Hu Junan, Yu Wei, Cui Jiating, Zhang Lun, Yu Wangfang
Department of Neurosurgery, Beilun District People's Hospital, Ningbo, 315800, Zhejiang, China.
Department of Radiology, Beilun District People's Hospital, Ningbo, 315800, Zhejiang, China.
Neurol Sci. 2025 Jun 2. doi: 10.1007/s10072-025-08279-4.
Postoperative central nervous system infections (PCNSIs), including meningitis, cerebral abscesses, and implant-associated infections, represent critical complications following neurosurgical procedures. These infections pose significant risks to patient outcomes due to delayed diagnosis, escalating antimicrobial resistance, and limited therapeutic efficacy. Conventional diagnostic approaches, such as cerebrospinal fluid (CSF) analysis, microbial cultures, and neuroimaging, exhibit notable limitations in sensitivity, specificity, and rapidity. This review highlights transformative technologies reshaping PCNSI diagnostics, including molecular assays (e.g., quantitative PCR, digital droplet PCR), metagenomic next-generation sequencing (mNGS), CRISPR-based pathogen detection platforms, metabolomics, and advanced molecular imaging modalities. Furthermore, we address translational challenges in clinical adoption, including cost barriers, standardization gaps, and the need for interdisciplinary collaboration. Emerging artificial intelligence (AI)-driven strategies are proposed to optimize pathogen identification, predict antimicrobial resistance profiles, and tailor personalized therapeutic regimens.
术后中枢神经系统感染（PCNSIs），包括脑膜炎、脑脓肿和植入物相关感染，是神经外科手术后的严重并发症。由于诊断延迟、抗菌药物耐药性不断增加以及治疗效果有限，这些感染对患者的预后构成了重大风险。传统的诊断方法，如脑脊液（CSF）分析、微生物培养和神经影像学检查，在敏感性、特异性和快速性方面存在显著局限性。本综述重点介绍了重塑PCNSI诊断的变革性技术，包括分子检测（如定量PCR、数字液滴PCR）、宏基因组下一代测序（mNGS）、基于CRISPR的病原体检测平台、代谢组学和先进的分子成像模式。此外，我们还讨论了临床应用中的转化挑战，包括成本障碍、标准化差距以及跨学科合作的必要性。我们提出了新兴的人工智能（AI）驱动策略，以优化病原体识别、预测抗菌药物耐药性谱并制定个性化治疗方案。
