Internal and emergency medicine
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Multicenter Study
Relapses of idiopathic inflammatory myopathies after vaccination against COVID-19: a real-life multicenter Italian study.
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) vaccination plays a crucial role as pivotal strategy to curb the coronavirus disease-19 (COVID-19) pandemic. The present study described the clinical status of patients affected by idiopathic inflammatory myopathies (IIM) after COVID-19 vaccination to assess the number of relapses. We included all patients affected by IIM and followed by Myositis Clinic, Rheumatology and Respiratory Diseases Units, Siena University Hospital, Bari University Hospital, Policlinico Umberto I, Sapienza University, Rome, and Policlinico Paolo Giaccone, Palermo. ⋯ No patients had flares after third dose. Our study represents the largest cohort of IIM patients in which the incidence of recurrence after anti-SARS-CoV-2 vaccine was assessed. In line with real-life data from other diseases, we found a clinical non-statistically significant risk of relapse in our patients, which occurred seldom, usually mild and in patients with a more severe and aggressive course of disease.
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Potential secondhand exposure of exhaled constituents from e-vapor product (EVP) use is a public health concern. We present a computational modeling method to predict air levels of exhaled constituents from EVP use. We measured select constituent levels in exhaled breath from adult e-vapor product users, then used a validated computational model to predict constituent levels under three scenarios (car, office, and restaurant) to estimate likely secondhand exposure to non-users. ⋯ Acetaldehyde and acrolein were below detectable limits; thus, no estimated exposure to non-EVP users is reported. The model predicted that nicotine and formaldehyde exposure to non-users was substantially lower during EVPs use compared to cigarettes. The model also predicted that exposure to propylene glycol, glycerin, nicotine and formaldehyde among non-users was below permissible exposure limits.
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Recently, global health has seen an increase in demand for assistance as a result of the COVID-19 pandemic. This has prompted many researchers to conduct different studies looking for variables that are associated with increased clinical risk, and find effective and safe treatments. Many of these studies have been limited by presenting small samples and a large data set. ⋯ This retrospective study of 150 hospitalized adult COVID-19 patients, of which we established two groups, those who died were called Case group (n = 53) while the survivors were Control group (n = 98). For analysis, a supervised learning algorithm eXtreme Gradient Boosting (XGBoost) has been used due to its good response compared to other methods because it is highly efficient, flexible and portable. In this study, the response to different treatments has been evaluated and has made it possible to accurately predict which patients have higher mortality using artificial intelligence, obtaining better results compared to other ML methods.
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Haemoglobin A1c (HbA1c) is a marker of glycaemic control in type 2 diabetes mellitus (T2DM). Increased waist circumference (WC) is known to be associated with T2DM. Therefore, we investigated the relationship of WC with HbA1c and explored its optimal cutoff for identifying prediabetes and diabetes risk. ⋯ The optimal cutoff values of WC indicating an HbA1c level of 5.7% and 6.5% was 83 cm (entropy = 0.943) and 85 cm (entropy = 0.365) in men, and 78 cm (entropy = 0.922) and 86 cm (entropy = 0.256) in women. The linear relationship between WC and HbA1c in this study suggests that addressing central obesity issue is beneficial to people with T2DM or at risk of T2DM. WC cutoff values of 85 cm for men and 86 cm for women are appropriate for recommendation to undergo diabetes screening.