对抗抗菌素耐药性的战争

S. T. Akinwotu, Oluwaseun Fapohunda
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引用次数: 0

摘要

抗微生物药物耐药性是一种全球性威胁,需要立即作出反应。过度使用和误用抗菌素已导致多种耐药微生物菌株的发展。随着这些药物消费率的增加,耐药率也在增加,导致医疗费用高企,死亡率上升。微生物耐药的生化和遗传机制是对抗抗生素耐药性的关键因素。我们检索和研究了来自PubMed、Scopus、b谷歌Scholar、ResearchGate等知名期刊/档案的最新文章,发现很多关于药物在治疗抗菌素耐药性方面的重要性的研究,近十年来,研究人员一直在深入研究可以解决多重耐药性的重要植物药,并提出了几种观点。研究人员认为,植物成分可能是解决抗菌素耐药性的潜在药物,几乎没有副作用,而且它们相对便宜,对低收入人群来说很容易获得。然而,未来在很大程度上将是机器驱动的,关于能够充分解决这种抗菌素耐药性威胁的新兴技术的报道很少。在这里,我们简要地讨论了这些有前途的技术如何用于对抗微生物对抗生素的耐药性;疫苗技术的使用、人工智能与机器学习算法的使用、纳米系统的使用以及CRISPR-cas技术的使用。
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War against antimicrobial resistance
Resistance to antimicrobials is a global threat that demands immediate response. The excessive use and misuse of antimicrobials has led to the development of multidrug resistant strains of microorganisms. As the consumption rate of these drugs increases, the resistance rate also increases, resulting in high cost of medical treatment and increased mortality rate. To war against antimicrobial resistance, the biochemical and genetic mechanism of resistance in microorganisms is a key factor to be considered. Recent articles from reputable journals/archives including PubMed, Scopus, Google Scholar, ResearchGate etc were retrieved and studied and we discovered that much have been researched on the importance of drugs in the treatment of antimicrobial resistance and in recent decade researchers have been delving into important botanicals that could address multidrug resistance with several opinions. Researchers have opinionated that phytoconstituents could be potential drugs that would address antimicrobial resistance with little or no side effect and they are relatively cheap and readily available to low-income people. However, the future is largely going to be machine driven and little have been reported on the emerging technologies that could adequately tackle this menace of antimicrobial resistance. Here we succinctly discussed how some of these promising technologies could be employed in combating the resistance of microorganism to antibiotics; the use of vaccine technologies, the use of Artificial Intelligence coupled with machine learning algorithms, the use of nanosystems and the use of CRISPR-cas technology.
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