Polymorphisms in Toll-Like Receptor 10 and Tuberculosis Susceptibility: Evidence from Three Independent Series
Bibliographic record
Abstract
Background: The toll-like receptor 2 (TLR2) mediated immune response is critical for host defense against Mycobacterium tuberculosis. There is evidence that TLR10, a TLR2 signaling modulator, may be involved in progression of tuberculosis (TB). Methods: Using a self-validating case–control design, we tested for an association between seven TLR10 polymorphisms and susceptibility to TB in three independent series with two distinct populations. Single nucleotide polymorphism (SNP) genotypes were determined by the SNPscanTM method. Three genetic models (additive, dominant, and recessive) as well as multiple-SNP score analyses were used to evaluate the risk of TB associated with the TLR10 SNPs. Results: By comparing TB patients with healthy controls, we observed two SNPs (rs11466617 and rs4129009) that were associated with decreased risk of TB in the Tibetan population, but did not in the Chinese Han population. Further analysis demonstrated that the rs11466617 CC genotype served as a protective factor against the progression of latent TB infection (LTBI) to active TB under the recessive model. None of the SNPs were significantly different in comparisons of TB uninfected people with LTBI individuals. Additionally, when the underlying four TB-associated loci were considered together in a multiple-SNP score analysis, we observed an allele dose-dependent decrease in TB risk in Tibetans. Conclusion: Variants of TLR10 may show an ethnic specificity on susceptibility to TB in Tibetan individuals. rs11466617 affected the susceptibility to progress from LTBI to active TB disease, but was not associated with the establishment of latent TB infection after Mycobacterium tuberculosis exposure. More studies are needed to verify this genetic epidemiological result and unravel the role of TLR10 SNPs in the pathogenesis of TB.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.004 | 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".