Trend in the Molecular Characterization and Geographic Distribution of Mycobacterium tuberculosis Complex Strains: A Systematic Review
Bibliographic record
Abstract
This systematic review aims to evaluate the global molecular characterization of Mycobacterium tuberculosis complex (MTBC) isolates from 2017 to June 2023, focusing on lineage distribution, drug resistance trends, and diagnostic methods. Following PRISMA guidelines, data from 10 high quality studies comprising 7,848 clinical samples across 10 countries were analyzed, yielding 3,216 confirmed MTBC isolates. Spoligotyping, MIRU-VNTR, and whole genome sequencing (WGS) were used to characterize strains, alongside various drug susceptibility testing (DST) methods. Lineage 4 (Euro-American) was the most widespread globally, especially in sub-Saharan Africa and Europe. Lineage 2 (Beijing), associated with multidrug resistance, predominated in South Asia, while Lineage 3 (CAS/Delhi) was prevalent in Pakistan and Sudan. MDR-TB rates varied widely from 1.9% in Ghana to 75% in Ireland with high rates also in Nepal (56.8%) and India (50.6%). Male patients accounted for 64% of MTBC cases, indicating gender disparities in disease burden. Spoligotyping was the most frequently used molecular method, though WGS is increasingly employed for its higher resolution. DST methods included the proportion method on Lowenstein–Jensen medium, MGIT 960, GeneXpert MTB/RIF, and Line Probe Assays (LPA). Findings reveal significant regional variation in MTBC lineages and resistance rates. To improve TB control, there is an urgent need for standardized molecular diagnostics, enhanced regional surveillance, and gender-sensitive treatment strategies. Investment in diagnostic infrastructure, particularly in high-burden regions, is critical to curbing the global spread of MDR-TB.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".