Effects of ferulic acid on oxidative stress, heat shock protein 70, connexin 43, and monoamines in the hippocampus of pentylenetetrazole-kindled rats
Bibliographic record
Abstract
The present study investigated the effects of ferulic acid (FA) on pentylenetetrazole (PTZ)-induced seizures, oxidative stress markers (malondialdehyde (MDA), catalase, and reduced glutathione (GSH)), connexin (Cx) 43, heat shock protein 70 (Hsp 70), and monoamines (serotonin (5-HT) and norepinephrine (NE)) levels in a rat model of PTZ-induced kindling. Sixty Sprague Dawley rats were divided into 5 equal groups: (a) normal group; (b) FA group: normal rats received FA at a dose of 40 mg/kg daily; (c) PTZ group: normal rats received PTZ at a dose of 50 mg/kg i.p. on alternate days for 15 days; (d) FA-before group: treatment was the same as for the PTZ group, except rats received FA; and (e) FA-after group: rats received FA from sixth dose of PTZ. PTZ caused a significant increase in MDA, Cx43, and Hsp70 along with a significant decrease in GSH, 5-HT, and NE levels and CAT activity in the hippocampus (p < 0.05). Pre- and post-treatment with FA caused significant improvement in behavioral parameters, MDA, CAT, GSH, 5-HT, NE, Cx43 expression, and Hsp70 expression in the hippocampal region (p < 0.05). We conclude that FA has neuroprotective effects in PTZ-induced epilepsy, which might be due to attenuation of oxidative stress and Cx43 expression and upregulation of neuroprotective Hsp70 and neurotransmitters (5-HT and NE).
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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".