Diagnostic microbiology and infectious disease最新文献

Background: Scrub typhus (ST) is a rickettsial infection endemic in Southeast Asia, with diagnostic challenges due to its clinical overlap with other febrile illnesses. Due to its accessibility and ease of use, Enzyme-linked Immunosorbent Assay (ELISA) is the widely preferred diagnostic tool.

Objectives: This study aims to evaluate the diagnostic performance of the Scrub typhus IgM Microlisa kit (J. Mitra & Co Pvt., Ltd.,) for detecting ST IgM antibodies, using the Scrub Typhus Detect™ IgM ELISA (Inbios International, Inc.) as a reference standard.

Methods: A retrospective observational study was conducted using 547 serum samples archived between January 2022 and December 2023. Samples were tested for ST IgM antibodies using the J. Mitra Scrub typhus IgM Microlisa kit (index test) and the Inbios Scrub Typhus Detect™ IgM ELISA kit (reference standard). Sensitivity, specificity, diagnostic accuracy, and likelihood ratios were calculated with 95 % confidence intervals.

Results: Of the 247 seropositive samples demonstrated a sensitivity of 87.45 % (95 % CI: 82.74-91.02), specificity of 97.67 % (95 % CI: 95.26-98.87), and diagnostic accuracy of 93.05 % (95 % CI: 90.61-94.9) by index kit. The performance of the index kit was consistent across different durations of fever, with the highest sensitivity observed in samples from patients with a fever lasting >7 days.

Conclusions: The J. Mitra Scrub typhus IgM Microlisa kit demonstrates high sensitivity, specificity, and diagnostic accuracy, comparable with that of the reference standard. It offers a reliable alternative for the routine laboratory diagnosis of scrub typhus, facilitating timely diagnosis and management.

Fungal and mycobacterial cultures are routinely sent on orthopedic operative specimens, but their impact on clinical care is not well established. Processing fungal and mycobacterial cultures is time and labor intensive, associated with significant healthcare costs, and may yield false-positive results due to contamination. The objective of this study was to determine the utility and diagnostic yield of fungal and mycobacterial cultures from orthopedic operative specimens. Patients undergoing surgery by the orthopedic or podiatry service that had operative specimens sent for fungal and mycobacterial cultures from January through December 2022 were included. Fungal and mycobacterial cultures were ordered on a total of 1109 operative specimens in 428 patients. A mean of 2 (standard deviation 1.3) specimens was collected per operative procedure. A microorganism was identified in 34 (3.1 %) of fungal cultures and in no mycobacterial cultures. Fungal microorganisms included yeast (89.7 %), mold (7.7 %) and dermatophytes (2.6 %). Results of the fungal cultures alone led to a change in management for 4 (0.9 %) patients. Results of the mycobacterial cultures did not lead to a change in management for any patient. The diagnostic yield of fungal and mycobacterial cultures on operative specimens in orthopedic and podiatry surgery is low and rarely results in a change in therapeutic management. Standardized approaches for targeted rather than routine use of these cultures should be developed to improve stewardship of laboratory resources.

Background: Pulmonary tuberculosis (PTB) remains a major global public health challenge, with diagnostic delays being a key factor contributing to its high morbidity and mortality. Growing evidence suggests that neutrophil extracellular traps (NETs) are closely associated with PTB pathogenesis. This study focuses on elucidating the role of NETs in PTB and identifying critical diagnostic methods and potential biomarkers.

Methods: Weighted gene co-expression network analysis (WGCNA) was employed to identify the three modules most strongly correlated with NETs. Differentially expressed genes (DEGs) from GSE39939 dataset were intersected with module genes to obtain NET-related DEGs. Four machine learning algorithms (LASSO, random forest, RFE, and Boruta) were applied to select feature genes and develop a PTB diagnostic model. Model's performance was evaluated using support vector machine (SVM)-based receiver operating characteristic (ROC) and precision-recall (PR) curves, with validation in the GSE39940 dataset. The optimal algorithm was selected to refine feature genes and construct a miRNA-gene regulatory network.

Results: ROC and PR curve analyses revealed that RFE and Boruta algorithms exhibited superior diagnostic efficacy in distinguishing active PTB from latent TB infection (LTBI). Further analysis identified five overlapping high-ranking feature genes (GPR84, SIGLEC10, CCR2, TMEM167A, and GYG1) between the RFE and Boruta algorithms. hsa-miR-1264, hsa-miR-664a-3p, hsa-miR-548e-5p, hsa-miR-4775, and hsa-miR-5056 were predicted to potentially target these genes.

Conclusion: RFE algorithm achieves high diagnostic accuracy for PTB and identifies five potential biomarkers (GPR84, SIGLEC10, CCR2, TMEM167A, and GYG1). These findings may provide valuable tools for PTB diagnosis and treatment.