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The novel coronavirus (COVID-19) pandemic still has a significant impact on the worldwide population's health and well-being. Effective patient screening, including radiological examination employing chest radiography as one of the main screening modalities, is an important step in the battle against the disease. Indeed, the earliest studies on COVID-19 found that patients infected with COVID-19 present with characteristic anomalies in chest radiography. In this paper, we introduce COVID-ConvNet, a deep convolutional neural network (DCNN) design suitable for detecting COVID-19 symptoms from chest X-ray (CXR) scans. The proposed deep learning (DL) model was trained and evaluated using 21,165 CXR images from the COVID-19 Database, a publicly available dataset. The experimental results demonstrate that our COVID-ConvNet model has a high prediction accuracy at 97.43% and outperforms recent related works by up to 5.9% in terms of prediction accuracy. [ABSTRACT FROM AUTHOR]

Maskne, a newly coined term deriving from the contraction of mask-related acne, is a form of mechanical acne resulting from continuous textile–skin adherence and friction. Prolonged mask use heats up the face environment, thus modifying skin microbiota and sebum production. Although effective prevalence is unknown, since the pandemic began and the prolonged use of masks was extended to the general population, maskne has been a frequent topic of consultation among dermatologists. This term has been successfully introduced into common language, with more than 200,000 hashtags on social media, where it is also possible to find "home remedies" that can worsen this dermatosis. The aim of this paper is to discuss the pathogenesis, address clinicians on the correct differential diagnoses among facial dermatoses, and move towards the correct therapy. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) can directly or indirectly affect the central and peripheral nervous systems, resulting in cognitive impairment, memory problems, and a wide range of neuromuscular involvement, including neuropathies. However, the long-term neurological complications of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection are not clear. The aim this study was to analyze a case report the presence of neurological sequelae due to post-Coronavirus disease 19 in a patient without apparent previous neurological symptoms. Clinical case: A 46-year-old patient, with no relevant history for the described condition, who, after severe COVID-19 infection, started a mixed neuropathy and mental fog syndrome as the main sequel. Multiple laboratory and imaging studies were performed during and after his hospital stay, and it was corroborated by an electromyography that it occurred from a neuropathy triggered by COVID-19 infection. Conclusions: This case provides additional evidence that mixed neuropathy and brain fog syndrome are potential complications of post-coronavirus disease 2019 syndrome. The neurological sequelae that manifest after a COVID-19 episode can be rapidly enhanced as a consequence of another alteration in some systems of the organism. However, future studies are necessary to elucidate the incidence of these neurological complications, their pathophysiological mechanisms and their therapeutic options. [ABSTRACT FROM AUTHOR]

Molecular tests are the gold standard to diagnose severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection but are associated with a diagnostic delay, while antigen detection tests can generate results within 20 min even outside a laboratory. In order to evaluate the accuracy and reliability of the FAST COVID-19 SARS-CoV-2 Antigen Rapid Test Kit (Ag-RDT), two respiratory swabs were collected simultaneously from 501 patients, with mild or no coronavirus disease 2019 (COVID-19)-related symptoms, and analyzed with both the Reverse Transcriptase-quantitative Polymerase Chain Reaction (RT-qPCR) and the FAST COVID-19 SARS-CoV-2 Antigen Rapid Test. Results were then compared to determine clinical performance in a screening setting. We measured a precision of 97.41% (95% CI 92.42–99.15%) and a recall of 98.26% (95% CI 93.88–99.25%), with a specificity of 99.22% (95% CI 97.74–99.74%), a negative predictive value of 99.48% (95% CI 97.98–99.87%), and an overall accuracy of 99.00% (95% CI 97.69–99.68%). Concordance was described by a Kappa coefficient of 0.971 (95% CI 0.947–0.996). Considering short lead times, low cost, and opportunities for decentralized testing, the Ag-RDT test can enhance the efforts to control SARS-CoV-2 spread in several settings. [ABSTRACT FROM AUTHOR]