Protective effect of naringin on 3-nitropropionic acid-induced neurodegeneration through the modulation of matrix metalloproteinases and glial fibrillary acidic protein
Bibliographic record
Abstract
Naringin (4',5,7-trihydroxy-flavonone-7-rhamnoglucoside), a flavonone present in grapefruit, has recently been reported to protect against neurodegeration, induced with 3-nitropropionic acid (3-NP), through its antioxidant, anti-inflammatory, and antiapoptotic properties. This study used a rat model of 3-NP-induced neurodegeneration to investigate the neuroprotective effects of naringin exerted by modulating the expression of matrix metalloproteinases and glial fibrillary acidic protein. Neurodegeneration was induced with 3-NP (10 mg/kg body mass, by intraperitoneal injection) once a day for 2 weeks, and induced rats were treated with naringin (80 mg/kg body mass, by oral gavage, once a day for 2 weeks). Naringin ameliorated the motor abnormalities caused by 3-NP, and reduced blood-brain barrier dysfunction by decreasing the expression of matrix metalloproteinases 2 and 9, along with increasing the expression of the tissue inhibitors of metalloproteinases 1 and 2 in 3-NP-induced rats. Further, naringin reduced 3-NP-induced neuroinflammation by decreasing the expression of nuclear factor-kappa B and glial fibrillary acidic protein. Thus, naringin exerts protective effects against 3-NP-induced neurodegeneration by ameliorating the expressions of matrix metalloproteinases and glial fibrillary acidic protein.
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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) | 0.000 | 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 | 0.000 |
| 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".