Artificial Intelligence-Driven Analysis of Antimicrobial-Resistant and Biofilm-Forming Pathogens on Biotic and Abiotic Surfaces.

IF 4.3 2区 医学 Q1 INFECTIOUS DISEASES
Akanksha Mishra, Nazia Tabassum, Ashish Aggarwal, Young-Mog Kim, Fazlurrahman Khan
{"title":"Artificial Intelligence-Driven Analysis of Antimicrobial-Resistant and Biofilm-Forming Pathogens on Biotic and Abiotic Surfaces.","authors":"Akanksha Mishra, Nazia Tabassum, Ashish Aggarwal, Young-Mog Kim, Fazlurrahman Khan","doi":"10.3390/antibiotics13080788","DOIUrl":null,"url":null,"abstract":"<p><p>The growing threat of antimicrobial-resistant (AMR) pathogens to human health worldwide emphasizes the need for more effective infection control strategies. Bacterial and fungal biofilms pose a major challenge in treating AMR pathogen infections. Biofilms are formed by pathogenic microbes encased in extracellular polymeric substances to confer protection from antimicrobials and the host immune system. Biofilms also promote the growth of antibiotic-resistant mutants and latent persister cells and thus complicate therapeutic approaches. Biofilms are ubiquitous and cause serious health risks due to their ability to colonize various surfaces, including human tissues, medical devices, and food-processing equipment. Detection and characterization of biofilms are crucial for prompt intervention and infection control. To this end, traditional approaches are often effective, yet they fail to identify the microbial species inside biofilms. Recent advances in artificial intelligence (AI) have provided new avenues to improve biofilm identification. Machine-learning algorithms and image-processing techniques have shown promise for the accurate and efficient detection of biofilm-forming microorganisms on biotic and abiotic surfaces. These advancements have the potential to transform biofilm research and clinical practice by allowing faster diagnosis and more tailored therapy. This comprehensive review focuses on the application of AI techniques for the identification of biofilm-forming pathogens in various industries, including healthcare, food safety, and agriculture. The review discusses the existing approaches, challenges, and potential applications of AI in biofilm research, with a particular focus on the role of AI in improving diagnostic capacities and guiding preventative actions. The synthesis of the current knowledge and future directions, as described in this review, will guide future research and development efforts in combating biofilm-associated infections.</p>","PeriodicalId":54246,"journal":{"name":"Antibiotics-Basel","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11351874/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Antibiotics-Basel","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3390/antibiotics13080788","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

Abstract

The growing threat of antimicrobial-resistant (AMR) pathogens to human health worldwide emphasizes the need for more effective infection control strategies. Bacterial and fungal biofilms pose a major challenge in treating AMR pathogen infections. Biofilms are formed by pathogenic microbes encased in extracellular polymeric substances to confer protection from antimicrobials and the host immune system. Biofilms also promote the growth of antibiotic-resistant mutants and latent persister cells and thus complicate therapeutic approaches. Biofilms are ubiquitous and cause serious health risks due to their ability to colonize various surfaces, including human tissues, medical devices, and food-processing equipment. Detection and characterization of biofilms are crucial for prompt intervention and infection control. To this end, traditional approaches are often effective, yet they fail to identify the microbial species inside biofilms. Recent advances in artificial intelligence (AI) have provided new avenues to improve biofilm identification. Machine-learning algorithms and image-processing techniques have shown promise for the accurate and efficient detection of biofilm-forming microorganisms on biotic and abiotic surfaces. These advancements have the potential to transform biofilm research and clinical practice by allowing faster diagnosis and more tailored therapy. This comprehensive review focuses on the application of AI techniques for the identification of biofilm-forming pathogens in various industries, including healthcare, food safety, and agriculture. The review discusses the existing approaches, challenges, and potential applications of AI in biofilm research, with a particular focus on the role of AI in improving diagnostic capacities and guiding preventative actions. The synthesis of the current knowledge and future directions, as described in this review, will guide future research and development efforts in combating biofilm-associated infections.

人工智能驱动的生物表面和非生物表面抗菌病原体和生物膜形成分析。
抗微生物(AMR)病原体对全球人类健康的威胁与日俱增,这凸显了采取更有效感染控制策略的必要性。细菌和真菌生物膜是治疗 AMR 病原体感染的一大挑战。生物膜是由病原微生物包裹在细胞外高分子物质中形成的,以抵御抗菌剂和宿主免疫系统的侵袭。生物膜还能促进抗生素耐药突变体和潜伏持久细胞的生长,从而使治疗方法复杂化。生物膜无处不在,由于它们能在人体组织、医疗器械和食品加工设备等各种表面定殖,因此对健康造成严重危害。生物膜的检测和特征描述对于及时干预和感染控制至关重要。为此,传统方法通常很有效,但却无法识别生物膜内的微生物种类。人工智能(AI)的最新进展为改善生物膜识别提供了新途径。机器学习算法和图像处理技术已显示出准确、高效地检测生物和非生物表面上形成生物膜的微生物的前景。这些进步有可能改变生物膜研究和临床实践,从而实现更快的诊断和更有针对性的治疗。本综述重点介绍了人工智能技术在医疗保健、食品安全和农业等各行各业中用于识别生物膜形成病原体的应用。综述讨论了人工智能在生物膜研究中的现有方法、挑战和潜在应用,尤其关注人工智能在提高诊断能力和指导预防行动方面的作用。本综述对当前知识和未来方向进行了总结,将为今后抗击生物膜相关感染的研发工作提供指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
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:481959085
Book学术官方微信