Killer immunoglobulin like receptor gene content diversity among Northern Indian population
Bibliographic record
Abstract
BACKGROUND: Genes encoding KIR receptors are clustered in one of the most variable regions of the human genome. KIR gene frequencies vary in worldwide populations and reveal high probability of individuals differing in their gene content. AIM: This study aimed to investigate KIR diversity among the northern Indian population who share features with either Western Eurasian or East Asian populations. It sought to decipher how northern Indians are associated phylogenetically with global populations whilst also focusing on differentiation of populations. SUBJECTS AND METHODS: This paper studied 867 northern Indians using PCR-SSP. Gene and genotypic frequencies were calculated, using statistical analyses. Findings were compared against 76 global populations of differing ethnicities. RESULTS: This northern Indian population shared characteristics with Western Eurasian or Asian Indian populations, as is evident from genetic distance, clustered heatmap, phylogenetic assessment and principal component analysis. The findings are consistent with the demographic history of northern India, including specific features, such as presence of comparatively high KIR B-haplotype as compared to A-haplotype. CONCLUSION: KIR frequencies and profiles of northern Indians were more similar to Western Eurasians, Africans and Asian Indians. This may suggest that KIR genes are under constant evolutionary pressures and selection, which may be linked to different invading pathogens.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".