PD-1 and ICOS counter-regulate tissue resident regulatory T cell development and IL-10 production during flu.

Regulatory T cells that express the transcription factor Foxp3 (Treg cells) are a highly heterogenous population of immunoregulatory cells critical for maintaining immune homeostasis and preventing immunopathology during infections. Tissue resident Treg (TR-Treg) cells are maintained within nonlymphoid tissues and have been shown to suppress proinflammatory tissue resident T cell responses and promote tissue repair. Human populations are repetitively exposed to influenza infections and lung tissue resident effector T cell responses are associated with flu-induced long-term pulmonary sequelae. The kinetics of TR-Treg cell development and molecular features of TR-Treg cells during repeated and/or long-term flu infections are unclear. Utilizing a Foxp3RFP/IL-10GFP dual reporter mouse model along with intravascular fluorescent in vivo labeling, we characterized the TR-Treg cell responses to repetitive heterosubtypic influenza infections. We found lung tissue resident Treg cells accumulated and expressed high levels of co-inhibitory and co-stimulatory receptors post primary and secondary infections. Blockade of PD-1 or ICOS signaling reveals that PD-1 and ICOS signaling pathways counter-regulate TR-Treg cell expansion and IL-10 production, during secondary influenza infection. Furthermore, the virus-specific TR-Treg cell response displayed distinct kinetics, when compared to conventional CD4+ tissue resident memory T cells, during secondary flu infection. Our results provide insight into the tissue resident Foxp3+ regulatory T cell response during repetitive flu infections, which may be applicable to other respiratory infectious diseases such as tuberculosis and COVID.

Topoisomerase 3b is dispensable for replication of a positive-sense RNA virus--murine coronavirus.

A recent study demonstrated that a DNA-RNA dual-activity topoisomerase complex, TOP3B-TDRD3, is required for normal replication of positive-sense RNA viruses, including several human flaviviruses and coronaviruses; and the authors proposed that TOP3B is a target of antiviral drugs. Here we examined this hypothesis by investigating whether inactivation of Top3b can inhibit the replication of a mouse coronavirus, MHV, using cell lines and mice that are inactivated of Top3b or Tdrd3. We found that Top3b-KO or Tdrd3-KO cell lines generated by different CRISPR-CAS9 guide RNAs have variable effects on MHV replication. In addition, we did not find significant changes of MHV replication in brains or lungs in Top3B-KO mice. Moreover, immunostaining showed that Top3b proteins are not co-localized with MHV replication complexes but rather, localized in stress granules in the MHV-infected cells. Our results suggest that Top3b does not have a universal role in promoting replication of positive-sense RNA virus, and cautions should be taken when targeting it to develop anti-viral drugs.

Net-Shaped DNA Nanostructures Designed for Rapid/Sensitive Detection and Potential Inhibition of the SARS-CoV-2 Virus.

We present a net-shaped DNA nanostructure (called "DNA Net" herein) design strategy for selective recognition and high-affinity capture of intact SARS-CoV-2 virions through spatial pattern-matching and multivalent interactions between the aptamers (targeting wild-type spike-RBD) positioned on the DNA Net and the trimeric spike glycoproteins displayed on the viral outer surface. Carrying a designer nanoswitch, the DNA Net-aptamers release fluorescence signals upon virus binding that are easily read with a handheld fluorimeter for a rapid (in 10 min), simple (mix-and-read), sensitive (PCR equivalent), room temperature compatible, and inexpensive (∼$1.26/test) COVID-19 test assay. The DNA Net-aptamers also impede authentic wild-type SARS-CoV-2 infection in cell culture with a near 1 × 103-fold enhancement of the monomeric aptamer. Furthermore, our DNA Net design principle and strategy can be customized to tackle other life-threatening and economically influential viruses like influenza and HIV, whose surfaces carry class-I viral envelope glycoproteins like the SARS-CoV-2 spikes in trimeric forms.

Mutations and Evolution of the SARS-CoV-2 Spike Protein.

The SARS-CoV-2 spike protein mediates target recognition, cellular entry, and ultimately the viral infection that leads to various levels of COVID-19 severities. Positive evolutionary selection of mutations within the spike protein has led to the genesis of new SARS-CoV-2 variants with greatly enhanced overall fitness. Given the trend of variants with increased fitness arising from spike protein alterations, it is critical that the scientific community understand the mechanisms by which these mutations alter viral functions. As of March 2022, five SARS-CoV-2 strains were labeled "variants of concern" by the World Health Organization: the Alpha, Beta, Gamma, Delta, and Omicron variants. This review summarizes the potential mechanisms by which the common mutations on the spike protein that occur within these strains enhance the overall fitness of their respective variants. In addressing these mutations within the context of the SARS-CoV-2 spike protein structure, spike/receptor binding interface, spike/antibody binding, and virus neutralization, we summarize the general paradigms that can be used to estimate the effects of future mutations along SARS-CoV-2 evolution.

Identifying Barriers to and Opportunities for Telehealth Implementation Amidst the COVID-19 Pandemic by Using a Human Factors Approach: A Leap Into the Future of Health Care Delivery?

The extensive uptake of telehealth has considerably transformed health care delivery since the beginning of the COVID-19 pandemic and has imposed tremendous challenges to its large-scale implementation and adaptation. Given the shift in paradigm from telehealth as an alternative mechanism of care delivery to telehealth as an integral part of the health system, it is imperative to take a systematic approach to identifying barriers to, opportunities for, and the overall impact of telehealth implementation amidst the current pandemic. In this work, we apply a human factors framework, the Systems Engineering Initiative for Patient Safety model, to guide our holistic analysis and discussion of telehealth implementation, encompassing the health care work system, care processes, and outcomes.

Clinical, laboratory and imaging predictors for critical illness and mortality in patients with COVID-19: protocol for a systematic review and meta-analysis.

INTRODUCTION: With the threat of a worldwide pandemic of COVID-19, it is important to identify the prognostic factors for critical conditions among patients with non-critical COVID-19. Prognostic factors and models may assist front-line clinicians in rapid identification of high-risk patients, early management of modifiable factors, appropriate triaging and optimising the use of limited healthcare resources. We aim to systematically assess the clinical, laboratory and imaging predictors as well as prediction models for severe or critical illness and mortality in patients with COVID-19. METHODS AND ANALYSIS: All peer-reviewed and preprint primary articles with a longitudinal design that focused on prognostic factors or models for critical illness and mortality related to COVID-19 will be eligible for inclusion. A systematic search of 11 databases including PubMed, EMBASE, Web of Science, Cochrane Library, CNKI, VIP, Wanfang Data, SinoMed, bioRxiv, Arxiv and MedRxiv will be conducted. Study selection will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Data extraction will be performed using the modified version of the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist and quality will be evaluated using the Newcastle-Ottawa Scale and the Quality In Prognosis Studies tool. The association between prognostic factors and outcomes of interest will be synthesised and a meta-analysis will be conducted with three or more studies reporting a particular factor in a consistent manner. ETHICS AND DISSEMINATION: Ethical approval was not required for this systematic review. We will disseminate our findings through publication in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD 42020178798.

Acute Kidney Injury Can Predict In-Hospital Mortality in Elderly Patients with COVID-19 in the ICU: A Single-Center Study.

OBJECTIVE: Severe or critical patients with coronavirus disease 2019 (COVID-19) are at increased risk for developing acute kidney injury (AKI). However, the rate of AKI in patients of different severities and independent predictive factors associated with AKI are not well understood. PATIENTS AND METHODS: We enrolled 107 severely or critically ill elderly patients with COVID-19 who were admitted to the intensive care unit (ICU) in Wuhan, China. AKI was defined according to the 2012 KDIGO criteria. We explored the association between AKI and in-hospital mortality using logistic regression. A predictive nomogram was formulated to predict the AKI development of patients with COVID-19 based on multivariate logistic regression. RESULTS: A total of 107 elderly patients were enrolled during the study period. The mean age was 70 (64-78) years, and 69 (64.5%) were men. For the 107 patients, the degree of severity of COVID-19 was categorized as 37 patients with the severe type (34.6%) and 70 patients with the critical type (65.4%). Overall, 48 of the 107 patients (44.9%) developed AKI during their hospitalization, while AKI occurred in 7 (18.9%) out of the 37 severe patients and 41 (44.9%) out of the 70 critical patients. Of the AKI patients, 35.4% (17/48) required continuous renal replacement therapy, including 14.3% of AKI patients in severe cases and 39.0% of AKI patients in critical cases. Kaplan-Meier analysis demonstrated that patients with AKI had a significantly higher risk for in-hospital mortality than severely and critically ill patients without AKI. Multivariate logistic regression analysis showed that AKI (OR = 33.74; 95% CI = 3.34-341.29; P = 0.003), septic shock (OR = 15.58; 95% CI = 2.08-116.78; P = 0.008), invasive mechanical ventilation (OR = 18.44; 95% CI = 2.35-144.69; P = 0.006), and oxygenation index (OR = 0.99; 95% CI = 0.98-1.000; P = 0.014) were independent risk factors for in-hospital mortality. A nomogram was established based on the multivariate analysis results. The C-index for the developed AKI model was 0.935 (95% CI, 0.892-0.978); when 10-fold cross validation was used to validate the model, the corrected C-index was 0.825. CONCLUSION: AKI is common among COVID-19 patients admitted to the ICU and is recognized as a marker of disease severity. The proposed nomogram accurately predicted AKI development in ICU patients with COVID-19 based on individual characteristics. Therefore, the strategy for kidney protection against severe or critical pneumonia is appropriate.

Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis.

BACKGROUND: The recent Coronavirus Disease 2019 (COVID-19) pandemic has placed severe stress on healthcare systems worldwide, which is amplified by the critical shortage of COVID-19 tests. METHODS: In this study, we propose to generate a more accurate diagnosis model of COVID-19 based on patient symptoms and routine test results by applying machine learning to reanalyzing COVID-19 data from 151 published studies. We aim to investigate correlations between clinical variables, cluster COVID-19 patients into subtypes, and generate a computational classification model for discriminating between COVID-19 patients and influenza patients based on clinical variables alone. RESULTS: We discovered several novel associations between clinical variables, including correlations between being male and having higher levels of serum lymphocytes and neutrophils. We found that COVID-19 patients could be clustered into subtypes based on serum levels of immune cells, gender, and reported symptoms. Finally, we trained an XGBoost model to achieve a sensitivity of 92.5% and a specificity of 97.9% in discriminating COVID-19 patients from influenza patients. CONCLUSIONS: We demonstrated that computational methods trained on large clinical datasets could yield ever more accurate COVID-19 diagnostic models to mitigate the impact of lack of testing. We also presented previously unknown COVID-19 clinical variable correlations and clinical subgroups.