Identification of <i>MORN3</i> and <i>LLGL2</i> as novel diagnostic biomarkers for latent tuberculosis infection using machine learning strategies and experimental verification-Reference-Cited by-同舟云学术

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Identification of MORN3 and LLGL2 as novel diagnostic biomarkers for latent tuberculosis infection using machine learning strategies and experimental verification

Published:2024-07-25 Issue:1 Volume:56 Page:
ISSN:0785-3890
Container-title:Annals of Medicine
language:en
Short-container-title:Annals of Medicine

Author:

Xie Longxiang¹²^ORCID,Zhu Gaoya²,Long Sibo³,Wang Mengna²,Cheng Xinxin²,Dong Yuzhe²,Wang Chaoyang¹,Wang Guirong³

Affiliation:

1. Department of General Surgery, Huaihe Hospital of Henan University, Henan University, Kaifeng, Henan, China

2. School of Basic Medical Sciences, Henan University, Kaifeng, Henan, China

3. Department of Clinical Laboratory, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Institute, Beijing, China

Funder

National Natural Science Foundation of China

National College Student Innovation and Entrepreneurship Training Program

Beijing Public Health Experts Project

Beijing Tongzhou Municipal Science & Technology commission

Tongzhou Yunhe Project under Grant

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/07853890.2024.2380797

Reference44 articles.

1. WHO's Global Tuberculosis Report 2022

2. Management of latent Mycobacterium tuberculosis infection: WHO guidelines for low tuberculosis burden countries

3. The Global Burden of Latent Tuberculosis Infection: A Re-estimation Using Mathematical Modelling

4. Latent Mycobacterium tuberculosis Infection

5. Relationship between DNA Methylation Profiles and Active Tuberculosis Development from Latent Infection: a Pilot Study in Nested Case-Control Design

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