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抗生物膜方法作为一种治疗感染的新范例。

Antibiofilm approaches as a new paradigm for treating infections.

机构信息

Université de Reims Champagne-Ardenne, Biomatériaux et Inflammation en Site Osseux, BIOS EA 4691, SFR Cap Santé, 51097 Reims, France.

Université de Reims Champagne-Ardenne, UFR Pharmacie, Service de Microbiologie, 51097 Reims, France.

出版信息

Prog Biomed Eng (Bristol). 2024 Apr;6(2). doi: 10.1088/2516-1091/ad1cd6. Epub 2024 Feb 9.

DOI:10.1088/2516-1091/ad1cd6
PMID:39506977
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11540418/
Abstract

The lack of effective antibiotics for drug-resistant infections has led the World Health Organization to declare antibiotic resistance a global priority. Most bacterial infections are caused by microbes growing in structured communities called biofilms. Bacteria growing in biofilms are less susceptible to antibiotics than their planktonic counterparts. Despite their significant clinical implications, bacterial biofilms have not received the attention they warrant, with no approved antibiotics specifically designed for their eradication. In this paper, we aim to shed light on recent advancements in antibiofilm strategies that offer compelling alternatives to traditional antibiotics. Additionally, we will briefly explore the potential synergy between computational approaches, including the emerging field of artificial intelligence, and the accelerated design and discovery of novel antibiofilm molecules in the years ahead.

摘要

由于缺乏治疗耐药感染的有效抗生素,世界卫生组织已将抗生素耐药性列为全球重点事项。大多数细菌性感染是由微生物在称为生物膜的结构化群落中生长引起的。与浮游状态的细菌相比,生长在生物膜中的细菌对抗生素的敏感性较低。尽管具有重要的临床意义,但细菌生物膜并未得到应有的重视,目前尚无专门用于消除它们的批准抗生素。本文旨在介绍抗生物膜策略的最新进展,这些策略为传统抗生素提供了极具吸引力的替代品。此外,我们还将简要探讨计算方法(包括新兴的人工智能领域)与新型抗生物膜分子的快速设计和发现之间的潜在协同作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a52/11540418/0b4b55d0c411/nihms-2008718-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a52/11540418/e13091d69532/nihms-2008718-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a52/11540418/7a61f44df8b1/nihms-2008718-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a52/11540418/443fdd3ddebe/nihms-2008718-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a52/11540418/0b4b55d0c411/nihms-2008718-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a52/11540418/e13091d69532/nihms-2008718-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a52/11540418/7a61f44df8b1/nihms-2008718-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a52/11540418/443fdd3ddebe/nihms-2008718-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5a52/11540418/0b4b55d0c411/nihms-2008718-f0004.jpg

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Molecular de-extinction of ancient antimicrobial peptides enabled by machine learning.机器学习助力古老抗菌肽的分子复活。
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