Medical Biotechnology Laboratory, Dr. B. R. Ambedkar Center for Biomedical Research, University of Delhi, New Delhi, India.
ICAR-National Institute of High Security Animal Diseases, Bhopal, Madhya Pradesh, India.
Int J Infect Dis. 2024 Oct;147:107200. doi: 10.1016/j.ijid.2024.107200. Epub 2024 Aug 6.
Mycoplasma pneumoniae (M. pneumoniae) continues to pose a significant disease burden on global public health as a respiratory pathogen. The antimicrobial resistance among M. pneumoniae strains has complicated the outbreak control efforts, emphasizing the need for robust surveillance systems and effective antimicrobial stewardship programs.
This review comprehensively investigates studies stemming from previous outbreaks to emphasize the multifaceted nature of M. pneumoniae infections, encompassing epidemiological dynamics, diagnostic innovations, antibiotic resistance, and therapeutic challenges.
We explored the spectrum of clinical manifestations associated with M. pneumoniae infections, emphasizing the continuum of disease severity and the challenges in gradating it accurately. Artificial intelligence and machine learning have emerged as promising tools in M. pneumoniae diagnostics, offering enhanced accuracy and efficiency in identifying infections. However, their integration into clinical practice presents hurdles that need to be addressed. Further, we elucidate the pivotal role of pharmacological interventions in controlling and treating M. pneumoniae infections as the efficacy of existing therapies is jeopardized by evolving resistance mechanisms.
Lessons learned from previous outbreaks underscore the importance of adaptive treatment strategies and proactive management approaches. Addressing these complexities demands a holistic approach integrating advanced technologies, genomic surveillance, and adaptive clinical strategies to effectively combat this pathogen.
肺炎支原体(M. pneumoniae)作为一种呼吸道病原体,继续对全球公共卫生造成重大疾病负担。M. pneumoniae 菌株的抗生素耐药性使疫情控制工作变得复杂,强调需要有强大的监测系统和有效的抗生素管理计划。
本综述全面调查了以往疫情的研究,强调了 M. pneumoniae 感染的多面性,包括流行病学动态、诊断创新、抗生素耐药性和治疗挑战。
我们探讨了与 M. pneumoniae 感染相关的临床表现谱,强调了疾病严重程度的连续性以及准确分级的挑战。人工智能和机器学习已成为 M. pneumoniae 诊断中很有前途的工具,在识别感染方面提供了更高的准确性和效率。然而,将它们整合到临床实践中存在需要解决的障碍。此外,我们阐明了药理干预在控制和治疗 M. pneumoniae 感染中的关键作用,因为现有治疗方法的疗效受到不断演变的耐药机制的威胁。
从以往疫情中吸取的教训强调了适应性治疗策略和主动管理方法的重要性。应对这些复杂性需要采用综合方法,整合先进技术、基因组监测和适应性临床策略,以有效对抗这种病原体。