Human Thromboxane A2 Receptor Genetic Variants: In Silico, In Vitro and “In Platelet” Analysis
Bibliographic record
Abstract
Thromboxane and its receptor have emerged as key players in modulating vascular thrombotic events. Thus, a dysfunctional hTP genetic variant may protect against (hypoactivity) or promote (hyperactivity) vascular events, based upon its activity on platelets. After extensive in silico analysis, six hTP-α variants were selected (C(68)S, V(80)E, E(94)V, A(160)T, V(176)E, and V(217)I) for detailed biochemical studies based on structural proximity to key regions involved in receptor function and in silico predictions. Variant biochemical profiles ranged from severe instability (C(68)S) to normal (V(217)I), with most variants demonstrating functional alteration in binding, expression or activation (V(80)E, E(94)V, A(160)T, and V(176)E). In the absence of patient platelet samples, we developed and validated a novel megakaryocyte based system to evaluate human platelet function in the presence of detected dysfunctional genetic variants. Interestingly, variant V80E exhibited reduced platelet activation whereas A160T demonstrated platelet hyperactivity. This report provides the most comprehensive in silico, in vitro and "in platelet" evaluation of hTP variants to date and highlightscurrent inherent problems in evaluating genetic variants, with possible solutions. The study additionally provides clinical relevance to characterized dysfunctional hTP variants.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".