智能噬菌体:利用人工智能解决假体关节感染。

IF 4.6 2区 医学 Q1 INFECTIOUS DISEASES
Nicita Mehta, Andrew T Nguyen, Edward K Rodriguez, Jason Young
{"title":"智能噬菌体:利用人工智能解决假体关节感染。","authors":"Nicita Mehta, Andrew T Nguyen, Edward K Rodriguez, Jason Young","doi":"10.3390/antibiotics14090949","DOIUrl":null,"url":null,"abstract":"<p><p>Traditional antibiotic therapy has encountered significant challenges for clinical treatment of infections for multiple reasons, including antimicrobial resistance (AMR) and poor efficacy against biofilms, demanding research into alternative therapeutic agents. Because of their unique antimicrobial mechanisms as well as their target specificity, diversity, exponential self-amplification, and anti-biofilm activity, combined with recent advances in genomics and synthetic biology, bacteriophages have attracted increased interest as potential alternatives or therapeutic adjuncts to antibiotics. However, obstacles such as phage-host specificity, bacterial resistance, and the selection of optimal phages, amongst other factors, impede clinical adoption of phage therapy. Here, machine learning (ML) and artificial intelligence (AI) tools have the opportunity to revolutionize phage therapy by enhancing scalability, efficiency and precision of these therapies. This article highlights potential key applications of ML/AI in the study, development and deployment of phage therapy.</p>","PeriodicalId":54246,"journal":{"name":"Antibiotics-Basel","volume":"14 9","pages":""},"PeriodicalIF":4.6000,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12466338/pdf/","citationCount":"0","resultStr":"{\"title\":\"Smart Phages: Leveraging Artificial Intelligence to Tackle Prosthetic Joint Infections.\",\"authors\":\"Nicita Mehta, Andrew T Nguyen, Edward K Rodriguez, Jason Young\",\"doi\":\"10.3390/antibiotics14090949\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Traditional antibiotic therapy has encountered significant challenges for clinical treatment of infections for multiple reasons, including antimicrobial resistance (AMR) and poor efficacy against biofilms, demanding research into alternative therapeutic agents. Because of their unique antimicrobial mechanisms as well as their target specificity, diversity, exponential self-amplification, and anti-biofilm activity, combined with recent advances in genomics and synthetic biology, bacteriophages have attracted increased interest as potential alternatives or therapeutic adjuncts to antibiotics. However, obstacles such as phage-host specificity, bacterial resistance, and the selection of optimal phages, amongst other factors, impede clinical adoption of phage therapy. Here, machine learning (ML) and artificial intelligence (AI) tools have the opportunity to revolutionize phage therapy by enhancing scalability, efficiency and precision of these therapies. This article highlights potential key applications of ML/AI in the study, development and deployment of phage therapy.</p>\",\"PeriodicalId\":54246,\"journal\":{\"name\":\"Antibiotics-Basel\",\"volume\":\"14 9\",\"pages\":\"\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2025-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12466338/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Antibiotics-Basel\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3390/antibiotics14090949\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"INFECTIOUS DISEASES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Antibiotics-Basel","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3390/antibiotics14090949","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"INFECTIOUS DISEASES","Score":null,"Total":0}
引用次数: 0

摘要

由于多种原因,传统抗生素治疗在感染的临床治疗中遇到了重大挑战,包括抗菌素耐药性(AMR)和对生物膜的疗效差,需要研究替代治疗药物。由于其独特的抗菌机制以及其靶点特异性、多样性、指数自我扩增和抗生物膜活性,结合基因组学和合成生物学的最新进展,噬菌体作为抗生素的潜在替代品或治疗辅助物引起了越来越多的兴趣。然而,诸如噬菌体-宿主特异性、细菌耐药性和最佳噬菌体的选择等因素阻碍了噬菌体治疗的临床应用。在这里,机器学习(ML)和人工智能(AI)工具有机会通过提高这些疗法的可扩展性、效率和准确性来彻底改变噬菌体疗法。本文重点介绍了ML/AI在噬菌体治疗的研究、开发和部署中的潜在关键应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Smart Phages: Leveraging Artificial Intelligence to Tackle Prosthetic Joint Infections.

Traditional antibiotic therapy has encountered significant challenges for clinical treatment of infections for multiple reasons, including antimicrobial resistance (AMR) and poor efficacy against biofilms, demanding research into alternative therapeutic agents. Because of their unique antimicrobial mechanisms as well as their target specificity, diversity, exponential self-amplification, and anti-biofilm activity, combined with recent advances in genomics and synthetic biology, bacteriophages have attracted increased interest as potential alternatives or therapeutic adjuncts to antibiotics. However, obstacles such as phage-host specificity, bacterial resistance, and the selection of optimal phages, amongst other factors, impede clinical adoption of phage therapy. Here, machine learning (ML) and artificial intelligence (AI) tools have the opportunity to revolutionize phage therapy by enhancing scalability, efficiency and precision of these therapies. This article highlights potential key applications of ML/AI in the study, development and deployment of phage therapy.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Antibiotics-Basel
Antibiotics-Basel Pharmacology, Toxicology and Pharmaceutics-General Pharmacology, Toxicology and Pharmaceutics
CiteScore
7.30
自引率
14.60%
发文量
1547
审稿时长
11 weeks
期刊介绍: Antibiotics (ISSN 2079-6382) is an open access, peer reviewed journal on all aspects of antibiotics. Antibiotics is a multi-disciplinary journal encompassing the general fields of biochemistry, chemistry, genetics, microbiology and pharmacology. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. Therefore, there is no restriction on the length of papers.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信