Decoding drug resistance in <i>Mycobacterium tuberculosis</i> complex: genetic insights and future challenges
Bibliographic record
Abstract
INTRODUCTION: (MTB) complicates TB eradication efforts. This review delves into the complexities of anti-TB drug resistance, its mechanisms, and implications on healthcare strategies globally. AREAS COVERED: We explore the genetic underpinnings of resistance to both first-line and second-line anti-TB drugs, highlighting the role of mutations in key genes. The discussion extends to advanced diagnostic techniques, such as Whole-Genome Sequencing (WGS), CRISPR-based diagnostics and their impact on identifying and managing drug-resistant TB. Additionally, we discuss artificial intelligence applications, current treatment strategies, challenges in managing MDR-TB and XDR-TB, and the global disparities in TB treatment and control, translating to different therapeutic outcomes and have the potential to revolutionize our understanding and management of drug-resistant tuberculosis. EXPERT OPINION: The current landscape of anti-TB drug resistance demands an integrated approach combining advanced diagnostics, novel therapeutic strategies, and global collaborative efforts. Future research should focus on understanding polygenic resistance and developing personalized medicine approaches. Policymakers must prioritize equitable access to diagnosis and treatment, enhancing TB control strategies, and support ongoing research and augmented government funding to address this critical public health issue effectively.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".