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West Nile virus (WNV) and Usutu virus (USUV) are neurotropic mosquito-borne flaviviruses that may infect humans. Although WNV is much more widespread and plays a much larger role in human health, the two viruses are characterized by similar envelope antigens, clinical manifestations, and present overlapping in terms of geographic range of transmission, host, and vector species. This review highlights some of the most relevant aspects of WNV and USUV human infections in Europe, and the possible implications of their co-circulation.

Abstract Background: The pathogenetic steps leading to Covid-19 interstitial pneumonia remain to be clarified. Most postmortem studies to date reveal diffuse alveolar damage as the most relevant histologic pattern. Antemortem lung biopsy may however provide more precise data regarding the earlier stages of the disease, providing a basis for novel treatment approaches. Objectives: To ascertain the morphological and immunohistochemical features of lung samples obtained in patients with moderate Covid-19 pneumonia. Methods: Transbronchial lung cryobiopsy was carried out in 12 Covid-19 patients within 20 days of symptom onset. Results: Histopathologic changes included spots of patchy acute lung injury with alveolar type II cell hyperplasia, with no evidence of hyaline membranes. Strong nuclear expression of phosphorylated STAT3 was observed in >50% of AECII. Interalveolar capillaries showed enlarged lumen and were in part arranged in superposed rows. Pulmonary venules were characterized by luminal enlargement, thickened walls, and perivascular CD4+ T-cell infiltration. A strong nuclear expression of phosphorylated STAT3, associated with PD-L1 and IDO expression, was observed in endothelial cells of venules and interstitial capillaries. Alveolar spaces macrophages exhibited a peculiar phenotype (CD68, CD11c, CD14, CD205, CD206, CD123/IL3AR, and PD-L1). Conclusions: Morphologically distinct features were identified in early stages of Covid-19 pneumonia, with epithelial and endothelial cell abnormalities different from either classical interstitial lung diseases or diffuse alveolar damage. Alveolar type II cell hyperplasia was a prominent event in the majority of cases. Inflammatory cells expressed peculiar phenotypes. No evidence of hyaline membranes and endothelial changes characterized by IDO expression might in part explain the compliance and the characteristic pulmonary vasoplegia observed in less-advanced Covid-19 pneumonia.

Gardnerella vaginalis (GV) is an anaerobic bacterial species involved in the pathogenesis of bacterial vaginosis (BV), a condition of vaginal dysbiosis associated with adverse pregnancy outcomes. GV strains are categorized into four clades, characterized by a different ability to produce virulence factors, such as sialidase. We investigated the distribution of GV clades and sialidase genes in the vaginal ecosystem of a cohort of pregnant women, assessing the correlations between GV clades and the whole vaginal microbiome. A total of 61 Caucasian pregnant women were enrolled. Their vaginal swabs, collected both at the first and third trimester of pregnancy, were used for (i) evaluation of the vaginal status by Nugent score, (ii) vaginal microbiome profiling by 16S rRNA sequencing, (iii) detection and quantification of GV clades and sialidase A gene by qPCR assays. DNA of at least one GV clade was detected in most vaginal swabs, with clade 4 being the most common one. GV clade 2, together with the presence of multiple clades (>2 simultaneously), were significantly associated with a BV condition. Significantly higher GV loads and sialidase gene levels were found in BV cases, compared to the healthy status. Clade 2 was related to the major shifts in the vaginal microbial composition, with a decrease in Lactobacillus and an increase in several BV-related taxa. As the number of GV clades detected simultaneously increased, a group of BV-associated bacteria tended to increase as well, while Bifidobacterium tended to decrease. A negative correlation between sialidase gene levels and Lactobacillus , and a positive correlation with Gardnerella , Atopobium , Prevotella , Megasphaera , and Sneathia were observed. Our results added knowledge about the interactions of GV clades with the inhabitants of the vaginal microbiome, possibly helping to predict the severity of BV and opening new perspectives for the prevention of pregnancy-related complications.

During pregnancy, the vaginal microbiome plays an important role in both maternal and neonatal health outcomes. Throughout pregnancy, the vaginal microbial composition undergoes significant changes, including a decrease in overall diversity and enrichment with Lactobacillus spp. In turn, the modifications in the microbial profiles are associated with shifts in the composition of vaginal metabolites. In this study, we characterized the vaginal metabolic profiles throughout pregnancy at two different gestational ages, correlating them with a microscopic evaluation of the vaginal bacterial composition. A total of 67 Caucasian pregnant women presenting to the Family Advisory Health Centres of Ravenna (Italy) were enrolled and a vaginal swab was collected at gestational ages 9–13 weeks (first trimester) and 20–24 weeks (second trimester). The composition of the vaginal microbiome was assessed by Nugent score and women were divided in ‘H’ (normal lactobacilli-dominated microbiota), ‘I’ (intermediate microbiota), and ‘BV’ (bacterial vaginosis) groups. Starting from the cell-free supernatants of the vaginal swabs, a metabolomic analysis was performed by means of a 1 H-NMR spectroscopy. From the first to the second trimester, a greater number of women showed a normal lactobacilli-dominated microbiota, with a reduction of cases of dysbiosis. These microbial shifts were associated with profound changes in the vaginal metabolic profiles. Over the weeks, a significant reduction in the levels of BV-associated metabolites (e.g. acetate, propionate, tyramine, methylamine, putrescine) was observed. At the same time, the vaginal metabolome was characterized by higher concentrations of lactate and of several amino acids (e.g. tryptophan, threonine, isoleucine, leucine), typically found in healthy vaginal conditions. Over time, the vaginal metabolome became less diverse and more homogeneous: in the second trimester, women with BV showed metabolic profiles more similar to the healthy/intermediate groups, compared to the first trimester. Our data could help unravel the role of vaginal metabolites in the pathophysiology of pregnancy.

PurposeTo investigate the prevalence, incidence and characteristics of bacterial infections and their impact on outcome in critically ill patients infected with COVID-19. MethodsWe conducted a prospective observational study in eight Italian ICUs from February to May 2020; data were collected through an interactive electronic database. Kaplan–Meier analysis (limit product method) was used to identify the occurrence of infections and risk of acquisition.ResultsDuring the study period 248 patients were recruited in the eight participating ICUs. Ninety (36.3%) patients developed at least one episode of secondary infection. An ICU length of stay between 7 and 14 days was characterized by a higher occurrence of infectious complications, with ventilator-associated pneumonia being the most frequent. At least one course of antibiotic therapy was given to 161 (64.9%) patients. Overall ICU and hospital mortality were 33.9% and 42.9%, respectively. Patients developing bacteremia had a higher risk of ICU mortality [45.9% vs. 31.6%, odds ratio 1.8 (95% CI 0.9–3.7), p = 0.069] and hospital mortality [56.8% vs. 40.3%, odds ratio 1.9 (95% CI 1.1–3.9), p = 0.04].ConclusionIn critically ill patients infected with COVID-19 the incidence of bacterial infections is high and associated with worse outcomes. Regular microbiological surveillance and strict infection control measures are mandated.

To exploit the features of digital PCR for implementing SARS-CoV-2 observational studies by reliably including the viral load factor expressed as copies/μL. A small cohort of 51 Covid-19 positive samples was assessed by both RT-qPCR and digital PCR assays. A linear regression model was built using a training subset, and its accuracy was assessed in the remaining evaluation subset. The model was then used to convert the stored cycle threshold values of a large dataset of 6208 diagnostic samples into copies/μL of SARS-CoV-2. The calculated viral load was used for a single cohort retrospective study. Finally, the cohort was randomly divided into a training set (n = 3095) and an evaluation set (n = 3113) to establish a logistic regression model for predicting case-fatality and to assess its accuracy. The model for converting the Ct values into copies/μL was suitably accurate. The calculated viral load over time in the cohort of Covid-19 positive samples showed very low viral loads during the summer inter-epidemic waves in Italy. The calculated viral load along with gender and age allowed building a predictive model of case-fatality probability which showed high specificity (99.0%) and low sensitivity (21.7%) at the optimal threshold which varied by modifying the threshold (i.e. 75% sensitivity and 83.7% specificity). Alternative models including categorised cVL or raw cycle thresholds obtained by the same diagnostic method also gave the same performance. The modelling of the cycle threshold values using digital PCR had the potential of fostering studies addressing issues regarding Sars-CoV-2; furthermore, it may allow setting up predictive tools capable of early identifying those patients at high risk of case-fatality already at diagnosis, irrespective of the diagnostic RT-qPCR platform in use. Depending upon the epidemiological situation, public health authority policies/aims, the resources available and the thresholds used, adequate sensitivity could be achieved with acceptable low specificity.

Background Multidrug-resistant Klebsiella pneumoniae represents a significant challenge in healthcare settings, prompting numerous studies on the rapid detection of antimicrobial resistance. Mass spectrometry has been recently integrated into routine laboratory diagnostics, providing highly sensitive results for pathogen identification. Furthermore, previously published studies have demonstrated its potential application in predicting antimicrobial resistance. Materials and methods The study collected 686 clinical isolates of K. pneumoniae from three Italian hospitals and used their MALDI-TOF mass spectra as input to machine learning models for predicting susceptibility profiles to amikacin and meropenem, which were selected as the most represented antibiotic molecules within the aminoglycoside and carbapenem classes, commonly used for the treatment of K. pneumoniae infections. After preprocessing, K. pneumoniae spectra were fed to machine learning classifiers within a nested cross-validation framework. Several performance metrics were computed to compare models and identify the most appropriate one for each antibiotic. Given the multicentric nature of the study, a batch-effect correction step was applied to reduce site-specific variability using the in-house developed Python package combatlearn (available on GitHub: https://github.com/EttoreRocchi/combatlearn ). Results The XGBoost model achieved the best performance for both antibiotics (AUROC = 0.822 ± 0.028 for amikacin; AUROC = 0.887 ± 0.019 for meropenem). A per-site performance analysis revealed that, while performances’ variability was inherently linked to each center’s sample size, combatlearn -based harmonization effectively aligned mean AUROC values across sites. Conclusions Our study demonstrates the capability of MALDI-TOF mass spectra to predict amikacin and meropenem resistance in K. pneumoniae directly from clinical spectra, supporting its potential as a rapid and cost-effective approach for both antimicrobial resistance surveillance through machine learning models and clinical decision support in routine microbiology practice.

Background The COVID-19 pandemic has significantly impacted human society at many levels, from public health to economics and transports, highlighting the need of approaches integrating all available information to better understand and model similar phenomena, also in order to develop early detection and responses. Methods In this paper we present an analysis of COVID-19 pandemic in the metropolitan area of Bologna, Italy, integrating an epidemiological mathematical model, SARS-CoV-2 virus quantification in wastewater, clinical hospitalization, vaccination campaign, virus genotypization and human mobility data in the period 2020-2022. Results We were able to follow the evolution of epidemic, observing the effect of vaccination and other factors that produced significant changes in hospitalizations. Moreover, by considering a mathematical model of COVID-19 epidemics spread, with parameters selected partly from literature and partly adapted to the local situation on a weekly basis, we identified a strict relation between human mobility at mesoscopic level and a sociability rate (related to model reproduction number). Conclusion Our results demonstrate the value of a interdisciplinary approach in monitoring and modeling epidemic trends. The observed relationships between mobility and sociability reveals the mutual impact of health issues on human activity and vice versa, providing insights for the implementation of effective response strategies in future pandemics.

Steatohepatitis and hepatobiliary manifestations constitute some of the most common extra-intestinal manifestations of Inflammatory Bowel Disease (IBD). On the other hand, non-alcoholic fatty liver disease (NAFLD) affects around 25% of the world’s population and is attracting ever more attention in liver transplant programs. To outline the specific pathways linking these two conditions is a pressing task for 21st-century researchers. We are accustomed to expecting the occurrence of fatty liver disease in obese people, but current evidence suggests that there are several different pathways also occurring in underweight patients. Genetic factors, inflammatory signals and microbiota are key players that could help in understanding the entire pathogenesis of NAFLD, with the aim of defining the multiple expressions of malnutrition. In the current review, we summarize the most recent literature regarding the epidemiology, pathogenesis and future directions for the management of NAFLD in patients affected by IBD.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first identified in Wuhan, China, in late 2019 and is the causative agent of the coronavirus disease 2019 (COVID-19) pandemic. Quantitative reverse-transcription polymerase chain reaction (qRT-PCR) represents the gold standard for diagnostic assays even if it cannot precisely quantify viral RNA copies. Thus, we decided to compare qRT-PCR with digital polymerase chain reaction (dPCR), which is able to give an accurate number of RNA copies that can be found in a specimen. However, the aforementioned methods are not capable to discriminate if the detected RNA is infectious or not. For this purpose, it is necessary to perform an endpoint titration on cell cultures, which is largely used in the research field and provides a tissue culture infecting dose per mL (TCID50/mL) value. Both research and diagnostics call for a model that allows the comparison between the results obtained employing different analytical methods. The aim of this study is to define a comparison among two qRT-PCR protocols (one with preliminary RNA extraction and purification and an extraction-free qRT-PCR), a dPCR and a titration on cell cultures. The resulting correlations yield a faithful estimation of the total number of RNA copies and of the infectious viral burden from a Ct value obtained with diagnostic routine tests. All these estimations take into consideration methodological errors linked to the qRT-PCR, dPCR and titration assays.