five

Data_Sheet_1_Multiplex detection of meningitis and encephalitis pathogens: A study from laboratory to clinic.docx

收藏
NIAID Data Ecosystem2026-03-14 收录
下载链接:
https://figshare.com/articles/dataset/Data_Sheet_1_Multiplex_detection_of_meningitis_and_encephalitis_pathogens_A_study_from_laboratory_to_clinic_docx/21738746
下载链接
链接失效反馈
官方服务:
资源简介:
IntroductionInfectious meningitis and encephalitis (ME) are life-threatening conditions are caused by various pathogens. Conventional laboratory tests with low sensitivity and specificity cannot help with early diagnosis. MethodsA prospective study using the novel multiplex PCR detection for 18 pathogens of ME (MME-18) was conducted to investigate the clinical utilization and the epidemiology characteristics of ME in southwestern China. Patients with suspected intracranial infection were recruited between May and October 2019 at West China Hospital of Sichuan University. The MME-18 was used to detect cerebrospinal fluid, and conventional experiments including cryptococcal capsular antigen detection, GeneXpert, real-time PCR, and clinical feedback were used to verify the result of MME-18. ResultsAmong 581 tested patients, 139 eligible individuals were enrolled in the study. Among them, Mycobacterium tuberculosis was the most common pathogen in mono-infection. Viruses and Cryptococcus neoformans were also frequently detected. Of 139 infected patients, 12 cases were diagnosed by MME-18 only, 57 patients by conventional testing only, and 70 cases by both comparator tests and MME-18. There were 96.3% (79/82) diagnoses made by MME-18 had a favorable outcome, and two of twelve diagnoses, made solely by MME-18, had a likely unclear clinical significance. DiscussionThe MME-18 showed satisfactory consistency with expert clinical consensus for patients presenting with ME. Combined with conventional testing and clinical suspicion, MME-18 may help clinicians with the early identification of pathogens.
创建时间:
2022-12-16
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作