Antimicrobial resistance (AMR) represents one of the most urgent global health threats, significantly impacting patient outcomes, healthcare systems, and economic sustainability. Rapid and accurate antimicrobial susceptibility testing (AST) are essential to guide targeted therapy, reduce inappropriate antimicrobial use, and support antimicrobial stewardship programs. However, conventional phenotypic AST methods, including broth microdilution, disk diffusion, agar dilution, and gradient strip tests, remain labor-intensive and require prolonged turnaround times, often delaying optimal therapeutic decisions. Although automated commercial platforms such as VITEK 2, BD Phoenix, MicroScan WalkAway, and Sensititre ARIS have improved laboratory workflow and standardization, they still rely on culture-based approaches and typically require 16–36 h to generate minimum inhibitory concentration (MIC) results. In recent years, several innovative rapid phenotypic AST technologies have emerged, aiming to significantly shorten the time to susceptibility results while maintaining high accuracy. This review provides an overview of currently available rapid automated phenotypic platforms for MIC determination, including VITEK^® Reveal™, ASTar, FASTinov^®AST, QuickMIC^®, and the Accelerate Pheno^® system. These systems employ advanced technologies such as volatile organic compound detection, flow cytometry, microfluidics, real-time imaging, and morphokinetic cellular analysis to deliver susceptibility results within a few hours directly from positive blood cultures. We summarize their technical principles, antibiotics and pathogens included, performances, and current limitations. Overall, the implementation of rapid phenotypic AST tools has the potential to substantially improve clinical decisions, optimize antimicrobial therapy, and contribute to fight AMR.

Background. Over the past decade, the World Health Organization has highlighted the need for rapid molecular diagnostics as first-line tools for detecting Mycobacterium tuberculosis complex (MTBC) to strengthen global tuberculosis control. At the same time, infections caused by non-tuberculous mycobacteria (NTM) have become increasingly prevalent, particularly in low TB-burden countries such as Italy. This changing epidemiological scenario underscores the necessity for fast and reliable methods capable of distinguishing NTM from MTBC, a critical step for guiding appropriate treatment. This study evaluated the diagnostic accuracy and potential applications of the STANDARD™ M10 MTB/NTM assay, which simultaneously detects and differentiates MTBC and NTM. Methods. A total of 155 clinical specimens (78.1% respiratory) from patients with suspected mycobacterial infection were tested by fluorescence microscopy, GeneXpert MTB/RIF Ultra (respiratory samples only), STANDARD™ M10 MTB/NTM and culture, used as the reference method. Results. Culture detected MTBC in 54% and NTM (predominantly slow-growing species) in 46% of samples. STANDARD™ M10 showed overall sensitivity and specificity of 70% and 100%, respectively. For MTBC, sensitivity was 85.1% with almost perfect agreement with culture (κ = 0.866), while for NTM, sensitivity was 50% with moderate agreement (κ = 0.566). Sensitivity decreased in microscopy-negative/culture-positive specimens, particularly for NTM. Compared with GeneXpert MTB/RIF Ultra, STANDARD™ M10 exhibited slightly lower sensitivity for MTBC but retained excellent specificity. Conclusions. STANDARD™ M10 MTB/NTM represents a rapid, fully automated tool to support early etiological diagnosis and MTB/NTM differentiation, mainly in selected samples or high-risk patients, but it does not replace culture or molecular tests providing species identification and MTBC drug-resistance profiling.

Background: Climate change represents a major global health challenge, with rising temperatures and altered precipitation patterns influencing the spread of infectious diseases. This study investigated the association between climatic factors (average temperature and precipitation) and the monthly proportion of laboratory-confirmed Legionella pneumophila serogroup 1 and West Nile Virus infections among clinically suspected patients in a large teaching hospital in Northern Italy. Methods: We retrospectively analyzed data from 2021 to 2024. The primary outcome was the monthly proportion of positive tests (standardized per 1000 clinically suspected patients) for Legionella pneumophila serogroup 1 (urinary antigen) and West Nile Virus (serology). Associations with climatic variables were assessed using linear and multivariate regression models, as well as Generalized Additive Models (GAMs). Seasonal effects were evaluated through ANOVA. Results: For Legionella pneumophila, precipitation was not significantly associated with the proportion of positive tests (p = 0.1438; R² = 0.049). In contrast, average temperature was a significant predictor: each 1 °C increase was associated with +0.52 positive cases per 1000 tested patients (p = 0.000283; R² = 0.267). Multivariate models confirmed temperature as the dominant factor. For West Nile Virus, precipitation showed no meaningful effect (p = 0.914). However, average temperature demonstrated a significant positive association with the proportion of positive cases (p = 0.00293; coefficient = 9.33), with seasonal analysis highlighting a marked summer peak (mean = 399.68 positive cases per 1000 tested; p = 0.00653). Conclusions: Our findings underline the predominant role of temperature over precipitation in driving the burden of both Legionella pneumophila and West Nile Virus infections among hospitalized patients. These results strengthen the evidence that the life cycles of these pathogens are tightly climate-dependent. Developing effective adaptation strategies is essential to mitigate climate-related health risks.