Biased signaling in platelet G-protein coupled receptors
Bibliographic record
Abstract
Platelets are small megakaryocyte-derived, anucleate, disk-like structures that play an outsized role in human health and disease. Both a decrease in the number of platelets and a variety of platelet function disorders result in petechiae or bleeding that can be life threatening. Conversely, the inappropriate activation of platelets, within diseased blood vessels, remains the leading cause of death and morbidity by affecting heart attacks and stroke. The fine balance of the platelet state in healthy individuals is controlled by a number of receptor-mediated signaling pathways that allow the platelet to rapidly respond and maintain haemostasis. G-protein coupled receptors (GPCRs) are particularly important regulators of platelet function. Here we focus on the major platelet-expressed GPCRs and discuss the roles of downstream signaling pathways (e.g., different G-protein subtypes or β-arrestin) in regulating the different phases of the platelet activation. Further, we consider the potential for selectively targeting signaling pathways that may contribute to platelet responses in disease through development of biased agonists. Such selective targeting of GPCR-mediated signaling pathways by drugs, often referred to as biased signaling, holds promise in delivering therapeutic interventions that do not present significant side effects, especially in finely balanced physiological systems such as platelet activation in haemostasis.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".