populations. Health policymaking and healthcare systems should proactively address the rising demand for appropriate psychological services among older adults.

PTSD is not the emblematic disorder of the COVID-19 pandemic; adjustment disorder is.

BACKGROUND: Posttraumatic stress disorder (PTSD) has been hailed by some as the emblematic mental disorder of the COVID-19 pandemic, assuming that PTSD's life-threat criterion was met de facto. More plausible outcomes like adjustment disorder (AD) have been overlooked. METHODS: An online cross-sectional survey was launched in the initial stage of the pandemic using a convenience sample of 5 913 adults to compare the prevalence of COVID-related probable PTSD versus probable AD. The abridged Impact of Event Scale - Revised (IES-6) assessed the severity of trauma- and stressor-related symptoms over the previous week. Demographic and pandemic-related data (e.g., receiving a formal diagnosis of COVID-19, job loss, loss of loved one, confinement, material hardship) were collected. A Classification and Regression Tree analysis was conducted to uncover the pandemic experiences leading to clinical 'caseness'. Caseness was defined by a score > 9 on the IES-6 symptom measure and further characterized as PTSD or AD depending on whether the Peritraumatic Distress Inventory's life-threat item was endorsed or not. RESULTS: The participants were predominantly Caucasian (72.8%), women (79.2%), with a university degree (85%), and a mean age of 42.22 (SD = 15.24) years; 3 647 participants (61.7%; 95%CI [60.4, 63.0]) met the threshold for caseness. However, when perceived life-threat was accounted for, only 6.7% (95%CI [6.1, 7.4]) were classified as PTSD cases, and 55% (95%CI [53.7, 56.2]) as AD cases. Among the AD cases, three distinct profiles emerged marked by the following: (i) a worst personal pandemic experience eliciting intense fear, helplessness or horror (in the absence, however, of any life-threat), (ii) a pandemic experience eliciting sadness/grief, and (iii) worrying intensely about the safety of significant others. CONCLUSIONS: Studies considering the life-threat criterion as met de facto during the pandemic are confusing PTSD for AD on most counts. This misconception is obscuring the various AD-related idioms of distress that have emerged during the pandemic and the actual treatment needs.

Probing the formation, structure and free energy relationships of M protein dimers of SARS-CoV-2.

The M protein of the novel coronavirus 2019 (SARS-CoV-2) is the major structural component of the viral envelope and is also the minimum requirement for virus particle budding. M proteins generally exist as dimers. In virus assembly, they are the main driving force for envelope formation through lateral interactions and interactions with other viral structural proteins that play a central role. We built 100 candidate models and finally analyzed the six most convincing structural features of the SARS-CoV-2 M protein dimer based on long-timescale molecular dynamics (MD) simulations, multiple free energy analyses (potential mean force (PMF) and molecular mechanics Poisson-Boltzmann surface area (MMPBSA)) and principal component analysis (PCA) to obtain the most reasonable structure. The dimer stability was found to depend on the Leu-Ile zipper motif and aromatic amino acids in the transmembrane domain (TMD). Furthermore, the C-terminal domain (CTD) effects were relatively small. These results highlight a model in which there is sufficient binding affinity between the TMDs of M proteins to form dimers through the residues at the interface of the three transmembrane helices (TMHs). This study aims to help find more effective inhibitors of SARS-CoV-2 M dimers and to develop vaccines based on structural information.

Characterization of the SARS-CoV-2 E Protein: Sequence, Structure, Viroporin, and Inhibitors.

The COVID-19 epidemic is one of the most influential epidemics in history. Understanding the impact of coronaviruses (CoVs) on host cells is very important for disease treatment. The SARS-CoV-2 envelope (E) protein is a small structural protein involved in many aspects of the viral life cycle. The E protein promotes the packaging and reproduction of the virus, and deletion of this protein weakens or even abolishes the virulence. This review aims to establish new knowledge by combining recent advances in the study of the SARS-CoV-2 E protein and by comparing it with the SARS-CoV E protein. The E protein amino acid sequence, structure, self-assembly characteristics, viroporin mechanisms and inhibitors are summarized and analyzed herein. Although the mechanisms of the SARS-CoV-2 and SARS-CoV E proteins are similar in many respects, specific studies on the SARS-CoV-2 E protein, for both monomers and oligomers, are still lacking. A comprehensive understanding of this protein should prompt further studies on the design and characterization of effective targeted therapeutic measures.

A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19).

OBJECTIVE: The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 26 million cases of Corona virus disease (COVID-19) in the world so far. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment are a priority. Pathogenic laboratory testing is typically the gold standard, but it bears the burden of significant false negativity, adding to the urgent need of alternative diagnostic methods to combat the disease. Based on COVID-19 radiographic changes in CT images, this study hypothesized that artificial intelligence methods might be able to extract specific graphical features of COVID-19 and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. METHODS: We collected 1065 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the inception transfer-learning model to establish the algorithm, followed by internal and external validation. RESULTS: The internal validation achieved a total accuracy of 89.5% with a specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with a specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images, the first two nucleic acid test results were negative, and 46 were predicted as COVID-19 positive by the algorithm, with an accuracy of 85.2%. CONCLUSION: These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. KEY POINTS: • The study evaluated the diagnostic performance of a deep learning algorithm using CT images to screen for COVID-19 during the influenza season. • As a screening method, our model achieved a relatively high sensitivity on internal and external CT image datasets. • The model was used to distinguish between COVID-19 and other typical viral pneumonia, both of which have quite similar radiologic characteristics.

Computational Study of the Ion and Water Permeation and Transport Mechanisms of the SARS-CoV-2 Pentameric E Protein Channel.

Coronavirus disease 2019 (COVID-19) is caused by a novel coronavirus (SARS-CoV-2) and represents the causative agent of a potentially fatal disease that is a public health emergency of international concern. Coronaviruses, including SARS-CoV-2, encode an envelope (E) protein, which is a small, hydrophobic membrane protein; the E protein of SARS-CoV-2 shares a high level of homology with severe acute respiratory syndrome coronavirus (SARS-CoV). In this study, we provide insights into the function of the SARS-CoV-2 E protein channel and the ion and water permeation mechanisms using a combination of in silico methods. Based on our results, the pentameric E protein promotes the penetration of cation ions through the channel. An analysis of the potential mean force (PMF), pore radius and diffusion coefficient reveals that Leu10 and Phe19 are the hydrophobic gates of the channel. In addition, the pore exhibits a clear wetting/dewetting transition with cation selectivity under transmembrane voltage, indicating that it is a hydrophobic voltage-dependent channel. Overall, these results provide structure-based insights and molecular dynamic information that are needed to understand the regulatory mechanisms of ion permeability in the pentameric SARS-CoV-2 E protein channel.