Neuroprotective Effect of Progesterone in MPTP-Treated Male Mice
Bibliographic record
Abstract
BACKGROUND: Numerous studies have reported on the neuroprotective activity of estradiol, whereas the effect of the other ovarian steroid, progesterone, is much less documented. METHODS: This study sought to investigate neuroprotection with a low dose of progesterone (1 µg) in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-treated male mice to model Parkinson's disease and compare it to the effect of this steroid in intact mice (experiment 1). We also investigated if high doses of progesterone could protect dopaminergic neurons already exposed to MPTP (experiment 2). We measured progesterone effects on various dopaminergic markers [dopamine and its metabolites, dopamine transporter (DAT) and vesicular monoamine transporter 2 (VMAT2)] and on neuroactive steroids in both plasma and the brain. RESULTS: For experiment 1, our results showed that progesterone completely prevented the effect of MPTP toxicity on dopamine concentrations, on the increase in the 3-methoxytyramine/dopamine ratio, as well as on VMAT2-specific binding in the striatum and the substantia nigra. Progesterone decreased MPTP effects on 3,4-dihydroxyphenylacetic acid concentrations and DAT-specific binding in the lateral part of the anterior striatum and in the middle striatum (medial and lateral parts). Progesterone treatment of intact mice had no effect on the markers investigated. For experiment 2, measures of dopaminergic markers in the striatum showed that 8 mg/kg of progesterone was the most effective dose to reduce MPTP effects, and more limited effects were observed with 16 mg/kg. We found that progesterone treatment increases the levels of brain progesterone itself as well as of its metabolites. CONCLUSION: Our result showed that progesterone has neuroprotective effects on dopaminergic neurons in MPTP-treated male mice.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".