Ocular oncology and pathology · 2026
Structured Clinicopathologic Data as a Prerequisite for Generalizable AI in Ocular Oncology.
Письмо в редакциюНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
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Wang Jun-Mei, Pan Yu-Ying, Hong Jian-Chen, Jiang Zai-Jie, Zhang Shi-Ying, Fan Rui-Jie, Yang Bi-Hui, Wang Zhi-Qiang, Zhu Ji-Ting, Yao Xiang-Ping
Аннотация
PURPOSE: This study leveraged CSF metagenomic next-generation sequencing (mNGS) to bridge this knowledge gap and elucidate the microbiota spectrum of CNS infections.
PATIENTS AND METHODS: We retrospectively analyzed CSF mNGS reports and clinical data from 264 patients with suspected CNS infections, who were enrolled from September 2019 to November 2023.
RESULTS: According to diagnostic criteria, 145 patients were diagnosed with CNS infections, including bacterial (27 cases, 18.6%), Mycobacterium tuberculosis (30, 20.7%), fungal (23, 15.9%), and viral (65, 44.8%) infections. The mNGS positive detection rate was 46.2% (67/145), with significant differences among groups (p < 0.001). A total of 22 pathogens were identified, most commonly Cryptococcus neoformans (16, 23.9%), Mycobacterium tuberculosis (10, 14.9%), and Epstein-Barr virus (9, 13.4%). The most frequent background microorganisms detected by mNGS were Cutibacterium acnes (58.6%), Moraxella osloensis (29.0%), and Malassezia restricta (26.2%).
CONCLUSION: High-throughput sequencing using mNGS revealed the microbial compositions in CSF samples from patients with CNS infections. This approach may enhance our understanding of pathogens and assist clinicians in making effective therapeutic decisions.
PATIENTS AND METHODS: We retrospectively analyzed CSF mNGS reports and clinical data from 264 patients with suspected CNS infections, who were enrolled from September 2019 to November 2023.
RESULTS: According to diagnostic criteria, 145 patients were diagnosed with CNS infections, including bacterial (27 cases, 18.6%), Mycobacterium tuberculosis (30, 20.7%), fungal (23, 15.9%), and viral (65, 44.8%) infections. The mNGS positive detection rate was 46.2% (67/145), with significant differences among groups (p < 0.001). A total of 22 pathogens were identified, most commonly Cryptococcus neoformans (16, 23.9%), Mycobacterium tuberculosis (10, 14.9%), and Epstein-Barr virus (9, 13.4%). The most frequent background microorganisms detected by mNGS were Cutibacterium acnes (58.6%), Moraxella osloensis (29.0%), and Malassezia restricta (26.2%).
CONCLUSION: High-throughput sequencing using mNGS revealed the microbial compositions in CSF samples from patients with CNS infections. This approach may enhance our understanding of pathogens and assist clinicians in making effective therapeutic decisions.