Neuroprotective and Anti-Inflammatory Effects of Rhus coriaria Extract in a Mouse Model of Ischemic Optic Neuropathy
Bibliographic record
Abstract
Modulating oxidative stresses and inflammation can potentially prevent or alleviate the pathological conditions of diseases associated with the nervous system, including ischemic optic neuropathy. In this study we evaluated the anti-neuroinflammatory and neuroprotective activities of Rhus coriaria (R. coriaria) extract in vivo. The half maximal inhibitory concentration (IC50) for DPPH, ABTS and β–carotene were 6.79 ± 0.009 µg/mL, 10.94 ± 0.09 µg/mL, and 6.25 ± 0.06 µg/mL, respectively. Retinal ischemia was induced by optic nerve crush injury in albino Balb/c mice. The anti-inflammatory activity of ethanolic extract of R. coriaria (ERC) and linoleic acid (LA) on ocular ischemia was monitored using Fluorescence Molecular Tomography (FMT). Following optic nerve crush injury, the mice treated with 400 mg/kg of ERC and LA exhibited an 84.87% and 86.71% reduction of fluorescent signal (cathepsin activity) respectively. The results of this study provide strong scientific evidence for the neuroprotective activity of the ERC, identifying LA as one of the main components responsible for the effect. ERC may be useful and worthy of further development for its adjunctive utilization in the treatment of optic neuropathy.
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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 | 0.000 | 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".