The <i>pncA</i> gene mutations of <i>Mycobacterium tuberculosis</i> in multidrug‐resistant tuberculosis
Bibliographic record
Abstract
The pncA gene encodes pyrazinamidase enzyme which converts drug pyrazinamide to active form pyrazinoic acid, but mutations in this gene can prevent enzyme activity which leads to pyrazinamide resistance. The cross-sectional study was carried out during 2016-2017 for 12 months. The purpose of the study was to detect mutation at codon 12 and codon 85 in the pncA gene in local multidrug-resistant tuberculosis (MDR-TB) patients by developing a simple molecular test so that disease could be detected timely in the local population. DNA extracted from sputum-cultured samples from MDR-TB patients and subjected to semi-multiplex allele-specific PCR by using self-designed primers against the pncA gene. Among 75 samples, 53 samples were subjected to molecular analysis based on purified DNA quantity and quality. The primers produced 250 and 480 bp fragments, indicating the mutations at codon 12 (aspartate to alanine) and codon 85 (leucine to proline) respectively. MDR-TB was more common in the age group 21-40 years. Fifty-seven percent of samples (n = 30) were found positive for pncA mutations, whereas 43% of samples (n = 23) showed negative results. Thirteen percent of samples (n = 4) had mutations at codon 12 in which aspartate was converted to alanine, and they produced an amplified product of 480 bp. Eighty-seven percent of samples (n = 26) had mutations at codon 85 in which leucine was converted to proline and amplified product size was 250 bp. The mutations were simple nucleotide substitutions. The prevalence of mutations in which leucine was substituted by proline was higher than the mutations in which aspartate was substituted by alanine. A high prevalence of substitution mutation (CTG → CCG; leucine to proline) was detected in MDR-TB cases. Earlier detection of MDR-TB via an effective molecular diagnostic method can control the MDR tuberculosis spread in the population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".