Killer Immunoglobulin-Like Receptor (KIR) Centromeric-AA Haplotype Is Associated with Ethnicity and Tuberculosis Disease in a Canadian First Nations Cohort
Bibliographic record
Abstract
Killer immunoglobulin-like receptors (KIR) on natural killer (NK) cells interact with other immune cells to monitor the immune system and combat infectious diseases, such as tuberculosis (TB). The balance of activating and inhibiting KIR interactions helps determine the NK cell response. In order to examine the enrichment or depletion of KIRs as well as to explore the association between TB status and inhibitory/stimulatory KIR haplotypes, we performed KIR genotyping on samples from 93 Canadian First Nations (Dene, Cree, and Ojibwa) individuals from Manitoba with active, latent, or no TB infection, and 75 uninfected Caucasian controls. There were significant differences in KIR genes between Caucasians and First Nations samples and also between the First Nations ethnocultural groups (Dene, Cree, and Ojibwa). When analyzing ethnicity and tuberculosis status in the study population, it appears that the KIR profile and centromeric haplotype are more predictive than the presence or absence of individual genes. Specifically, the decreased presence of haplotype B centromeric genes and increased presence of centromeric-AA haplotypes in First Nations may contribute to an inhibitory immune profile, explaining the high rates of TB in this 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".