{
  "abstract": "Objectives: Early assessment of treatment response is critical for optimizing the management of multidrug-resistant/rifampicin-resistant tuberculosis (MDR/RR-TB). We aimed to establish a molecular bacterial load assay (MBLA) and evaluate early bacterial load dynamics for predicting 6-month treatment response. Methods: A multicenter prospective cohort study enrolled patients with MDR/RR-TB in China. MBLA was established and validated against conventional culture methods. Generalized linear mixed models (GLMM) and artificial neural network (ANN) models were constructed by integrating 14-day and 2-month bacterial load changes, and evaluated using area under the curve (AUC), accuracy, sensitivity, specificity, and F1 score. Results: MBLA enabled quantification within 24 h and strongly correlated with conventional culture (r = 0.792, −0.790; both P < 0.001). Patients with 6-month sputum culture conversion showed a rapid decline in sputum bacterial load, especially within the first 14 days after treatment initiation. Sputum bacterial load changes over the first 14 days of treatment yielded acceptable prognostic performance for predicting 6-month treatment response, albeit inferior to those obtained at 2 months. The ANN model outperformed the GLMM, with testing set AUCs of 0.91 (14-day) and 0.98 (2-month). Conclusions: MBLA enables rapid quantification of viable sputum bacterial load. The ANN model incorporating 14-day bacterial load dynamics achieves a favorable AUC for predicting 6-month sputum culture conversion. It may only serve as an auxiliary early-warning indicator to supplement routine clinical assessment for MDR/RR-TB patients. Prospective interventional trials and independent external validation are required before any consideration of its use to inform personalized anti-tuberculosis treatment decisions.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing 101149, PR China; School of Public Health (Shenzhen), Shenzhen Key Laboratory of Pathogenic Microbes and Biosafety, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University, Guangdong 518107, PR China",
    "Department of Tuberculosis, Guangzhou Chest Hospital/ Guangzhou Key Laboratory of Tuberculosis Research/ State Key Laboratory of Respiratory Disease, Guangzhou 510095, PR China",
    "Wuhan Pulmonary Hospital, Wuhan Institute for Tuberculosis Control, Wuhan, Hubei Province 430030, PR China",
    "Department of Tuberculosis, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing 101149, PR China",
    "Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing 101149, PR China",
    "Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing 101149, PR China; Corresponding author."
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 13,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Fuzhen Zhang"
    },
    {
      "affs": [
        1
      ],
      "name": "Zilong Yang"
    },
    {
      "affs": [
        1
      ],
      "name": "Min Jiang"
    },
    {
      "affs": [
        2
      ],
      "name": "Xiaomeng Hu"
    },
    {
      "affs": [
        2
      ],
      "name": "Jun Li"
    },
    {
      "affs": [
        3
      ],
      "name": "Kewei Liu"
    },
    {
      "affs": [
        4
      ],
      "name": "Yilin Wang"
    },
    {
      "affs": [
        2
      ],
      "name": "Yan Hu"
    },
    {
      "affs": [
        4
      ],
      "name": "Yu Dong"
    },
    {
      "affs": [
        4
      ],
      "name": "Mingyang Qin"
    },
    {
      "affs": [
        3
      ],
      "name": "Qiang Li"
    },
    {
      "affs": [
        5
      ],
      "name": "Liang Li"
    },
    {
      "affs": [
        5
      ],
      "name": "Yu Pang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4zzwej0s6t61s.bib",
  "cited_by": 0,
  "doi": "10.1016/j.jinf.2026.106838",
  "fulltext_url": "http://www.sciencedirect.com/science/article/pii/S0163445326001647",
  "id": "4zzwej0s6t61s",
  "issns": [
    "1532-2742"
  ],
  "landing_url": "https://doi.org/10.1016/j.jinf.2026.106838",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "RA1-1270"
    ],
    [
      "lcc",
      "RC109-216"
    ],
    [
      "openalex",
      "2725|Tuberculosis Research and Epidemiology"
    ]
  ],
  "openalex": "W7203967854",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/4zzwej0s6t61s.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:43:16Z",
      "native_id": "c9a68ab78cf34c4dbd656dc80c030bc5",
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  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2725|Tuberculosis Research and Epidemiology",
      "subfield": "infectious-disease"
    }
  ],
  "title": "Early sputum bacterial load dynamics predict treatment response in multidrug-resistant/rifampicin-resistant tuberculosis: A multicenter prospective cohort study",
  "title_text": "Early sputum bacterial load dynamics predict treatment response in multidrug-resistant/rifampicin-resistant tuberculosis: A multicenter prospective cohort study",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:43:16Z",
  "url": "https://research.draconitic.com/paper/4zzwej0s6t61s",
  "venue": "Journal of Infection",
  "versions": []
}