Aztreonam-avibactam (AZA) is a recently approved agent active against metallo-β-lactamase-producing Enterobacterales. However, independent evaluations of commercially available antimicrobial susceptibility testing (AST) products remain limited. We evaluated 56 carbapenem-resistant Enterobacterales isolates using four newly available AZA AST products, including two gradient diffusion tests (bioMérieux Etest; Liofilchem MIC Test Strip) and two disk diffusion tests (Hardy, Liofilchem) across three Mueller-Hinton agar manufacturers at two independent clinical laboratories. A diverse isolate collection enriched for nonsusceptible and breakpoint-adjacent MICs was used to rigorously assess categorical agreement (CA). Broth microdilution (BMD) was used as the reference method. Essential agreement (EA), categorical agreement, error rates, and reproducibility were analyzed. All four commercial AST products demonstrated acceptable analytical performance compared with reference BMD. Gradient diffusion demonstrated high EA (bioMérieux 92.6%, Liofilchem 89.6%), whereas CA ranged from 81.5% to 89.3% across all four products. Minor errors accounted for all categorical discrepancies and occurred almost exclusively among breakpoint-adjacent isolates. Medium-related variability was modest, and quality control (QC) performance was generally high, with strong within-site precision and consistent performance across laboratories. This study provides the first comprehensive head-to-head comparison of commercial AZA susceptibility testing products and supports their implementation in clinical microbiology laboratories.IMPORTANCEAztreonam-avibactam is an important new treatment option for infections caused by carbapenem-resistant Enterobacterales, particularly metallo-β-lactamase-producing organisms, but its clinical use requires reliable susceptibility testing. We provide the first head-to-head evaluation of four newly available commercial gradient and disk diffusion products across two independent laboratories and three Mueller-Hinton agar manufacturers. By using a diverse isolate collection enriched for MICs near clinical breakpoints, we also subjected these products to a stringent assessment of categorical agreement. Gradient diffusion demonstrated high essential agreement, and no major or very major errors remained after repeat and discrepancy testing with any product. These findings provide timely, practical evidence to guide clinical laboratories implementing aztreonam-avibactam susceptibility testing.

Microscopic detection of Plasmodium spp. using thin and thick blood smears remains the gold standard for diagnosing malaria, but its performance is highly dependent on stain quality and microscopist expertise. Recently, artificial intelligence (AI)-based technologies have enhanced laboratory performance in malaria diagnosis. This study aimed to evaluate the performance of miLab MAL (Noul, Inc., Republic of Korea), an AI-assisted microscopy system designed to detect Plasmodium species using thin blood smears. Over a 6-month period, 162 blood samples from patients with suspected malaria or undergoing post-treatment follow-up were prospectively analyzed using conventional diagnostic methods and compared with miLab MAL results. Conventional methods identified 83 positive and 79 negative samples, whereas miLab MAL identified 76 positive and 86 negative samples. Compared with loop-mediated isothermal amplification (LAMP) and/or thick blood smear, miLab MAL showed a sensitivity of 90.4%, a specificity of 98.7%, and a Cohen's kappa of 0.89. When compared with thin smear microscopy alone, sensitivity increased to 97.4%, with a specificity of 97.7% and a kappa value of 0.95. Regarding species identification, 11 P. falciparum were not correctly identified at the species level, while two P. ovale and one P. malariae were misidentified as P. vivax by the miLab MAL. Overall, the miLab MAL tended to underestimate parasitemia. This first evaluation of miLab MAL in France shows a promising performance for Plasmodium detection, although improvements are needed for species differentiation and parasitemia quantification. With further optimization, this approach may become a valuable tool for malaria diagnosis in resource-limited or high-burden settings.IMPORTANCEArtificial intelligence (AI)-assisted microscopy is a promising approach to improving the consistency and accuracy of malaria diagnosis, particularly in laboratories with limited microscopy expertise. In this study, we evaluated the performance of the miLab MAL, an AI-assisted digital microscopy system developed for the rapid detection and identification of Plasmodium species in clinical samples. The system demonstrated high sensitivity and specificity for Plasmodium detection, suggesting its potential value as a diagnostic support tool in routine laboratory settings. However, limitations were identified in species-level identification and parasite quantification.

Carbapenem-resistant Enterobacterales (CRE) are an increasing public health concern, particularly as carbapenemase-producing isolates, such as NDM and OXA-48-like producers, become more prevalent. Rapid and accurate identification of carbapenemases is crucial to guide appropriate antibiotic therapy. Although recent epidemiologic data show that CRE increasingly harbor multiple carbapenemases, the performance of common phenotypic tests-the modified carbapenem inactivation method (mCIM) and EDTA-modified CIM (eCIM)-has not been well defined in these isolates. We examined the performance of mCIM and eCIM among CRE-encoding NDM, OXA-48-like, or both. We included 172 genotypically confirmed carbapenemase-encoding CRE isolates from perirectal swabs collected from patients admitted to two intensive care units at a hospital in South India. Isolates included Escherichia coli (53.5%), Klebsiella pneumoniae (41.3%), and Enterobacter species (5.3%). Overall, 112 isolates (65.1%) encoded NDM alone, 23 (13.4%) encoded OXA-48-like alone, and 37 (21.5%) encoded both NDM and OXA-48-like. mCIM positivity was 98.2% for NDM, 47.8% for OXA-48-like, and 94.6% for NDM+OXA-48-like encoders; among mCIM-positive isolates, eCIM positivity rates were 99.1%, 91%, and 97.1%, respectively. When compared to molecular carbapenemase gene detection, mCIM/eCIM was highly sensitive for classifying isolates encoding NDM as metallo-β-lactamase (MBL) producers, but significantly misclassified isolates encoding OXA-48-like as having an MBL or lacking a carbapenemase. In the largest collection of NDM and OXA-48-like co-encoding CRE isolates to date, mCIM/eCIM typically reported these as MBL producers.

The optimal phage susceptibility testing (PST) method is unclear. Here, two PST methods were compared using Pseudomonas aeruginosa isolates recovered from patients with cystic fibrosis enrolled in a placebo-controlled clinical trial of a single intravenous dose of a four-phage cocktail (WRAIR-PAM-CF1). In total, 508 P. aeruginosa sputum isolates were evaluated, including isolates collected before or after phage or placebo treatment, isolates from the same trial participants over time, and isolates of different colony morphologies from the same participants at the same time point. Susceptibility to phages PaWRA01Φ11, PaWRA01Φ39, PaWRA02Φ83, and PaWRA02Φ87 and a cocktail of all four phages was assessed using a plaque assay (PA) and a Biolog Omnilog-based liquid assay (LA) with the same bacterial inoculum. A total of 170/508 isolates (33%) and 195/508 isolates (38%) were identified as susceptible to the cocktail by PA and LA, respectively. Among the susceptible isolates, 169/170 (99%; PA) and 187/195 (96%; LA) also tested susceptible to at least one of the four phages in the cocktail. Susceptible percent agreement between the PA and LA ranged from 76% to 100% and non-susceptible percent agreement ranged from 89% to 100% for all phages studied and the cocktail. Phage PaWRA02Φ83 demonstrated the greatest individual activity, with 164/508 (32%) and 188/508 (37%) isolates testing susceptible by PA and LA, respectively. The four-phage cocktail provided numerically broader coverage of P. aeruginosa (i.e., had the highest susceptibility rate) than did any single phage. There was high concordance between PST results by PA and LA.IMPORTANCEStandardization of phage susceptibility testing (PST) methods is essential to support phage therapy trials and any future clinical implementation. There are no standardized PST methods or interpretive criteria for PST. In this study, PST was performed by a plaque assay and a Biolog Omnilog-based liquid assay on Pseudomonas aeruginosa isolates recovered from cystic fibrosis patients enrolled in a clinical trial. Susceptibility of 508 isolates to four phages and a cocktail of all four was evaluated. There was high concordance between susceptibility assessed by plaque and liquid assays and numerically broader coverage of P. aeruginosa by the cocktail compared to any single phage. This manuscript should be of interest to those performing PST.

Candidozyma auris (formerly Candida auris), first described in 2009, has spread to 82 countries across 6 continents, with more than 84,000 cumulative colonization and infection cases reported through December 2025. Whole-genome sequencing has resolved the species into six deeply divergent clades (I-VI) that differ geographically and in antifungal susceptibility. This minireview synthesizes peer-reviewed literature and public health surveillance data on whole-genome-sequencing-confirmed clade distribution and susceptibility across the United States, the European Union/European Economic Area, the United Kingdom, Latin America and the Caribbean, Saudi Arabia and the wider Gulf region, Canada, Singapore, Australia, and New Zealand. Clades I and III are now the most geographically widespread outbreak-associated lineages, documented across nearly every region reviewed. The United States has diversified from early clade I dominance to a four-clade landscape by 2013-2022 (clade I, 56.0%; clade III, 29.4%; clade IV, 14.1%; and clade II, 0.5%). Latin America remains the principal reservoir of clade IV; Saudi Arabia and the Gulf region are dominated by clade I, and the discovery of clade VI in Singapore and Bangladesh illustrates continuing genomic diversification. Clade I carries the highest burden of multidrug and pan-resistance; clade III shows high fluconazole resistance but lower echinocandin resistance; and clade IV, historically associated with lower resistance, is now rising. Because regional antifungal resistance largely reflects clade composition rather than independent regional drift, clade-aware surveillance should directly inform local stewardship, diagnostic inclusivity, and regulatory bridging across markets.

Our objective was to evaluate the diagnostic accuracy of Aspergillus droplet digital PCR (ddPCR) in exhaled breath condensate (EBC) and bronchoalveolar lavage fluid (BALF) for diagnosing invasive aspergillosis (IA), and to compare its performance with quantitative real-time PCR (qPCR) in patients with hematologic malignancies. We conducted a prospective single-center study, including adults admitted to leukemia and allogeneic hematopoietic stem cell transplant units who underwent bronchoscopy for pulmonary infiltrates between October 2021 and December 2024. EBC and BALF were collected concurrently. ddPCR was performed on EBC and BALF (supernatant and cell pellet); qPCR was performed on BALF samples. IA was classified according to EORTC/MSGERC criteria without incorporating PCR into the reference standard. Diagnostic performance characteristics, including area under the receiver operating characteristic curve (AUC-ROC), were calculated. Among 203 participants, 26 (12.8%) were diagnosed with probable or proven IA. EBC ddPCR showed poor performance (AUC-ROC 0.55, 95% CI 0.43-0.67). BALF supernatant ddPCR demonstrated the best performance (AUC-ROC 0.88, 95% CI 0.80-0.96), with sensitivity of 0.65 (95% CI 0.43-0.84) and specificity of 0.96 (95% CI 0.92-0.99). BALF supernatant outperformed BALF cell pellet testing for both ddPCR and qPCR. Compared with qPCR, ddPCR showed higher sensitivity and similarly high specificity. Among participants without prior mold-active antifungal therapy, BALF supernatant ddPCR showed improved performance (AUC-ROC 0.95, 95% CI 0.89-1.00). In this large prospective study, EBC ddPCR demonstrated limited utility for the diagnosis of IA. BALF supernatant was the optimal specimen type, and ddPCR outperformed qPCR. Diagnostic performance improved substantially without prior mold-active antifungal exposure.IMPORTANCEInvasive aspergillosis is difficult to diagnose in patients with hematologic malignancies, and non-invasive diagnostic approaches are needed. In this prospective study of 203 patients, Aspergillus droplet digital PCR performed poorly in exhaled breath condensate (area under the receiver operating characteristic curve 0.55; 95% CI 0.43-0.67) but showed good diagnostic performance in bronchoalveolar lavage supernatant fluid (area under the receiver operating characteristic curve 0.88; 95% CI 0.80-0.96). Droplet digital PCR demonstrated higher sensitivity than quantitative PCR with similarly high specificity, and bronchoalveolar lavage supernatant fluid consistently outperformed cell pellet testing. Among patients without prior mold-active antifungal therapy, the area under the receiver operating characteristic curve was 0.95 (95% CI 0.89-1.00). These findings support bronchoalveolar lavage supernatant fluid as the preferred specimen for Aspergillus droplet digital PCR and warrant further evaluation of this approach for diagnosing invasive aspergillosis.

The description of new taxa and nomenclature updates to currently known taxa from aquatic animal species continues at a rapid pace. After a review of the literature from 2024 to 2025, bacteria in major morphologic categories were compiled. As previously noted, bacteria characterized as oxidase-positive Gram-negative bacilli predominated. Additions included new taxa in common families associated with aquatic environments like Aeromonadaceae, Flavobacteriaceae, and Pseudomonadaceae, as well as more uncommon families, such as Pasteurellaceae. A number of Gram-positive organisms are noted, including Planococcus notacanthi sp. nov., the sole coccus described, and multiple bacilli, including new taxa in the Streptomyces and Paenibacillus genera. Two anaerobic species are highlighted, as well as a spirochete and a new member of family Chlamydiaceae. Revised taxa are briefly mentioned. As in previous reviews, most new and revised taxa are isolated from healthy aquatic animals, and the role of these new bacteria in health and disease has yet to be elucidated. Bacteria with potential clinical significance for aquatic animals or potential production of bioactive or antimicrobial substances are highlighted.

Sepsis prediction algorithms based on machine learning are increasingly embedded in electronic health record systems, and clinical laboratories have been largely absent from their development, validation, and governance. This is a consequential gap. Laboratory variables are central inputs to most deployed sepsis artificial intelligence (AI) tools, and routine analytical events such as platform transitions, reagent reformulations, and reference interval updates can shift the distributions of these inputs. Such distribution shifts are a recognized cause of degraded model performance; however, the effects of laboratory-specific changes remain insufficiently characterized for sepsis tools in clinical use. Neither clinical teams nor laboratories are currently well positioned to detect these effects. This review is written for clinical laboratorians who encounter these tools in practice or are asked to evaluate them for implementation. We describe the data sources used as model inputs and how analytical context, preanalytical variability, and ordering patterns influence model behavior. Five deployed algorithms with real-world implementation evidence are profiled alongside the broader preclinical landscape, including early warning systems, mortality and complication prediction models, and sepsis subtyping research. Development and implementation pitfalls are reviewed with attention to laboratory-specific considerations, and the regulatory and governance landscape is addressed with emphasis on where laboratorian involvement is most needed. Controlled outcomes data supporting currently deployed tools remain limited, and reported performance frequently does not replicate at external sites. Laboratorians bring expertise in analytical quality, data provenance, and change management that is not replicated elsewhere on implementation teams. As the field moves toward sepsis subtyping, continuous monitoring, and local model calibration, laboratorian expertise in analytical quality and monitoring will become increasingly central to how these tools are built, deployed, and maintained.

Pediatric skeletal system infections are medical emergencies that may cause severe acute complications and long-term orthopedic disabilities. Because the clinical presentation of septic arthritis and osteomyelitis may mimic non-infectious conditions, early identification of the causative agent and determination of its antibiotic susceptibility profile are cornerstones of patient management. The etiology of childhood skeletal system infections has changed over time, reflecting the implementation of conjugate vaccines against Haemophilus influenzae type b and Streptococcus pneumoniae, as well as the development of sensitive molecular detection tests. These novel laboratory methods, coupled with rapid pathogen identification by MALDI-TOF technology, have reduced the fraction of "culture-negative" infections and shortened the turnaround time for pathogen identification from days to hours. However, even today, a substantial fraction of bone and joint infections remains microbiologically unconfirmed. Metagenomic next-generation sequencing holds great promise, but its high cost, the need for technical expertise, and the detection of commensal organisms of dubious significance severely limit its routine use. On the other hand, metagenomic next-generation sequencing has not expanded the list of known pediatric skeletal system pathogens, suggesting that underutilization of current diagnostic methodologies, rather than reduced sensitivity, drives suboptimal diagnosis.