Vaccination against an airborne pathogen is very effective if it induces also the development of mucosal antibodies that can protect against infection. The mRNA‐based vaccine‐encoding SARS‐CoV‐2 full‐length spike protein (BNT162b2, Pfizer/BioNTech) protects also against infection despite being administered systemically. Here, we show that upon vaccination, cognate IgG molecules are also found in the saliva and are more abundant in SARS‐CoV‐2 previously exposed subjects, paralleling the development of plasma IgG. The antibodies titer declines at 3 months from vaccination. We identified a concentration of specific IgG in the plasma above which the relevant IgG can be detected in the saliva. Regarding IgA antibodies, we found only protease‐susceptible IgA1 antibodies in plasma while they were present at very low levels in the saliva over the course of vaccination of SARS‐CoV‐2‐naïve subjects. Thus, in response to BNT162b2 vaccine, plasma IgG can permeate into mucosal sites and participate in viral protection. It is not clear why IgA1 are detected in low amount, they may be proteolytically cleaved.

Community-acquired pneumonia (CAP) is accountable for high mortality in both pediatric and adult populations worldwide, about one-third of hospitalized patients pass away within a year of being discharged from the facility. The high mortality and morbidity rates are closely related to cardiovascular complications that are consequent or concomitant to the acute episode of pneumonia. An updated perspective on the major pathophysiological mechanisms, prevalence, risk factors, outcomes, and relevant treatments of cardiovascular events in CAP patients is provided in the current study. It is possible to evaluate the pathophysiology of cardiac disease in this population based on plaque-related events, such as acute myocardial infarction, or events unrelated to plaque, such as arrhythmias and heart failure. With an absolute rate of cardiovascular problems ranging broadly from 10% to 30%, CAP raises the risk of both plaque-related and plaque-unrelated events. Both in- and out-patients may experience these issues at admission, throughout hospitalization, or even up to a year following discharge. At long-term follow-up, cardiac events account for more than 30% of deaths in CAP patients, making them a significant cause of mortality. If patients at risk for cardiac events are stratified, diagnostic tools, monitoring, and preventive measures may be applied to these patients. A prospective evaluation of cardioprotective treatments is urgently required from a research point of view.

Background: Sepsis is one of the major causes of in-hospital death, and is frequent in patients presenting to the emergency department (ED). Early identification of high-risk septic patients is critical. Machine learning (ML) techniques have been proposed for identification and prognostication of ED septic patients, but these models often lack pre-hospital data and lack validation against early sepsis identification scores (such as qSOFA) and scores for critically ill patients (SOFA, APACHE II). Methods We conducted an electronic health record (EHR) study to test whether interpretable and scalable ML models predict mortality in septic ED patients and compared their performance with clinical scores. Consecutive adult septic patients admitted to ED over 18 months were included. We built ML models, ranging from a simple-classifier model, to unbalanced and balanced logistic regression, and random forest, and compared their performance to qSOFA, SOFA, and APACHE II scores. Results: We included 425 sepsis patients after screening 38,500 EHR for sepsis criteria. Overall mortality was 15.2% and peaked in patients coming from retirement homes (38%). Random forest, like balanced (0.811) and unbalanced logistic regression (0.863), identified patients at risk of mortality (0.813). All ML models outperformed qSOFA, APACHE II, and SOFA scores. Age, mean arterial pressure, and serum sodium were major mortality predictors. Conclusions: We confirmed that random forest models outperform previous models, including qSOFA, SOFA, and APACHE II, in identifying septic patients at higher mortality risk, while maintaining good interpretability. Machine learning models may gain further adoption in the future with increasing diffusion and granularity of EHR data, yielding the advantage of increased scalability compared to standard statistical techniques.

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.