Glibenclamide, a Sur1-Trpm4 antagonist, does not improve outcome after collagenase-induced intracerebral hemorrhage
Bibliographic record
Abstract
The sulfonylurea 1 transient receptor potential melastatin 4 (Sur1-Trpm4) receptor is selectively expressed after intracerebral hemorrhage (ICH). This upregulation contributes to increases in intracellular sodium. Water follows sodium through aquaporin channels, leading to cytotoxic edema. Even after edema is thought to have resolved, ionic dyshomeostasis persists, as does blood-brain barrier (BBB) damage. Glibenclamide, a hypoglycemic agent that inhibits Sur1-Trpm4, has been shown to reduce BBB damage and edema following infusion of autologous blood into the brain (ICH) as well as after other brain injuries. In order to further assess efficacy, we used the collagenase ICH model in rats to test whether glibenclamide reduces edema, attenuates ion dyshomeostasis, improves BBB damage, and reduces lesion volume. We tested a widely-used glibenclamide dose shown effective in other studies (10 μg/kg loading dose followed by 200 ng/hr for up to 7 days). Early initiation of glibenclamide did not significantly impact edema (72 hours), BBB permeability (72 hours), or lesion volume after ICH (28 days). Recovery from neurological impairments was also not improved by glibenclamide. These results suggest that glibenclamide will not improve outcome in ICH. However, the treatment appeared to be safe as there was no effect on bleeding or other physiological variables.
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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 | 0.000 |
| 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 | 0.001 |
| 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".