Effects of barbiturates on human platelet aggregation differ depending on their chemical structures
Bibliographic record
Abstract
The effects of barbiturates on human platelet function are not fully understood. Since we have already revealed the effects and mechanisms of thiopental, thiamylal, and pentobarbital in platelets, the present study attempted to elucidate (i) the effects of other barbiturates on human platelet aggregation, (ii) the underlying mechanisms, and (iii) the structure-function relationship of barbiturates in platelets. Barbiturates, including amobarbital, butalbital, secobarbital, barbital, phenobarbital, metharbital, and primidone, were examined. Human platelet aggregation induced by adenosine diphosphate (ADP), epinephrine, and (+)-9,11-epithia-11,12-methano-thromboxane A2 (STA2), a thromboxane A2 analog, was measured using an 8-channel light-transmission aggregometer. The cytosolic free calcium concentration ([Ca2+]i) was measured by fluorometer using fura-2 loaded platelets. Inositol 1,4,5-trisphosphate (IP3) formation induced by STA2 was determined by a commercially available IP3 assay kit. Amobarbital, butalbital, and secobarbital suppressed ADP-, epinephrine- and STA2-induced platelet aggregation and the STA2-induced [Ca2+]i increase, even when Ca2+ influx was blocked by Ni2+. However, they did not affect STA2-induced IP3 formation. Barbital, phenobarbital, metharbital, and primidone (up to 1 mM) had no effect on ADP- and epinephrine-induced platelet aggregation. Thus, we conclude that amobarbital, butalbital, and secobarbital inhibit platelet aggregation by suppressing [Ca2+]i increase without affecting IP3 formation. However, these antiaggregatory effects may not have clinical importance, since the barbiturate concentrations used were higher than clinically relevant ones. The other tested barbiturates had no effects on platelet aggregation. The data indicate that the effects of barbiturates on platelet aggregation differ depending on their chemical structures.
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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) | 0.000 | 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) | 0.001 | 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".