CD8A gene polymorphisms predict severity factors in chronic rhinosinusitis
Bibliographic record
Abstract
BACKGROUND: A genetic basis to chronic rhinosinusitis (CRS) is postulated, but remains elusive. We have recently identified low levels of circulating CD8 lymphocytes as a frequent finding in difficult-to-treat or refractory CRS. In major histocompatibility complex 1 class 1 (MHC1) deficiency, low circulating levels of CD8 lymphocytes secondary to mutations in the cluster of differentiation 8a (CD8A), tapasin 1 (TAP1), tapasin 2 (TAP2), or tapasin binding-protein (TAPBP) genes lead to a clinical syndrome, which is associated with severe CRS. The objective of this work was to identify whether genetic factors associated with MHC1 deficiency are present in CRS. METHODS: Previous results from a genomewide association study of CRS were screened for polymorphisms in the CD8A, TAP1, TAP2, and TAPBP genes associated with MHC1 immunodeficiency syndrome. Significant polymorphisms were tested for associations with demographic factors characterizing severe CRS. RESULTS: Polymorphisms in the CD8A (rs3810831) and TAPBP (rs2282851) genes were significantly associated with CRS. Major allele homozygosity for CD8A (rs3810831) was associated with a higher frequency of affected relatives (p = 0.052), increased severity as characterized by age at diagnosis (p = 0.009), age at first surgery (p = 0.004), and number of surgeries (p = 0.008), whereas TAPBP (rs2282851) was associated increased risk for CRS (odds ratio [OR] = 2.48, p = 0.0076). CONCLUSION: Modified CD8A or TAPBP gene function may contribute to the development of refractory CRS via altered MHC1 function and reduction of circulating CD8 lymphocytes. Identification of markers in the CD8A or TAPBP genes via sequencing may offer a basis for genetic testing in CRS.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".