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本文引用的文献

1
Artificial intelligence in tuberculosis: a new ally in disease control.人工智能在结核病防治中的应用：疾病控制的新助力
Breathe (Sheff). 2024 Dec 10;20(3):240056. doi: 10.1183/20734735.0056-2024. eCollection 2024 Oct.
2
Host and bacterial lipid metabolism during tuberculosis infections: possibilities to synergise host- and bacteria-directed therapies.结核病感染期间的宿主与细菌脂质代谢：协同宿主导向和细菌导向疗法的可能性
Crit Rev Microbiol. 2025 May;51(3):463-483. doi: 10.1080/1040841X.2024.2370979. Epub 2024 Jun 25.
3
Cryo-electron microscopy-based drug design.基于冷冻电子显微镜的药物设计。
Front Mol Biosci. 2024 Mar 4;11:1342179. doi: 10.3389/fmolb.2024.1342179. eCollection 2024.
4
Discovery of dual-active ethionamide boosters inhibiting the Mycobacterium tuberculosis ESX-1 secretion system.发现双重活性乙硫异烟胺增强剂抑制结核分枝杆菌 ESX-1 分泌系统。
Cell Chem Biol. 2024 Apr 18;31(4):699-711.e6. doi: 10.1016/j.chembiol.2023.12.007. Epub 2024 Jan 4.
5
Economic burden of multidrug-resistant tuberculosis on patients and households: a global systematic review and meta-analysis.耐多药结核病给患者和家庭带来的经济负担：全球系统评价和荟萃分析。
Sci Rep. 2023 Dec 15;13(1):22361. doi: 10.1038/s41598-023-47094-9.
6
The social determinants of national tuberculosis incidence rates in 116 countries: a longitudinal ecological study between 2005-2015.116 个国家结核病发病率的社会决定因素：2005-2015 年期间的纵向生态研究。
BMC Public Health. 2023 Feb 15;23(1):337. doi: 10.1186/s12889-023-15213-w.
7
Anti-virulence therapeutic strategies against bacterial infections: recent advances.针对细菌感染的抗毒力治疗策略：最新进展
Germs. 2022 Jun 30;12(2):262-275. doi: 10.18683/germs.2022.1328. eCollection 2022 Jun.
8
The Application of Artificial Intelligence in the Diagnosis and Drug Resistance Prediction of Pulmonary Tuberculosis.人工智能在肺结核诊断及耐药性预测中的应用
Front Med (Lausanne). 2022 Jul 28;9:935080. doi: 10.3389/fmed.2022.935080. eCollection 2022.
9
The small-molecule SMARt751 reverses resistance to ethionamide in acute and chronic mouse models of tuberculosis.小分子 SMARt751 逆转了急性和慢性结核小鼠模型对乙硫异烟胺的耐药性。
Sci Transl Med. 2022 May 4;14(643):eaaz6280. doi: 10.1126/scitranslmed.aaz6280.
10
Tuberculosis detection from chest x-rays for triaging in a high tuberculosis-burden setting: an evaluation of five artificial intelligence algorithms.从高结核病负担环境中的胸部 X 光片中检测结核病以进行分诊：五种人工智能算法的评估。
Lancet Digit Health. 2021 Sep;3(9):e543-e554. doi: 10.1016/S2589-7500(21)00116-3.

The next generation of drug resistant tuberculosis drug design.

作者信息

Singh Vinayak

机构信息

Holistic Drug Discovery and Development (H3D) Centre, University of Cape Town, Cape Town, South Africa.

Institute of Infectious Disease and Molecular Medicine (IDM), University of Cape Town, Cape Town, South Africa.

出版信息

Future Med Chem. 2025 Feb;17(4):385-387. doi: 10.1080/17568919.2025.2453406. Epub 2025 Jan 15.

DOI:10.1080/17568919.2025.2453406
PMID:39814693
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11834412/
Abstract
摘要