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Background During the first epidemic wave, COVID-19 surveillance focused on quantifying the magnitude and the escalation of a growing global health crisis. The scientific community first assessed risk through basic indicators, such as the number of cases or rates of new cases and deaths, and later began using other direct impact indicators to conduct more detailed analyses. We aimed at synthesizing the scientific community's contribution to assessing the direct impact of the COVID-19 pandemic on population health through indicators reported in research papers. Methods We conducted a rapid scoping review to identify and describe health indicators included in articles published between January 2020 and June 2021, using one strategy to search PubMed, EMBASE and WHO COVID-19 databases. Sixteen experts from European public health institutions screened papers and retrieved indicator characteristics. We also asked in an online survey how the health indicators were added to and used in policy documents in Europe. Results After reviewing 3891 records, we selected a final sample of 67 articles and 233 indicators. We identified 52 (22.3%) morbidity indicators from 33 articles, 105 severity indicators (45.1%, 27 articles) and 68 mortality indicators (29.2%, 51). Respondents from 22 countries completed 31 questionnaires, and the majority reported morbidity indicators (29, 93.5%), followed by mortality indicators (26, 83.9%). Conclusions The indicators collated here might be useful to assess the impact of future pandemics. Therefore, their measurement should be standardized to allow for comparisons between settings, countries and different populations. [ABSTRACT FROM AUTHOR]

Background The National Institutes of Health (NIH) mobilized more than $4 billion in extramural funding for the COVID-19 pandemic. Assessing the research output from this effort is crucial to understanding how the scientific community leveraged federal funding and responded to this public health crisis. Methods NIH-funded COVID-19 grants awarded between January 2020 and December 2021 were identified from NIH Research Portfolio Online Reporting Tools Expenditures and Results using the "COVID-19 Response" filter. PubMed identifications of publications under these grants were collected and the NIH iCite tool was used to determine citation counts and focus (eg, clinical, animal). iCite and the NIH's LitCOVID database were used to identify publications directly related to COVID-19. Publication titles and Medical Subject Heading terms were used as inputs to a machine learning–based model built to identify common topics/themes within the publications. Results and Conclusions We evaluated 2401 grants that resulted in 14 654 publications. The majority of these papers were published in peer-reviewed journals, though 483 were published to preprint servers. In total, 2764 (19%) papers were directly related to COVID-19 and generated 252 029 citations. These papers were mostly clinically focused (62%), followed by cell/molecular (32%), and animal focused (6%). Roughly 60% of preprint publications were cell/molecular-focused, compared with 26% of nonpreprint publications. The machine learning–based model identified the top 3 research topics to be clinical trials and outcomes research (8.5% of papers), coronavirus-related heart and lung damage (7.3%), and COVID-19 transmission/epidemiology (7.2%). This study provides key insights regarding how researchers leveraged federal funding to study the COVID-19 pandemic during its initial phase. [ABSTRACT FROM AUTHOR]

The severe acute respiratory syndrome coronavirus 2 virus has, up to the time of this article, resulted in >770 million cases of COVID-19 illness worldwide, and approximately 7 million deaths, including >1.1 million in the United States. Although defined as a respiratory virus, early in the pandemic, it became apparent that considerable numbers of people recovering from COVID-19 illness experienced persistence or new onset of multi-system health problems, including neurologic and cognitive and behavioral health concerns. Persistent multi-system health problems are defined as Post-COVID-19 Condition (PCC), Post-Acute Sequelae of COVID-19, or Long COVID. A significant number of those with PCC report cognitive problems. This paper reviews the current state of scientific knowledge on persisting cognitive symptoms in adults following COVID-19 illness. A brief history is provided of the emergence of concerns about persisting cognitive problems following COVID-19 illness and the definition of PCC. Methodologic factors that complicate clear understanding of PCC are reviewed. The review then examines research on patterns of cognitive impairment that have been found, factors that may contribute to increased risk, behavioral health variables, and interventions being used to ameliorate persisting symptoms. Finally, recommendations are made about ways neuropsychologists can improve the quality of existing research. [ABSTRACT FROM AUTHOR]

Motivation A patient's disease phenotype can be driven and determined by specific groups of cells whose marker genes are either unknown or can only be detected at late-stage using conventional bulk assays such as RNA-Seq technology. Recent advances in single-cell RNA sequencing (scRNA-seq) enable gene expression profiling in cell-level resolution, and therefore have the potential to identify those cells driving the disease phenotype even while the number of these cells is small. However, most existing methods rely heavily on accurate cell type detection, and the number of available annotated samples is usually too small for training deep learning predictive models. Results Here, we propose the method ScRAT for phenotype prediction using scRNA-seq data. To train ScRAT with a limited number of samples of different phenotypes, such as coronavirus disease (COVID) and non-COVID, ScRAT first applies a mixup module to increase the number of training samples. A multi-head attention mechanism is employed to learn the most informative cells for each phenotype without relying on a given cell type annotation. Using three public COVID datasets, we show that ScRAT outperforms other phenotype prediction methods. The performance edge of ScRAT over its competitors increases as the number of training samples decreases, indicating the efficacy of our sample mixup. Critical cell types detected based on high-attention cells also support novel findings in the original papers and the recent literature. This suggests that ScRAT overcomes the challenge of missing marker genes and limited sample number with great potential revealing novel molecular mechanisms and/or therapies. Availability and implementation The code of our proposed method ScRAT is published at https://github.com/yuzhenmao/ScRAT. [ABSTRACT FROM AUTHOR]