Use of Short Tandem Repeat Sequences to Study Mycobacterium leprae in Leprosy Patients in Malawi and India
Bibliographic record
Abstract
BackgroundInadequate understanding of the transmission of Mycobacterium leprae makes it difficult to predict the impact of leprosy control interventions. Genotypic tests that allow tracking of individual bacterial strains would strengthen epidemiological studies and contribute to our understanding of the disease.Methodology/Principal FindingsGenotyping assays based on variation in the copy number of short tandem repeat sequences were applied to biopsies collected in population-based epidemiological studies of leprosy in northern Malawi, and from members of multi-case households in Hyderabad, India. In the Malawi series, considerable genotypic variability was observed between patients, and also within patients, when isolates were collected at different times or from different tissues. Less within-patient variability was observed when isolates were collected from similar tissues at the same time. Less genotypic variability was noted amongst the closely related Indian patients than in the Malawi series.Conclusions/SignificanceLineages of M. leprae undergo changes in their pattern of short tandem repeat sequences over time. Genetic divergence is particularly likely between bacilli inhabiting different (e.g., skin and nerve) tissues. Such variability makes short tandem repeat sequences unsuitable as a general tool for population-based strain typing of M. leprae, or for distinguishing relapse from reinfection. Careful use of these markers may provide insights into the development of disease within individuals and for tracking of short transmission chains.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".