Thiopental enhances human platelet aggregation by increasing arachidonic acid release
Bibliographic record
Abstract
Conflicting results have been reported regarding the effect of thiopental on aggregation and cytosolic calcium levels in platelets. The present study attempted to clarify these phenomena. Using platelet-rich plasma or washed suspensions, platelet aggregation, thromboxane (TX) B2 formation, arachidonic acid (AA) release, and cytosolic free calcium concentrations ([Ca2+]i) were measured in the presence or absence of thiopental (30-300 microM). Platelet activation was induced by adenosine diphosphate (ADP, 0.5-15 microM), epinephrine (0.1-20 microM) arachidonic acid (0.5-1.5 mM), or (+)-9,11-epithia-11,12-methano-TXA2 (STA2, 30-500 nM). Measurements of primary aggregation were performed in the presence of indomethacin (10 microM). Low concentrations of ADP and epinephrine, which did not induce secondary aggregation in a control study, induced strong secondary aggregation in the presence of thiopental (> or = 100 microM). Thiopental (> or = 100 microM) also increased the TXB2 formation induced by ADP and epinephrine. Thiopental (300 microM) increased ADP- and epinephrine-induced 3H-AA release. Thiopental (300 microM) also augmented the ADP- and epinephrine-induced increases in [Ca2+]i in the presence of indomethacin. Thiopental appears to enhance ADP- and epinephrine-induced secondary platelet aggregation by increasing AA release during primary aggregation, possibly by the activation of phospholipase A2.
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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".