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PubMed2026

Global reach and sustained engagement of a structured digital education program in medical mycology: an observational analysis of the 2025 ESCMID-EFISG webinar series.

OBJECTIVES: Evaluate the 2025 European Society of Clinical Microbiology and Infectious Diseases-European Fungal Infection Study Group webinar series to assess digital education as a scalable, equitable model for global professional development in medical mycology. METHODS: This observational study analyzed Zoom metadata across 17 webinars (January-December 2025). Metrics included registration, unique viewers, peak concurrent views, attendance rate, and duration. RESULTS: The series recorded 4631 registrations and 1372 unique participants. Median live attendance was 199 (interquartile range [IQR] 138-269), with a 39.3% attendance rate (IQR 33.7-47.5%) and peak concurrent viewership of 165 (IQR 106-229). Median session duration was 108 minutes. Webinars engaged a median of 60 countries (range 26-89) simultaneously, spanning 128 countries globally. Faculty comprised 67 unique experts from 23 countries with balanced gender representation (52.8% men, 47.2% women), of whom 16.4% (n = 11/67) were affiliated with institutions in low- and middle-income countries. CONCLUSION: Structured digital programs achieve wide global reach and sustained engagement. Strong participation in long-form sessions supports implementing Continuing Medical Education accreditation and unrestricted on-demand access to enhance global health equity.

Antimicrobial resistance↗

PubMed2026

Machine learning to differentiate colonization from infection in multidrug-resistant Gram-negative bacteria: implications for further research.

PURPOSE OF REVIEW: Machine learning has emerged as a promising tool to support antimicrobial decision-making in infectious diseases. In colonized patients, distinguishing multidrug-resistant Gram-negative bacteria (MDR-GNB) colonization from true infection remains a major clinical challenge, as both delayed appropriate therapy in severe infections and unnecessary broad-spectrum antimicrobial use may adversely affect patient outcomes and antimicrobial stewardship. This review discusses the current evidence on machine learning models for predicting or detecting MDR-GNB infection in colonized patients, highlights key methodological limitations of the available literature, and outlines future research priorities. RECENT FINDINGS: Current evidence specifically evaluating machine learning models beyond logistic regression in MDR-GNB-colonized patients remains limited. Overall, while machine learning may achieve encouraging discriminatory performance, important methodological limitations persist. Most notably, predictive models are frequently developed in heterogeneous populations that do not reflect the clinically relevant populations of colonized patients in which treatment decisions are made. Furthermore, improvements in predictive performance remain modest, possibly reflecting limited sample sizes and data granularity rather than insufficient algorithmic complexity. In our opinion, future advances could require multicenter datasets enriched with longitudinal clinical, microbiological, and genomic information, together with automated feature extraction from electronic health records. SUMMARY: The main challenge for machine learning in predicting MDR-GNB infection in colonized patients may lie not in developing increasingly sophisticated algorithms, but in generating clinically representative datasets and adopting rigorous methodological standards for model development, validation, calibration, and implementation. Future research should prioritize clinically meaningful target populations and demonstrate improvements in patient outcomes and antimicrobial stewardship beyond conventional measures of predictive performance.

antimicrobial resistance↗