Deciphering the microbial landscape of lower respiratory tract infections: insights from metagenomics and machine learning.

Journal: Frontiers in cellular and infection microbiology
PMID: 38846353

Abstract

BACKGROUND: Lower respiratory tract infections represent prevalent ailments. Nonetheless, current comprehension of the microbial ecosystems within the lower respiratory tract remains incomplete and necessitates further comprehensive assessment. Leveraging the advancements in metagenomic next-generation sequencing (mNGS) technology alongside the emergence of machine learning, it is now viable to compare the attributes of lower respiratory tract microbial communities among patients across diverse age groups, diseases, and infection types.

Authors

  • Jiahuan Li
    Clinical Medicine Department, North Sichuan Medical College, Nanchong, China.
  • Anying Xiong
    Laboratory of Allergy and Precision Medicine, Chengdu Institute of Respiratory Health, the Third People's Hospital of Chengdu, Affiliated Hospital of Southwest Jiaotong University, Chengdu, China.
  • Junyi Wang
  • Xue Wu
    School of Civil Engineering, Southeast University, Nanjing 210096, China.
  • Lingling Bai
    Laboratory of Allergy and Precision Medicine, Chengdu Institute of Respiratory Health, the Third People's Hospital of Chengdu, Affiliated Hospital of Southwest Jiaotong University, Chengdu, China.
  • Lei Zhang
    Division of Gastroenterology, Union Hospital, Tongji Medical College Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Xiang He
    Department of Dermatology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
  • Guoping Li
    Clinical Medicine Department, North Sichuan Medical College, Nanchong, China.

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