Betanin improves motor function and alleviates experimental Parkinsonism via downregulation of TLR4/MyD88/NF-κB pathway: Molecular docking and biological investigations
Bibliographic record
Abstract
Parkinson's disease (PD) is a progressive neuroinflammatory and degenerative disease. In this study, we investigated the neuroprotective action of betanin in the rotenone-induced Parkinson-like mice model. Twenty-eight adult male Swiss albino mice were divided into four groups: Vehicle, Rotenone, Rotenone + Betanin 50 mg/kg, and Rotenone + Betanin 100 mg/kg. Parkinsonism was induced by subcutaneous injection of 9 doses of rotenone (1 mg/kg/48 h) plus betanin at 50 and 100 mg/kg/48 h in rotenone + betanin groups for twenty days. Motor dysfunction was assessed after the end of the therapeutic period using the pole, rotarod, open-field, grid, and cylinder tests. Malondialdehyde, reduced glutathione (GSH), Toll-like receptor 4 (TLR4), myeloid differentiation primary response-88 (MyD88), nuclear factor kappa- B (NF-κB), neuronal degeneration in the striatum were evaluated. In addition, we assessed the immunohistochemical densities of tyrosine hydroxylase (TH) in Str and in substantia nigra compacta (SNpc). Our results showed that rotenone remarkably decreased (results of tests), increased decreased TH density with a significant increase in MDA, TLR4, MyD88, NF-κB, and a decrease in GSH (p < 0.05). Treatment with betanin significantly results of tests), increased TH density. Furthermore, betanin significantly downregulated malondialdehyde and improved GSH. Additionally, the expression of TLR4, MyD88, and NF-κB was significantly alleviated. Betanin's powerful antioxidative and anti-inflammatory properties can be related to its neuroprotective potential as well as its ability to delay or prevent neurodegeneration in PD.
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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) | 0.000 | 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 | 0.001 |
| 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".