Internal and emergency medicine
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Randomized Controlled Trial
A machine learning diagnostic model for Pneumocystis jirovecii pneumonia in patients with severe pneumonia.
The diagnosis of Pneumocystis jirovecii pneumonia (PCP) in patients presenting with severe pneumonia is challenging and delays in treatment were associated with worse prognosis. This study aimed to develop a rapid, easily available, noninvasive machine learning diagnostic model for PCP among patients with severe pneumonia. ⋯ We constructed a PCP diagnostic model in patients with severe pneumonia using four easily available and noninvasive clinical indicators. With satisfying diagnostic performance and good clinical practicability, this model may help clinicians to make early diagnosis of PCP, reduce the delays of treatment and improve the prognosis among these patients.
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During the first two waves of the COVID-19 emergency in Italy, internal medicine high-dependency wards (HDW) have been organized to manage patients with acute respiratory failure (ARF). There is heterogeneous evidence about the feasibility and outcomes of non-invasive respiratory supports (NIRS) in settings outside the intensive care unit (ICU), including in patients deemed not eligible for intubation (i.e., with do-not-intubate, DNI status). Few data are available about the different NIRS modalities applied to ARF patients in the newly assembled internal medicine HDW. ⋯ Multivariate regression models showed older age (odds ratio-OR 4.74), chronic ischemic heart disease (OR 2.76), high respiratory rate after 24 h (OR 7.13), and suspected acute respiratory distress syndrome-ARDS (OR 21.1) as predictors of mortality risk or ETI. Our real-life experience shows that NIRS was feasible in internal medicine HDW with an acceptable success rate. Although DNI patients had a worse prognosis, the use of NIRS represented a reasonable chance of treatment.
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The intestinal mucosa represents the most extensive human barrier having a defense function against microbial and food antigens. This barrier is represented externally by a mucus layer, consisting mainly of mucins, antimicrobial peptides, and secretory immunoglobulin A (sIgA), which serves as the first interaction with the intestinal microbiota. Below is placed the epithelial monolayer, comprising enterocytes and specialized cells, such as goblet cells, Paneth cells, enterochromaffin cells, and others, each with a specific protective, endocrine, or immune function. ⋯ Conversely, the impairment of the mucosal barrier function, the alteration of the normal luminal microbiota composition (dysbiosis), or the imbalance between pro- and anti-inflammatory mucosal factors may result in inflammation and disease. Another crucial component of the intestinal barrier is the gut-vascular barrier, formed by endothelial cells, pericytes, and glial cells, which regulates the passage of molecules into the bloodstream. The aim of this review is to examine the various components of the intestinal barrier, assessing their interaction with the mucosal immune system, and focus on the immunological processes underlying homeostasis or inflammation.
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Pain remains one of the most difficult-to-treat domains in patients with rheumatoid arthritis (RA). In clinical trials, the Janus kinase inhibitors (JAKis) have demonstrated good efficacy in pain relief. Aim of our study was to evaluate the real-life effectiveness of JAKis in improving pain in patients with RA in different states of baseline disease activity. ⋯ Pauci-inflammatory patients at treatment start achieved good outcomes, with 40.4% experiencing ≥ 70% pain improvement, and 35.7% VAS ≤ 10 mm. JAKis show efficacy in pain relief in real life. The improvement of painful symptoms also in those patients with limited objective inflammation may open new perspectives on the management of difficult-to-treat RA.
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This study aimed to evaluate the effectiveness of various scoring systems in predicting in-hospital mortality for COVID-19 patients admitted to the internal medicine ward. We conducted a prospective collection of clinical data from patients admitted to the Internal Medicine Unit at Santa Maria Nuova Hospital in Florence, Italy, with confirmed pneumonia caused by SARS-CoV-2. We calculated three scoring systems: the CALL score, the PREDI-CO score, and the COVID-19 in-hospital Mortality Risk Score (COVID-19 MRS). ⋯ The mortality rate increased significantly across increasing quartiles (p<0.001). In conclusion the COVID-19 in-hospital Mortality Risk Score (MRS) demonstrated reasonable prognostic stratification for patients admitted to the internal medicine ward with SARS-CoV-2-induced pneumonia. The inclusion of Delirium and IL6 as additional prognostic indicators in the scoring systems enhanced their predictive performance, specifically in determining in-hospital mortality among COVID-19 patients.