Mefenamate, an Agent that Fails to Attenuate Experimental Cerebral Infarction
Bibliographic record
Abstract
BACKGROUND: Blockade of nonselective cation channels is a potential therapeutic approach that has not been attempted in cerebral ischemia, in spite of the ability of these channels to allow cellular calcium influx into neurons. Fenamates are a class of molecules that block these channels, and many congeners are also anti-inflammatory and free radical scavenging. These three mechanisms may contribute to brain damage in ischemia. METHODS: Pretreatment or posttreatment with mefenamate (30 mg/kg) was evaluated in a temperature-controlled rat transient focal ischemia model. Quantitative histopathology on 26 coronal sections allowed determination of tissue necrosis and tissue atrophy at one week survival. RESULTS: Neither pre- nor postischemic administration of a dose previously shown effective in preventing epileptic neuronal necrosis was found to reduce necrosis in cortex, nor in any subcortical structures. CONCLUSIONS: We conclude that nonselective cation channel blockade with mefenamate affords no neuroprotection in this model. Publication bias against negative studies exists in the literature, but we here report negative findings due to the multiple potentially positive actions of the drug. Closer examination of the effects of the molecule, however, reveals several potentially negative effects as well. We conclude there may be inherent weakness in pharmacologic monotherapy, even with molecules having protean potentially beneficial effects. This conclusion seems to have been borne out by the results of recent clinical trials.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".