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Wellbeing engineering with regard to drops reduction and recognition inside mature medical center in-patients: a scoping evaluation.
Gender imbalances in academia have been evident historically and persist today. For the past 60 years, we have witnessed the increase of participation of women in biomedical disciplines, showing that the gender gap is shrinking. However, preliminary evidence suggests that women, including female researchers, are disproportionately affected by the COVID-19 pandemic in terms of unequal distribution of childcare, elderly care, and other kinds of domestic and emotional labor. Sudden lockdowns and abrupt shifts in daily routines have had disproportionate consequences on their productivity, which is reflected by a sudden drop in research output in biomedical research, consequently affecting the number of female authors of scientific publications.

The objective of this study is to test the hypothesis that the COVID-19 pandemic has had a disproportionate adverse effect on the productivity of female researchers in the biomedical field in terms of authorship of scientific publications.

This is a retrospective obs in the gender gap was persistent across the 10 countries with the highest number of researchers. These results should be used to inform the scientific community of this worrying trend in COVID-19 research and the disproportionate effect that the pandemic has had on female academics.
Our findings document a decrease in the number of publications by female authors in the biomedical field during the global pandemic. This effect was particularly pronounced for papers related to COVID-19, indicating that women are producing fewer publications related to COVID-19 research. This sudden increase in the gender gap was persistent across the 10 countries with the highest number of researchers. These results should be used to inform the scientific community of this worrying trend in COVID-19 research and the disproportionate effect that the pandemic has had on female academics.Digital technologies have been transforming methods of health care delivery and have been embraced within the health, social, and public response to the COVID-19 pandemic. However, this has directed attention to the "inverse information law" (also called "digital inverse care law") and digital inequalities, as people who are most in need of support (in particular, older people and those experiencing social deprivation) are often least likely to engage with digital platforms. The response to the COVID-19 pandemic represents a sustained shift to the adoption of digital approaches to working and engaging with populations, which will continue beyond the COVID-19 pandemic. Therefore, it is important to understand the underlying factors contributing to digital inequalities and act immediately to avoid digital inequality contributing to health inequalities in the future. The response to COVID-19 represents a sustained shift to adopting digital approaches to working and engaging with populations which will continue beyond this pandemic. Resveratrol manufacturer Therefore it is important that we understand the underlying factors contributing to digital inequalities, and act now to protect against digital inequality contributing to health inequalities in the future.
Accurate and timely diagnosis and effective prognosis of the disease is important to provide the best possible care for patients with COVID-19 and reduce the burden on the health care system. Machine learning methods can play a vital role in the diagnosis of COVID-19 by processing chest x-ray images.

The aim of this study is to summarize information on the use of intelligent models for the diagnosis and prognosis of COVID-19 to help with early and timely diagnosis, minimize prolonged diagnosis, and improve overall health care.

A systematic search of databases, including PubMed, Web of Science, IEEE, ProQuest, Scopus, bioRxiv, and medRxiv, was performed for COVID-19-related studies published up to May 24, 2020. This study was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. All original research articles describing the application of image processing for the prediction and diagnosis of COVID-19 were considered in the analysis. Two reactice for the diagnosis and prognosis of COVID-19.
The COVID-19 pandemic has resulted in social isolation, which has a potential negative impact on the educational routines (eg, the suspension of face-to-face appointments) and mental health of medical students. The Mario Pinotti II (MPII) study is a 24-week observational study that conducted scheduled telephone calls every 2 weeks to verify the occurrence of COVID-19 in patients with rheumatic diseases on chronic hydroxychloroquine therapy (from March 29, 2020, to September 30, 2020). The effects of voluntarily participating in a research project (ie, one that involves interactions via telephone contact with patients, professors, rheumatologists, and colleagues) on the daily lives and mental health of medical students requires evaluation.

As medical students are professionals in training and have a high level of responsibility in terms of handling the emotional and physical aspects of several diseases, this study aims to evaluate the impacts of the COVID-19 pandemic and participation in the MPII study on he Federal University of São Paulo Ethics Committee (CAAE 34034620.0.0000.5505).

Data were collected from both medical student groups from July 20 to August 31, 2020. Data extraction was completed in September 2020. The data analysis is ongoing. We expect the results to be published in the first semester of 2021.

This study will provide insight into the effects of participating in a research project on depression, anxiety, and stress, which will be determined by applying the DASS-21 to a large sample of Brazilian undergraduate medical students. We will also evaluate the impact of the COVID-19 pandemic on medical students' educational routines and medical training.

DERR1-10.2196/24617.
DERR1-10.2196/24617.[This corrects the article DOI 10.2196/24165.].This article is devoted to designing distributed adaptive attack-free protocols for the consensus of linear multi-input multioutput multiagent systems under directed graphs, where the appointed-time reduced-order observers are proposed based only upon the relative output information among neighboring agents. One of the distinguishing features of the attack-free protocols lies in the prohibition on information transmission via the communication channel. By viewing the relative control input as the unknown input on the dynamics of each agent, a class of new unknown input observers is introduced with only the relative output measurement involved. The appointed-time estimation of the consensus error is achieved by utilizing jump discontinuity in the observer design and employing the property of the nilpotent matrix. Moreover, a linear transformation is made on the system of consensus error to realize the observer order reduction. Both theoretical analysis and simulation illustration are presented to reveal the effectiveness of the proposed attack-free protocols.
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