Transporters in human platelets: physiologic function and impact for pharmacotherapy
Bibliographic record
Abstract
Platelets store signaling molecules (eg, serotonin and ADP) within their granules. Transporters mediate accumulation of these molecules in platelet granules and, on platelet activation, their translocation across the plasma membrane. The balance between transporter-mediated uptake and elimination of signaling molecules and drugs in platelets determines their intracellular concentrations and effects. Several members of the 2 major transporter families, ATP-binding cassette (ABC) transporters and solute carriers (SLCs), have been identified in platelets. An example of an ABC transporter is MRP4 (ABCC4), which facilitates ADP accumulation in dense granules. MRP4 is a versatile transporter, and various additional functions have been proposed, notably lipid mediator release and a role in aspirin resistance. Several other ABC proteins have been detected in platelets with functions in glutathione and lipid homeostasis. The serotonin transporter (SERT, SLC6A4) in the platelet plasma membrane represents a well-characterized example of the SLC family. Moreover, recent experiments indicate expression of OATP2B1 (SLCO2B1), a high affinity transporter for certain statins, in platelets. Changes in transporter localization and expression can affect platelet function and drug sensitivity. This review summarizes available data on the physiologic and pharmacologic role of transporters in platelets.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".