Novel multiplex PCR-SSP method for centromeric KIR allele discrimination
Bibliographic record
Abstract
Allelic diversity of the KIR2DL receptors drive differential expression and ligand-binding affinities that impact natural killer cell function and patient outcomes for diverse cancers. We have developed a global intermediate resolution amplification-refractory mutation system (ARMS) PCR-SSP method for distinguishing functionally relevant subgroups of the KIR2DL receptors, as defined by phylogenetic study of the protein sequences. Use of the ARMS design makes the method reliable and usable as a kit, with all reactions utilizing the same conditions. Six reactions define six subgroups of KIR2DL1; four reactions define three subgroups of KIR2DL2; and five reactions define four subgroups of KIR2DL3. Using KIR allele data from a cohort of 426 European-Americans, we identified the most common KIR2DL subtypes and developed the high-throughput PCR-based methodology, which was validated on a separate cohort of 260 healthy donors. Linkage disequilibrium analysis between the different KIR2DL alleles revealed that seven allelic combinations represent more than 95% of the observed population genotypes for KIR2DL1/L2/L3. In summary, our findings enable rapid typing of the most common KIR2DL receptor subtypes, allowing more accurate prediction of co-inheritance and providing a useful tool for the discrimination of observed differences in surface expression and effector function among NK cells exhibiting disparate KIR2DL allotypes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".