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灾难性选择:硬币的另一面。

Catastrophic selection: the other side of the coin.

机构信息

SaBio, Instituto de Investigación en Recursos Cinegéticos (IREC), Consejo Superior de Investigaciones Científicas (CSIC), Universidad de Castilla-La Mancha (UCLM)-Junta de Comunidades de Castilla-La Mancha (JCCM), Ciudad Real, Spain.

Department of Veterinary Pathobiology, Center for Veterinary Health Sciences, OK State University, Stillwater, OK, USA.

出版信息

Ann Med. 2024 Dec;56(1):2391014. doi: 10.1080/07853890.2024.2391014. Epub 2024 Aug 14.

DOI:10.1080/07853890.2024.2391014
PMID:39140291
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11328594/
Abstract

Recently, a machine learning molecular de-extinction paleoproteomic approach was used to recover inactivated antimicrobial peptides to overcome the challenges posed by antibiotic-resistant pathogens. The authors showed the possibility of identifying lost molecules with antibacterial capacity, but the other side of the coin associated with catastrophic selection should be considered for the development of new pharmaceuticals.

摘要

最近,一种机器学习的分子灭绝古蛋白组学方法被用于恢复失活的抗菌肽,以克服抗生素耐药病原体带来的挑战。作者展示了识别具有抗菌能力的丢失分子的可能性,但为了开发新的药物,也应该考虑与灾难性选择相关的另一面。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5dac/11328594/6e2cc4197db4/IANN_A_2391014_F0001_C.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5dac/11328594/6e2cc4197db4/IANN_A_2391014_F0001_C.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5dac/11328594/6e2cc4197db4/IANN_A_2391014_F0001_C.jpg

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1
Catastrophic selection: the other side of the coin.灾难性选择:硬币的另一面。
Ann Med. 2024 Dec;56(1):2391014. doi: 10.1080/07853890.2024.2391014. Epub 2024 Aug 14.
2
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本文引用的文献

1
Deep-learning-enabled antibiotic discovery through molecular de-extinction.通过分子复活实现基于深度学习的抗生素发现。
Nat Biomed Eng. 2024 Jul;8(7):854-871. doi: 10.1038/s41551-024-01201-x. Epub 2024 Jun 11.
2
Machine learning in infectious diseases: potential applications and limitations.机器学习在传染病学中的应用:潜在的应用和局限性。
Ann Med. 2024 Dec;56(1):2362869. doi: 10.1080/07853890.2024.2362869. Epub 2024 Jun 10.
3
Explainable artificial intelligence and machine learning: novel approaches to face infectious diseases challenges.
可解释人工智能和机器学习:应对面部传染病挑战的新方法。
Ann Med. 2023;55(2):2286336. doi: 10.1080/07853890.2023.2286336. Epub 2023 Nov 27.
4
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。
Cell Host Microbe. 2023 Aug 9;31(8):1260-1274.e6. doi: 10.1016/j.chom.2023.07.001. Epub 2023 Jul 28.
5
AI search of Neanderthal proteins resurrects 'extinct' antibiotics.对尼安德特人蛋白质的人工智能搜索复活了“已灭绝”的抗生素。
Nature. 2023 Jul 28. doi: 10.1038/d41586-023-02403-0.
6
Allergic reactions to tick saliva components in zebrafish model.对斑马鱼模型中蜱唾液成分的过敏反应。
Parasit Vectors. 2023 Jul 19;16(1):242. doi: 10.1186/s13071-023-05874-2.
7
Resurrection of endogenous retroviruses during aging reinforces senescence.衰老过程中内源性逆转录病毒的复活增强了衰老。
Cell. 2023 Jan 19;186(2):287-304.e26. doi: 10.1016/j.cell.2022.12.017. Epub 2023 Jan 6.
8
Current and Future Strategies for the Diagnosis and Treatment of the Alpha-Gal Syndrome (AGS).α-半乳糖综合征(AGS)诊断与治疗的当前及未来策略
J Asthma Allergy. 2022 Jul 18;15:957-970. doi: 10.2147/JAA.S265660. eCollection 2022.
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Paleoproteomics.古蛋白组学。
Chem Rev. 2022 Aug 24;122(16):13401-13446. doi: 10.1021/acs.chemrev.1c00703. Epub 2022 Jul 15.
10
Tick Intrastadial Feeding and Its Role on IgE Production in the Murine Model of Alpha-gal Syndrome: The Tick "Transmission" Hypothesis.蜱虫在阿尔法-gal综合征小鼠模型中的阶段内取食及其对IgE产生的作用:蜱虫“传播”假说
Front Immunol. 2022 Mar 4;13:844262. doi: 10.3389/fimmu.2022.844262. eCollection 2022.