The epidemiology of vancomycin-resistant Enterococcus faecium (VREfm) varies across different countries, with a steady global increase. In Portugal, however, epidemiological data on clinical VREfm have been scarce since the early 2000s. This long-term study investigates VREfm isolates from human infections collected at a Porto hospital between 2010 and 2021. Two hundred VREfm isolates, mostly urinary (39
Pediatric obesity is linked to multi-organ inflammation and an increased risk of cardiometabolic and steatotic liver disease. To identify circulating biomarkers of cardiometabolic risk, we performed proximity extension assay proteomics, to quantify 149 inflammation- and cardiovascular-related proteins in a cross-sectional study of 4024 children and adolescents (2377 with obesity and 1647 with normal weight). We identified protein signatures linked to obesity, dyslipidemia, insulin resistance, hyperglycemia, hypertension, and related cardiometabolic phenotypes. Using machine learning, a three-protein panel (CDCP1, FGF21, HAOX1) combined with liver enzymes improved prediction of steatotic liver disease versus liver enzymes alone (receiver operating characteristic–area under the curve (ROC–AUC) = 0.83 vs. 0.77; DeLong’s test, P < 0.05). During a 1-year non-pharmacological obesity intervention (n = 184), reductions in adiposity were associated with decreased inflammatory cytokines (including CDCP1, FGF21), which correlated with improvements in cardiometabolic risk profiles. Here we show that circulating proteomic signatures may mediate obesity-related cardiometabolic risk in youth. This study identifies protein signatures of pediatric obesity linked to cardiometabolic risk, shows treatment-related changes, and highlights a three-protein panel predictive of steatotic liver disease for early diagnosis
SCOPE:The 2017 European Committee on Antimicrobial Susceptibility Testing (EUCAST) subcommittee report on the role of whole genome sequencing (WGS) in antimicrobial susceptibility testing (AST) concluded that WGS antimicrobial susceptibility prediction (WGS-ASP) was not a sufficiently robust alternative to AST to guide clinical decision making at that stage and that more evidence was required [1]. Since then, the use of WGS, bioinformatic tools, machine learning (ML)/artificial intelligence (AI), databases, and prediction approaches has greatly expanded, along with an increased knowledge of resistance mechanisms and their contribution to antimicrobial susceptibility. In response, a new EUCAST ad hoc subcommittee was established in 2024 to review the literature, with the aim of assessing the current potential and limitations of WGS-ASP. METHODS:As in the previous report, the subcommittee reviewed the literature on a 'by organism' basis but expanded the list to also include enterococci, Haemophilus influenzae, and Bacteroides fragilis in addition to those already included in the first version: Enterobacterales, Pseudomonas aeruginosa, Acinetobacter baumannii, Neisseria gonorrhoeae, Staphylococcus aureus, Streptococcus pneumoniae, Clostridioides difficile, and Mycobacterium tuberculosis. Additional sections were included to cover advances in metagenomics, other omics technologies and ML/AI. The full report was compiled and reviewed by all subcommittee members before public consultation in November 2025. CONCLUSIONS AND RECOMMENDATIONS:Significant progress has been achieved in WGS-ASP, with growing evidence supporting its ability to distinguish wild-type from non-wild-type isolates and, consequently, susceptible from resistant strains, particularly for M. tuberculosis and when clinical breakpoints align with the epidemiological cut-off (ECOFF). Despite these advances, important challenges remain before WGS-ASP can be adopted as a clinical decision-making tool. Addressing these gaps will require integrated phenotypic and genotypic surveillance to strengthen the evidence base for complex resistance mechanisms and newer antimicrobial agents, alongside comparative assessments that consider both ECOFF and clinical breakpoints. The analyses will require reference method phenotypic AST and high-quality genomic data. It is critical to ensure that datasets reflect the target populations and encompass the full spectrum of antimicrobial susceptibility, while developing unified interpretation frameworks and harmonized bioinformatics tools to standardize outputs. Robust external quality assessment schemes will be essential for clinical validation, and emerging technologies such as AI and ML offer promising avenues to enhance predictive accuracy. Finally, improvements in cost and turnaround time, coupled with evaluations of setting-specific cost-effectiveness, will be key to enabling practical implementation of WGS-ASP.
Important questions remain regarding differences in geographical and age-specific mortality patterns as the COVID-19 pandemic evolved in consecutive waves, and how COVID-19 mortality compares to seasonal influenza. In a pandemic situation, excess all-cause mortality provides a more complete and robust measure than cause-specific mortality. Data submitted by 26 countries participating in the European Mortality Monitoring (EuroMOMO) network between 2020 and 2023 was analysed to quantify excess all-cause mortality during the COVID-19 pandemic. Excess mortality from this period was compared to previous influenza seasons from 2014 to 2019. Pooled estimates of excess mortality showed four main waves during the COVID-19 pandemic period, most markedly in people aged 65 years and above, with timing and magnitude that varied between countries. Here we show that prior to implementation of control measures and COVID-19 vaccination, excess mortality greatly exceeded typical seasonal influenza mortality, but later during the pandemic was at levels comparable to influenza.
The nasal microbiome, a dynamic assemblage of commensals and opportunistic pathogens, is crucial to human health. Using cross-sectional data from 1,608 adults and longitudinal sampling of 149 individuals over 8–22 months, we identified nine nasal community state types (CSTs), defined by bacterial density and indicator taxa, with varying stability and transition patterns. Core taxa such as Staphylococcus epidermidis and Cutibacterium acnes were highly stable, while opportunistic pathogens like Staphylococcus aureus and Moraxella catarrhalis had shorter residence times. Interactions between Dolosigranulum pigrum and Corynebacterium pseudodiphtheriticum/propinquum were linked to reduced S. aureus colonization. Host factors, including age and biological sex, significantly shaped microbiome dynamics: men exhibited higher bacterial densities and pathogen colonization, while women showed more stable commensal-dominated CSTs. Aging was associated with shifts in CST frequencies, with declining S. aureus and increasing Enterobacterales. These findings reveal potential strategies by modulating nasal microbiome dynamics to reduce pathogen colonization and improve health.
