Differential Protective Properties of Estradiol and Tamoxifen against Methamphetamine-Induced Nigrostriatal Dopaminergic Toxicity in Mice
Bibliographic record
Abstract
Mechanisms implicated in protective potential of estrogens are poorly understood. Tamoxifen, a selective estrogen receptor modulator (SERM), presents a neuroprotective effect against methamphetamine (MA)- and methoxy-phenyltetrahydropyridine (MPTP)-induced toxicity when used alone but abolishes estrogen's positive effects when combined with this hormone. In order to understand tamoxifen's protective properties, the present study compared it to estradiol on several markers of dopaminergic neurons to achieve a relatively comprehensive comparison between these two agents. Estradiol benzoate (E) or tamoxifen were used at different concentrations (E: 1, 10 or 40 microg; tamoxifen: 12.5, 125 or 500 microg) 24 h prior to a MA injection in ovariectomized CD-1 mice. The effects of the lesion and treatments were studied on striatal dopamine (DA) concentrations, dopamine and monoamine vesicular transporters (DAT and VMAT2), and preproenkephalin (PPE) mRNA levels. Both treatments, at all concentrations, prevented the MA-induced decrease of striatal DA concentrations and VMAT2 binding. Only E was able to prevent loss of DAT binding in the lateral striatum and to attenuate the MA-induced increase in striatal PPE mRNA levels (at 1 or 40 microg). Therefore, in this paradigm, E and tamoxifen differentially modulated MA-induced neuronal damages. While both treatments prevented the DA decrease, E protected more efficiently other dopaminergic parameters suggesting that overall E is more effective than tamoxifen as a neuroprotectant of the nigrostriatal dopaminergic system.
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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 | 0.000 | 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 | 0.000 |
| 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".