Gallic acid improved behavior, brain electrophysiology, and inflammation in a rat model of traumatic brain injury
Bibliographic record
Abstract
Traumatic brain injury (TBI) is one of the main causes of intellectual and cognitive disabilities. In the clinic it is essential to limit the development of cognitive impairment after TBI. In this study, the effects of gallic acid (GA; 100 mg/kg, per oral, from 7 days before to 2 days after TBI induction) on neurological score, passive avoidance memory, long-term potentiation (LTP) deficits, and levels of proinflammatory cytokines including interleukin-1 beta (IL-1β), interleukin 6 (IL-6), and tumor necrosis factor-α (TNF-α) in the brain have been evaluated. Brain injury was induced following Marmarou's method. Data were analyzed by one-way and repeated measures ANOVA followed by Tukey's post-hoc test. The results indicated that memory was significantly impaired (p < 0.001) in the group treated with TBI + vehicle, together with deterioration of the hippocampal LTP and increased brain tissue levels of IL-1β, IL-6, and TNF-α. GA treatment significantly improved memory and LTP in the TBI rats. The brain tissue levels of IL-1β, IL-6, and TNF-α were significantly reduced (p < 0.001) in the group treated with GA. The results suggest that GA has neuroprotective properties against TBI-induced behavioral, electrophysiological, and inflammatory disorders, probably via the decrease of cerebral proinflammatory cytokines.
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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 | 0.001 | 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) | 0.001 | 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".