Protective Effects of Ursodeoxycholic Acid Against Oxidative Stress and Neuroinflammation Through Mitogen-Activated Protein Kinases Pathway in MPTP-Induced Parkinson Disease
Bibliographic record
Abstract
OBJECTIVES: Parkinson disease (PD) is the second most common neurodegenerative disorder, and no disease-modifying medications are available. Ursodeoxycholic acid (UDCA) has been shown to prevent neuronal damage; however, the effect of UDCA on PD is unclear. This study aimed to the role of UDCA on 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced mouse model of PD. METHODS: Mice were divided into 3 experimental groups: the control group, MPTP group, and UDCA-treat group. Mice were tested for behavioral impairments, and slices at the level of the ventral midbrain were collected to perform hematoxylin and eosin and terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling staining and immunohistochemistry. To evaluate the levels of dopamine (DA), serotonin (5-HT), antioxidant markers, and inflammatory cytokines, enzyme-linked immunoassays were carried out. The protein (α-synuclein, p38, phospho-p38, c-Jun N-terminal kinase [JNK], and phospho-JNK) expression was examined adopting Western blot. RESULTS: We found that UDCA reduced the MPTP-induced degeneration of DA neurons, improved behavioral impairments, and decreased the protein level of α-synuclein, accompanied with increases of DA and 5-HT. In the present study, UDCA prevented DA neurons from MPTP toxicity with increased superoxide dismutase, catalase, glutathione, and decreased malondialdehyde levels. Ursodeoxycholic acid prevented DA neurons from MPTP toxicity with decreased levels of tumor necrosis factor α, interferon γ, and interleukin (IL)-1β, IL-6, and IL-10. Our results demonstrated that UDCA inhibited the phosphorylation of JNK and p38MAPK. CONCLUSIONS: This study revealed protective effects of UDCA against oxidative stress and neuroinflammation through mitogen-activated protein kinases pathways in MPTP-induced PD, suggesting that UDCA may be a novel therapeutic candidate for 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) | 0.001 | 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".