PubMed COVID-19 Clinical Care

CONCLUSIONS: These evidence-based recommendations are intended to reduce the use of antibiotics to treat colds and acute bronchitis, for which they are not indicated. Further clinical trials of symptomatic treatments for cough should be performed in

CONCLUSIONS: Nursing students faced difficulties in their first clinical practice during the COVID-19 lockdown, which made a challenging study situation. Too much autonomy and responsibility for their own learning reinforced a loss of experience. The

CONCLUSION: The proposed model uses three classifiers and shows promising results by selecting the important features and maximizing the prediction accuracy.

CONCLUSIONS: There is an urgent need to look at therapeutic strategies and factors which enhance resilience and promote better coping in this population.

The success of the supervised learning process for feedforward neural networks, especially multilayer perceptron neural network (MLP), depends on the suitable configuration of its controlling parameters (i.e., weights and biases). Normally, the

CONCLUSIONS: This assessment further confirmed the NCDK role in maintaining continuity of care on a short-term period. However, its effectiveness was dependent on the health system supply chain in place and the capacity of facilities to manage and

CONCLUSIONS: Most patients with COVID-19 supported on ECMO in both waves survived in the short and longer term. At follow-up patients had similar lung function and QOL across the two waves. This suggests that ECMO has an ongoing role in the

The COVID-19 pandemic has posed the greatest threat to global health over the last three years. Due to the temporary shortage of appropriate vaccines, a systematic change in human behaviour is necessary to keep the spread of the virus under control

Automatic facial expression recognition (AFER), sometimes referred to as emotional recognition, is important for socializing. Automatic methods in the past two years faced challenges due to Covid-19 and the vital wearing of a mask. Machine learning

CONCLUSIONS: These findings suggest that LTL can be a useful parameter for predicting disease severity in patients, as individuals with short telomeres may have a higher risk of developing severe COVID-19.