Evaluating Neurotherapeutic Potential of Myricetin by In-Vivo research models
Bibliographic record
Abstract
Background: Oxidative stress, neuroinflammation, and poor neurotrophic signaling are associated with neurological diseases. Myricetin is a bioflavonoid present in berries, vegetables, and medicinal herbs, with strong anti-inflammatory, antioxidant, and neurotrophic properties in animal studies. The objective of the present in-vivo research was to assess the neuroprotective potential of myricetin on nerve growth factor (NGF) expression and neuroinflammation in chemically induced neural injury. Materials and Methods: Thirty healthy male adult Wistar rats (180-220g, 8 weeks old) were randomly divided into five groups (n=6 per group). Group I was used as a control, with no intervention. Groups II to V were exposed to a standard propionic acid (PPA) to induce neuroinflammation. Groups III-V were induced and subsequently administered with oral myricetin of 50 mg/kg, 100 mg/kg, and 200 mg/kg for 28 days. Serum levels of NGF were measured with Enzyme-linked Immunosorbent assay (ELISA). One-way ANOVA was performed for statistical analysis using SPSS. Results: PPA decreased NGF to 4.3 ± 0.5 pg/ml (p< 0.001). Myricetin restored NGF to 9.3, 7.6, and 9.8 pg/mL via 50, 100, and 200 mg/kg doses, respectively. At 200 mg/kg, C-reactive protein (CRP), tumor necrosis factor (TNF), and malondialdehyde (MDA) were decreased to 1.7 ± 0.2 mg/L, 26 ± 3 pg/mL, and 2.9 ± 0.3 nmol/mg, compared to 4.9 ± 0.4, 58 ± 4, and 6.7 ± 0.4. (p < 0.001). Conclusion: Myricetin exhibits promising neurotherapeutic potential, evidenced by its ability to upregulate NGF and mitigate neuroinflammatory damage, making it a potential therapeutic option for the treatment of neurological disorders.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".