Evolution and emergence of multidrug-resistant <i>Mycobacterium tuberculosis</i> in Chisinau, Moldova
Bibliographic record
Abstract
1. ABSTRACT Background Drug-resistant tuberculosis is a high priority threat to global public health. There are still critical gaps in understanding how novel drug-resistant M. tuberculosis strains emerge and, once emergent, what drives the differential propagation of certain epidemiologically-successful strains over others. This study sought to describe the joint evolutionary and epidemiological histories of a novel multidrug-resistant M. tuberculosis strain recently identified in the capital city of the Republic of Moldova (MDR Ural/4.2). Methods Using whole genome sequence data and Bayesian phylogenomic methods, we reconstruct the stepwise acquisition of drug-resistance mutations in the MDR Ural/4.2 strain, estimate its historical bacterial population size over time, and infer the migration history of this strain between Eastern European countries. Results We infer that MDR Ural/4.2 likely evolved (via acquisition of rpoB S450L, which confers resistance to rifampin) in the early 1990s, during a period of social turmoil following Moldovan independence from the Soviet Union. This strain subsequently underwent substantial population size expansion in the early 2000s, at a time when national guidelines encouraged in hospital treatment of TB patients. We infer exportation of this strain and its INH-resistant ancestral precursor from Moldova to neighboring countries starting as early as 1985. Conclusions Our findings underscore how public health practice and social determinants of health shape the conditions under which M. tuberculosis evolves, and demonstrates how historical changes in these conditions shape present-day challenges in TB control. These findings underscore the need for regional coordination in TB control across Eastern Europe.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".