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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 teacher head, 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".