Abstract：
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
摘要：
肺炎支原体（M.肺炎）作为呼吸道病原体继续对全球公共卫生造成重大疾病负担。肺炎支原体菌株的耐药性使疫情控制工作变得复杂，强调需要强大的监测系统和有效的抗菌药物管理计划。这篇综述全面调查了以前爆发的研究，以强调肺炎支原体感染的多面性。包括流行病学动态，诊断创新，抗生素耐药性,和治疗挑战。我们探索了与肺炎支原体感染相关的临床表现谱，强调疾病严重程度的连续性和准确分级的挑战。人工智能和机器学习已经成为肺炎支原体诊断中很有前途的工具，提高识别感染的准确性和效率。然而,他们融入临床实践提出了需要解决的障碍。Further,我们阐明了药物干预在控制和治疗肺炎支原体感染方面的关键作用,因为不断发展的耐药机制会危及现有疗法的疗效.从以前的疫情中吸取的经验教训强调了适应性治疗策略和主动管理方法的重要性。解决这些复杂性需要整合先进技术的整体方法，基因组监测,以及有效对抗这种病原体的适应性临床策略。
