Tranexamic acid concentrations associated with human seizures inhibit glycine receptors
Bibliographic record
Abstract
Antifibrinolytic drugs are widely used to reduce blood loss during surgery. One serious adverse effect of these drugs is convulsive seizures; however, the mechanisms underlying such seizures remain poorly understood. The antifibrinolytic drugs tranexamic acid (TXA) and ε-aminocaproic acid (EACA) are structurally similar to the inhibitory neurotransmitter glycine. Since reduced function of glycine receptors causes seizures, we hypothesized that TXA and EACA inhibit the activity of glycine receptors. Here we demonstrate that TXA and EACA are competitive antagonists of glycine receptors in mice. We also showed that the general anesthetic isoflurane, and to a lesser extent propofol, reverses TXA inhibition of glycine receptor-mediated current, suggesting that these drugs could potentially be used to treat TXA-induced seizures. Finally, we measured the concentration of TXA in the cerebrospinal fluid (CSF) of patients undergoing major cardiovascular surgery. Surprisingly, peak TXA concentration in the CSF occurred after termination of drug infusion and in one patient coincided with the onset of seizures. Collectively, these results show that concentrations of TXA equivalent to those measured in the CSF of patients inhibited glycine receptors. Furthermore, isoflurane or propofol may prevent or reverse TXA-induced seizures.
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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) | 0.002 | 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".