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Natural Killer (NK) cells are lymphocytes of the innate immunity that play a crucial role in the control of viral infections in the absence of a prior antigen sensitization. Indeed, they display rapid effector functions against target cells with the capability of direct cell killing and antibody-dependent cell-mediated cytotoxicity. Furthermore, NK cells are endowed with immune-modulatory functions innate and adaptive immune responses via the secretion of chemokines/cytokines and by undertaking synergic crosstalks with other innate immune cells, including monocyte/macrophages, dendritic cells and neutrophils. Recently, the Coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread globally. Although the specific role of NK cells in COVID-19 pathophysiology still need to be explored, mounting evidence indicates that NK cell tissue distribution and effector functions could be affected by SARS-CoV-2 infection and that a prompt NK cell response could determine a good clinical outcome in COVID-19 patients. In this review, we give a comprehensive overview of how SARS-CoV-2 infection interferes with NK cell antiviral effectiveness and their crosstalk with other innate immune cells. We also provide a detailed characterization of the specific NK cell subsets in relation to COVID-19 patient severity generated from publicly available single cell RNA sequencing datasets. Finally, we summarize the possible NK cell-based therapeutic approaches against SARS-CoV-2 infection and the ongoing clinical trials updated at the time of submission of this review. We will also discuss how a deep understanding of NK cell responses could open new possibilities for the treatment and prevention of SARS-CoV-2 infection.

We described features of hospitalized Covid-19 patients and identified predictors of clinical deterioration. We included patients consecutively admitted at Humanitas Research Hospital (Rozzano, Milan, Italy); retrospectively extracted demographic; clinical; laboratory and imaging findings at admission; used survival methods to identify factors associated with clinical deterioration (defined as intensive care unit (ICU) transfer or death), and developed a prognostic index. Overall; we analyzed 239 patients (29.3% females) with a mean age of 63.9 (standard deviation [SD]; 14.0) years. Clinical deterioration occurred in 70 patients (29.3%), including 41 (17.2%) ICU transfers and 36 (15.1%) deaths. The most common symptoms and signs at admission were cough (77.8%) and elevated respiratory rate (34.1%), while 66.5% of patients had at least one coexisting medical condition. Imaging frequently revealed ground-glass opacity (68.9%) and consolidation (23.8%). Age; increased respiratory rate; abnormal blood gas parameters and imaging findings; coexisting coronary heart disease; leukocytosis; lymphocytopenia; and several laboratory parameters (elevated procalcitonin; interleukin-6; serum ferritin; C-reactive protein; aspartate aminotransferase; lactate dehydrogenase; creatinine; fibrinogen; troponin-I; and D-dimer) were significant predictors of clinical deterioration. We suggested a prognostic index to assist risk-stratification (C-statistic; 0.845; 95% CI; 0.802–0.887). These results could aid early identification and management of patients at risk, who should therefore receive additional monitoring and aggressive supportive care.

Predicting clinical deterioration in COVID-19 patients remains a challenging task in the Emergency Department (ED). To address this aim, we developed an artificial neural network using textual (e.g. patient history) and tabular (e.g. laboratory values) data from ED electronic medical reports. The predicted outcomes were 30-day mortality and ICU admission. We included consecutive patients from Humanitas Research Hospital and San Raffaele Hospital in the Milan area between February 20 and May 5, 2020. We included 1296 COVID-19 patients. Textual predictors consisted of patient history, physical exam, and radiological reports. Tabular predictors included age, creatinine, C-reactive protein, hemoglobin, and platelet count. TensorFlow tabular-textual model performance indices were compared to those of models implementing only tabular data. For 30-day mortality, the combined model yielded slightly better performances than the tabular fastai and XGBoost models, with AUC 0.87 ± 0.02, F1 score 0.62 ± 0.10 and an MCC 0.52 ± 0.04 ( p  < 0.32). As for ICU admission, the combined model MCC was superior ( p  < 0.024) to the tabular models. Our results suggest that a combined textual and tabular model can effectively predict COVID-19 prognosis which may assist ED physicians in their decision-making process.

Background The clinical diagnosis, severity assessment, and outcome prognostication of community-acquired pneumonia (CAP) remain challenging due to the complex disease pathophysiology. Accurate outcome prediction is crucial for optimizing patient management, reducing mortality, and minimizing hospital and ICU admissions. Methods In this prospective observational cohort study, 228 CAP patients with varying degrees of disease severity were assessed. Clinical and demographic data, along with multiple biomarker measurements, including pentraxin-3 (PTX3), were analysed longitudinally. The primary outcome was clinical failure. Results Among the single parameters evaluated, the oxygen saturation to fraction of inspired oxygen ratio (SpO 2 /FiO 2 ), PTX3, and mid-regional pro-adrenomedullin (MRproADM) demonstrated the strongest predictive performance, with areas under the curve (AUC) of 0.799, 0.709, and 0.647, respectively. Machine learning (ML) experiments integrating multiple features identified the optimal algorithm for outcome prediction, combining these stand-alone markers at baseline and 72 h. The optimal ML model achieved an AUC of 0.950 (95% CI 0.83–0.96), recall of 92.6%, accuracy of 92.0%, and precision of 86.6%, representing a > 15% AUC improvement over any individual biomarker. Conclusions While SpO 2 /FiO 2 remains the most reliable stand-alone prognostic marker, PTX3 demonstrated significant independent outcome predictive value. When integrated with other biomarkers using ML-based models, outcome prediction significantly improved, underscoring its potential for CAP patient management. Trial registration n NCT06491004 (ClinicalTrials.gov).

COVID-19 vaccines have revolutionized prevention and clinical management by reducing disease severity and mortality. However, their long-term impact on hospitalization is unclear. This retrospective study assessed whether vaccination status, timing, and number of vaccine doses influence the risk of hospitalization and COVID-19 pneumonia in a large cohort in Italy, several years after initial vaccine rollout. From 1 October 2023, to 2 February 2024, at Humanitas Research Hospital (Milan) and two affiliates, we recorded age, sex, comorbidities, vaccination status (number of doses and time since last dose), admission type (urgent vs. elective), and pneumonia diagnosis. Baseline health was quantified by the Charlson Comorbidity Index. Among 16,034 admissions (14,874 patients), vaccination data were available for 5743 cases: 40.8% were in the emergency setting and 59.2% were elective. Patients presented with pneumonia in 6.8% of cases. Laboratory results confirmed COVID-19 pneumonia occurred in 43.7% of pneumonia cases, with a 16.9% mortality. Patients with no vaccine dose had a higher proportion of COVID-19 pneumonia, while COVID-19 pneumonia rates were lower in individuals who had received more vaccine doses. There were no significant differences in COVID-19 pneumonia risk by timing of last vaccination. Moreover, hospitalized unvaccinated patients had overall more frequent emergency admissions (57.3%), while patients with three or more doses had about a ~40% emergency admission rate. COVID-19 positivity during hospitalization was highest in unvaccinated patients (90.7%) and declined with vaccination status. Vaccinated patients, especially those with multiple doses, had significantly lower COVID-19 pneumonia rates and emergency admissions. These findings suggest a possible protective effect of vaccination in modifying the clinical presentation and severity of illness among those who are hospitalized and support continued vaccination efforts for high-risk groups to reduce severe adverse outcomes.

BackgroundEarly prognostic stratification of patients with sepsis is a difficult clinical challenge. Aim of this study was to evaluate novel molecules in association with clinical parameters as predictors of 90-days mortality in patients admitted with sepsis at Humanitas Research Hospital.MethodsPlasma samples were collected from 178 patients, diagnosed based on Sepsis-3 criteria, at admission to the Emergency Department and after 5 days of hospitalization. Levels of pentraxin 3 (PTX3), soluble IL-1 type 2 receptor (sIL-1R2), and of a panel of pro- and anti-inflammatory cytokines were measured by ELISA. Cox proportional-hazard models were used to evaluate predictors of 90-days mortality.ResultsCirculating levels of PTX3, sIL-1R2, IL-1β, IL-6, IL-8, IL-10, IL-18, IL-1ra, TNF-α increased significantly in sepsis patients on admission, with the highest levels measured in shock patients, and correlated with SOFA score (PTX3: r=0.44, p<0.0001; sIL-1R2: r=0.35, p<0.0001), as well as with 90-days mortality. After 5 days of hospitalization, PTX3 and cytokines, but not sIL-1R2 levels, decreased significantly, in parallel with a general improvement of clinical parameters. The combination of age, blood urea nitrogen, PTX3, IL-6 and IL-18, defined a prognostic index predicting 90-days mortality in Sepsis-3 patients and showing better apparent discrimination capacity than the SOFA score (AUC=0.863, 95% CI: 0.780−0.945 vs. AUC=0.727, 95% CI: 0.613-0.840; p=0.021 respectively).ConclusionThese data suggest that a prognostic index based on selected cytokines, PTX3 and clinical parameters, and hence easily adoptable in clinical practice, performs in predicting 90-days mortality better than SOFA. An independent validation is required.

Emerging evidence shows that individuals with COVID-19 who survive the acute phase of illness may experience lingering symptoms in the following months. There is no clear indication as to whether these symptoms persist for a short time before resolving or if they persist for a long time. In this review, we will describe the symptoms that persist over time and possible predictors in the acute phase that indicate long-term persistence. Based on the literature available to date, fatigue/weakness, dyspnea, arthromyalgia, depression, anxiety, memory loss, slowing down, difficulty concentrating and insomnia are the most commonly reported persistent long-term symptoms. The extent and persistence of these in long-term follow-up is not clear as there are still no quality studies available. The evidence available today indicates that female subjects and those with a more severe initial disease are more likely to suffer permanent sequelae one year after the acute phase. To understand these complications, and to experiment with interventions and treatments for those at greater risk, we must first understand the physio-pathological mechanisms that sustain them.

Interleukin-1 (IL-1)/IL-1 receptor family consists of activators and inhibitors which play a key role in inflammation, emergency myelopoiesis, and myeloid cell activation. The latter includes the IL-1R2 decoy receptor. To investigate the expression and significance of IL-1R2 in sepsis, we conducted high-dimensional flow cytometry of circulating cells from patients stratified according to the Sequential Sepsis-Related Organ Failure Assessment (SOFA) score. Here we report that the IL-1 decoy receptor is selectively upregulated on the plasma membrane of leukocytes and, in particular, monocytes from septic patients, and downregulated in septic shock. Flow cytometry combined with transcriptomic analysis of publicly available datasets indicated that IL-1R2 is associated with the differentiation of monocytes to a population of circulating monocytic cells with macrophage features (Mono/M φ ). In vitro stimulation of monocytes from healthy donors with Colony Stimulating Factors (CSFs), in particular GM-CSF and Lipopolysaccharides (LPS), induced IL-1R2 + Mono/M φ , which recapitulated the characteristics of sepsis-associated monocytic cells, including low expression of HLA-DR, high levels of macrophage markers such as MS4A4A and CD63, immune checkpoints, immunosuppressive molecules and selected scavenger receptors. Membrane-associated IL-1R2 and MS4A4A correlated with immunological markers, cytokine storm, and clinical parameters (e.g., SOFA score, creatinine, survival), reflecting the infection severity in hospitalized patients. Thus, in sepsis IL-1R2 is expressed in a subset of circulating monocytes co-expressing mature macrophage and immune dysfunction features with clinical significance.

Ursodeoxycholic acid (UDCA) was demonstrated to reduce susceptibility to SARS-CoV-2 infection in vitro and improve infection course in chronic liver diseases. However, real-life evidence is lacking. We analyzed the impact of UDCA on COVID-19 outcomes in patients hospitalized in a tertiary center. Between January 2020 and January 2023, among 3847 patients consecutively hospitalized for COVID19, 57 (=UDCA group) were taking UDCA. The UDCA and the control groups (n = 3790) did not differ concerning comorbidities including diabetes mellitus type 2 (15.8% vs. 12.8%) and neoplasia (12.3% vs. 9.4%). Liver diseases and vaccination rate were more common in the UDCA group (14.0% vs. 2.5% and 54.4% vs. 30.2%, respectively). Overall mortality and CPAP treatment were 22.8 % and 15.7% in the UDCA, and 21.3% and 25.9% in the control group. Mortality was similar (p = 0.243), whereas UDCA was associated with a lower rate of CPAP treatment (OR = 0.76, p < 0.05). Treatment with UDCA was not an independent predictor of survival in patients hospitalized for COVID-19.