Estrogenic modulation of brain activity: implications for schizophrenia and Parkinson’s disease
Bibliographic record
Abstract
Evidence suggests the estrogens may play a role in various mental and neurodegenerative diseases. We review the evidence implicating estradiol in schizophrenia and Parkinson's disease. Epidemiologic and clinical studies on the effects of estrogens in schizophrenia are surveyed, and animal studies and in vitro models of the modulatory effects of estrogens on neurotransmitters associated with schizophrenia (i.e., dopamine, serotonin, glutamate) are reviewed. Epidemiologic and clinical data suggesting a role for estrogens in Parkinson's disease and in vivo and in vitro models demonstrating neuroprotective effects of estrogens are then examined. Despite the numerous animal studies on the effects of estrogens in the brain, clinical data are sparse and often contradictory. Compounds with more specific and potent estrogenic activity in the brain are required to further research efforts in this area. Possible candidates are the selective estrogen receptor modulators (SERMs), whose agonist or antagonist properties depend on the target tissue. The effects of various SERMs in the brain are reviewed, and our novel findings on the effects of SERMs on 5-HT2A receptors in the rat cortex and nucleus accumbens are presented. We suggest that drugs with estrogenic activity in the brain may have therapeutic potential, either by modulating brain neurotransmission or through neuroprotective activity.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".