Introduction: Eravacycline (ERV) is a novel synthetic fluorocycline antibiotic with broad-spectrum antibacterial efficacy against pathogens. This study aimed to investigate the clinical effectiveness of eravacycline and its correlation with minimum inhibitory concentrations (MICs) against infections caused by Acinetobacter baumannii or Klebsiella pneumoniae . Methods: This retrospective multicenter study investigated the real-world use of ERV in 1,796 adults with infection caused by A. baumannii or K. pneumoniae in China. Antimicrobial susceptibility of strains and laboratory test results during treatment were analyzed. Microbiological and clinical outcomes were assessed at the end of treatment and day 30. Results: The overall susceptibility rate to ERV was 96.0% (1,027/1,070), and around 98% of carbapenem-resistant isolates were susceptible to ERV. ERV had a 4-fold lower MIC ₉₀ than tigecycline against both pathogens. At end of treatment, treatment success (microbiological eradication or clinical resolution) occurred in 82.6% (1,483/1,796) of the cohort with microbiological eradication achieved 76.1% (789/1,037). At day 30, infection cure was achieved in 83.57% (1,501/1,796) of the cases. Different ERV regimens (monotherapy or concomitant therapy) had no influence on the treatment and 30-day clinical outcomes. Multivariable analysis identified that elevated C-reactive protein (CRP) levels during treatment, bloodstream infection, sepsis and specific clinical interventions (e.g., central venous catheterization) were independent predictors of treatment failure. Conclusions: The study highlights ERV’s utility in infection of A. baumannii or K. pneumoniae , especially carbapenem-resistant strains.

Moraxella catarrhalis, a commensal in the human nasopharynx, plays a significant role in the acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Its pathogenicity involves adherence to respiratory epithelial cells, leading to infection through a macropinocytosis-like mechanism. Previous investigations highlighted the diverse abilities of M. catarrhalis isolates with different phenotypes to adhere to and invade respiratory epithelial cells. This study used a murine COPD model and in vitro experiments to explore the factors influencing the pathogenicity of distinct phenotypes of M. catarrhalis. Transcriptome sequencing suggested a potential association between actin cytoskeleton regulation and the infection of lung epithelial cells by M. catarrhalis with different phenotypes. Electron microscopy and Western blot analyses revealed a decrease in filamentous actin (F-actin) expression upon infection with various M. catarrhalis phenotypes. Inhibition of actin polymerization indicated the involvement of F-actin dynamics in M. catarrhalis internalization, distinguishing it from the adhesion process. Notably, hindering F-actin polymerization impaired the internalization of M. catarrhalis. These findings contribute vital theoretical insights for developing preventive strategies and individualized clinical treatments for AECOPD patients infected with M. catarrhalis. The study underscores the importance of understanding the nuanced interactions between M. catarrhalis phenotypes and host lung epithelial cells, offering valuable implications for the management of AECOPD infections.

Automated AI-assisted colony counting may improve standardization in digital microbiology, but performance can be affected by colony density, culture medium, colony morphology, adhesion, and plate artifacts. We evaluated the Starry-300 AI colony counting system using 382 standardized pure culture bacterial and yeast plates across four agar media. AI-assisted counts were compared with a three-reader median ImageJ-assisted manual comparator derived from independent counts by experienced technologists. The AI workflow showed close agreement with the manual consensus comparator across a broad colony density range. Overall, 360/382 plates (94.24%) were within ±10 CFU and 377/382 plates (98.69%) were within ±30 CFU of the manual median count. Error-based and agreement analyses showed a mean absolute error of 3.19 CFU/plate; both the intraclass correlation coefficient and Lin’s concordance correlation coefficient were0.99. AI software analysis required approximately 5–15 s/plate, although this did not include plate handling, correction, or reporting. These findings support the analytical feasibility of reviewable AI-assisted colony enumeration under controlled pure culture conditions. Further validation using primary clinical specimens, mixed cultures, near-threshold samples, and external sites is required before broad clinical implementation.

Microbial colony analysis is routinely used in clinical microbiology, pharmaceutical quality control, food safety, and environmental monitoring. Colony enumeration remains challenging because real-world plates frequently show crowding, size variation, background debris, and plate-rim interference. Low contrast on membrane or filter media, weak colony boundaries, and ultra-dense or too-numerous-to-count (TNTC) growth further complicate analysis. These conditions increase counting variability and limit rule-based counters. This review examines artificial intelligence (AI)-enabled colony analysis and links recurrent plate-reading challenges to method selection and validation requirements. We examine traditional threshold- and morphology-based pipelines, object detection, semantic segmentation, instance segmentation, and density- or keypoint-based estimation. Current evidence is stronger for plate-level triage and semi-quantitative interpretation than for direct colony enumeration. However, evidence supporting routine quantitative colony-forming unit (CFU) reporting across laboratories remains limited. Reliable deployment requires methods suited to plate format, organism, imaging conditions, and intended use, with validation in the target workflow.

Candida infections are among the most common fungal diseases worldwide and are associated with notable morbidity and mortality, particularly in immunocompromised and critically ill patients. Moreover, the emergence and spread of antifungal resistance pose an increasing threat to global public health. Therefore, evidence-based recommendations are urgently needed to guide the surveillance and control of Candida infections. A multidisciplinary guidance development group comprising experts of infectious diseases, clinical microbiology, pharmacology, infection control, and guideline methodology jointly developed the present clinical practice guidance. The process followed internationally recognized grading of recommendations, assessment, development and evaluation (GRADE) framework. Clinical questions were formulated based on priority issues or areas of uncertainty in current practice, and corresponding recommendations were developed after systematic review and synthesis of the best available evidence. The strength of recommendations was classified as strong or conditional (weak), and the certainty of evidence was graded from high to low. The guidance provides recommendations on diagnostic strategies, antifungal therapy, and infection prevention and control measures for Candida infections, integrating both global evidence and national clinical experience. The target users include clinicians, microbiologists, infection control practitioners, and policymakers involved in the management of fungal diseases. The guidance aims to support optimized clinical decision-making, improve antifungal stewardship, and strengthen infection control strategies to address the growing challenge posed by Candida infections.