Distribution of common and rare drug resistance patterns in Mycobacterium tuberculosis clinical isolates revealed by GenoType MTBDRplus and MTBDRsl assay
Bibliographic record
Abstract
Background: Tuberculosis (TB) remains a significant global health emergency caused by Mycobacterium tuberculosis (Mtb). The epidemiology, transmission, genotypes, mutational patterns, and clinical consequences of TB have been extensively studied worldwide, however, there is a lack of information regarding the epidemiology and mutational patterns of Mtb in Pakistan, specifically concerning the prevalence of multi-drug resistant TB (MDR-TB). Methods: This study aimed to investigate the incidence of Mtb and associated mutational patterns using the line probe assay (LPA). Previous studies have reported a high frequency of mutations in the rpoB, inhA, and katG genes, which are associated with resistance to rifampicin (RIF) and isoniazid (INH). Therefore, the current study utilized LPA to detect mutations in the rpoB, katG, and inhA genes to identify multi-drug resistant Mtb. Results: LPA analysis of a large pool of Mtb isolates, including samples from 241 sputum-positive patients, revealed that 34.85% of isolates were identified as MDR-TB, consistent with reports from various regions worldwide. The most prevalent mutations observed were rpoB S531L and inhA promoter C15T, which were associated with resistance to RIF and INH, respectively. Conclusions: This study highlights the effectiveness of GenoType MTBDRplus and MTBDRsl assays as valuable tools for TB management. These assays enable rapid detection of resistance to RIF, INH, and fluoroquinolones (FQs) in Mtb clinical isolates, surpassing the limitations of solid and liquid media-based methods. The findings contribute to our understanding of MDR-TB epidemiology and provide insights into the genetic profiles of Mtb in Pakistan, which are essential for effective TB control strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".