Currently, few evidences have shown the possible involvement of autoimmunity in patients affected by coronavirus disease 2019 (COVID‐19). In this study, we elucidate whether severe acute respiratory syndrome coronavirus disease 2 (SARS‐CoV‐2) stimulates autoantibody production and contributes to autoimmunity activation. We enrolled 40 adult patients (66.8 years mean age) admitted to Alessandria Hospital between March and April 2020. All the patients had a confirmed COVID‐19 diagnosis and no previously clinical record of autoimmune disease. Forty blood donors were analyzed for the same markers and considered as healthy controls. Our patients had high levels of common inflammatory markers, such as C reactive protein, lactate dehydrogenase, ferritin, and creatinine. Interleukin‐6 concentrations were also increased, supporting the major role of this interleukin during COVID‐19 infection. Lymphocyte numbers were generally lower compared with healthy individuals. All the patients were also screened for the most common autoantibodies. We found a significant prevalence of antinuclear antibodies, antineutrophil cytoplasmic antibodies, and ASCA immunoglobulin A antibodies. We observed that patients having a de novo autoimmune response had the worst acute viral disease prognosis and outcome. Our results sustain the hypothesis that COVID‐19 infection correlates with the autoimmunity markers. Our study might help clinicians to: (a) better understand the heterogeneity of this pathology and (b) correctly evaluate COVID‐19 clinical manifestations. Our data explained why drugs used to treat autoimmune diseases may also be useful for SARS‐CoV‐2 infection. In addition, we highly recommend checking patients with COVID‐19 for autoimmunity markers, mainly when deciding on whether to treat them with plasma transfer therapy. Study Highlights: WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC? ☑ Recent data sustain the idea that autoimmune phenomena exist in patients with coronavirus disease 2019 (COVID‐19), but other investigations are necessary to define the possible link between severe acute respiratory syndrome coronavirus disease 2 (SARS‐CoV‐2) infection and autoimmune disease onset. WHAT QUESTION DID THIS STUDY ADDRESS? ☑ In this monocentric study, we demonstrated how SARS‐CoV‐2 infection could be associated with an autoimmune response and development of autoantibodies. WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE? ☑ Patients with COVID‐19 having an increased level of inflammatory markers and strong autoantibodies positivity (i.e., antinuclear antibodies and antineutrophil cytoplasmic antibodies) presented the worst clinical outcome. HOW MIGHT THIS CHANGE CLINICAL PHARMACOLOGY OR TRANSLATIONAL SCIENCE? ☑ These results suggest that the drugs normally used to treat autoimmune diseases should also be considered during SARS‐CoV‐2, improving public health. In addition, before starting a transfer plasma therapy, it is important to also evaluate the autoimmunity conditions of the patients with COVID‐19. Transferring antibodies or trying to neutralize them should be done with precaution. It is possible that the risk of developing or increasing the autoimmune response may enhance.

Clinical features and natural history of coronavirus disease 2019 (COVID-19) differ widely among different countries and during different phases of the pandemia. Here, we aimed to evaluate the case fatality rate (CFR) and to identify predictors of mortality in a cohort of COVID-19 patients admitted to three hospitals of Northern Italy between March 1 and April 28, 2020. All these patients had a confirmed diagnosis of SARS-CoV-2 infection by molecular methods. During the study period 504/1697 patients died; thus, overall CFR was 29.7%. We looked for predictors of mortality in a subgroup of 486 patients (239 males, 59%; median age 71 years) for whom sufficient clinical data were available at data cut-off. Among the demographic and clinical variables considered, age, a diagnosis of cancer, obesity and current smoking independently predicted mortality. When laboratory data were added to the model in a further subgroup of patients, age, the diagnosis of cancer, and the baseline PaO₂/FiO₂ ratio were identified as independent predictors of mortality. In conclusion, the CFR of hospitalized patients in Northern Italy during the ascending phase of the COVID-19 pandemic approached 30%. The identification of mortality predictors might contribute to better stratification of individual patient risk.

Lupus Nephritis (LN) still represents one of the most severe complications of Systemic Lupus Erythematosus (SLE) and a major risk factor for morbidity and mortality. However, over the last few years, several studies have paved the way for a deeper understanding of its pathogenetic mechanisms and more targeted treatments. This review aims to provide a comprehensive update on progress on several key aspects in this setting: pathogenetic mechanisms of LN, including new insight into the role of autoantibodies, complement, vitamin D deficiency, and interaction between infiltrating immune cells and kidney resident ones; the evolving role of renal biopsy and biomarkers, which may integrate information from renal histology; newly approved drugs such as voclosporin (VOC) and belimumab (BEL), allowing a more articulate strategy for induction therapy, and other promising phase III-immunosuppressive (IS) agents in the pipeline. Several adjunctive treatments aimed at reducing cardiovascular risk and progression of chronic renal damage, such as antiproteinuric agents, represent an important complement to IS therapy. Furthermore, non-pharmacological measures concerning general lifestyle and diet should also be adopted when managing LN. Integrating these therapeutic areas requires an effort towards a holistic and multidisciplinary approach. At the same time, the availability of an increasingly wider armamentarium may translate into improvements in patient’s renal outcomes over the next decades.

Artificial Intelligence (AI) is rapidly transforming medical education by enabling adaptive tutoring, interactive simulation, diagnostic enhancement, and competency-based assessment. This narrative review explores how AI has influenced learning processes in undergraduate and postgraduate medical training, focusing on methodological rigor, educational impact, and implementation challenges. The literature reveals promising results: large language models can generate didactic content and foster academic writing; AI-driven simulations enhance decision-making, procedural skills, and interprofessional communication; and deep learning systems improve diagnostic accuracy in visually intensive tasks such as radiology and histology. Despite promising findings, the existing literature is methodologically heterogeneous. A minority of studies use controlled designs, while the majority focus on short-term effects or are confined to small, simulated cohorts. Critical limitations include algorithmic opacity, generalizability concerns, ethical risks (e.g., GDPR compliance, data bias), and infrastructural barriers, especially in low-resource contexts. Additionally, the unregulated use of AI may undermine critical thinking, foster cognitive outsourcing, and compromise pedagogical depth if not properly supervised. In conclusion, AI holds substantial potential to enhance medical education, but its integration requires methodological robustness, human oversight, and ethical safeguards. Future research should prioritize multicenter validation, longitudinal evaluation, and AI literacy for learners and educators to ensure responsible and sustainable adoption.

Introduction. The clinical course of Coronavirus Disease 2019 (COVID-19) is highly heterogenous, ranging from asymptomatic to fatal forms. The identification of clinical and laboratory predictors of poor prognosis may assist clinicians in monitoring strategies and therapeutic decisions. Materials and Methods. In this study, we retrospectively assessed the prognostic value of a simple tool, the complete blood count, on a cohort of 664 patients ( 260; 39%, median age 70 (56-81) years) hospitalized for COVID-19 in Northern Italy. We collected demographic data along with complete blood cell count; moreover, the outcome of the hospital in-stay was recorded. Results. At data cut-off, 221/664 patients (33.3%) had died and 453/664 (66.7%) had been discharged. Red cell distribution width (RDW) ( 10.4; ), neutrophil-to-lymphocyte (NL) ratio ( 7.6; ), and platelet count ( 5.39; ), along with age ( 87.6; ) and gender ( 17.3; ), accurately predicted in-hospital mortality. Hemoglobin levels were not associated with mortality. We also identified the best cut-off for mortality prediction: a was characterized by an odds ratio for in-hospital (2.40-4.82), while the OR for a