An NO Donor Approach to Neuroprotective and Procognitive Estrogen Therapy Overcomes Loss of NO Synthase Function and Potentially Thrombotic Risk
Bibliographic record
Abstract
Selective estrogen receptor modulators (SERMs) are effective therapeutics that preserve favorable actions of estrogens on bone and act as antiestrogens in breast tissue, decreasing the risk of vertebral fractures and breast cancer, but their potential in neuroprotective and procognitive therapy is limited by: 1) an increased lifetime risk of thrombotic events; and 2) an attenuated response to estrogens with age, sometimes linked to endothelial nitric oxide synthase (eNOS) dysfunction. Herein, three 3(rd) generation SERMs with similar high affinity for estrogen receptors (ERα, ERβ) were studied: desmethylarzoxifene (DMA), FDMA, and a novel NO-donating SERM (NO-DMA). Neuroprotection was studied in primary rat neurons exposed to oxygen glucose deprivation; reversal of cholinergic cognitive deficit was studied in mice in a behavioral model of memory; long term potentiation (LTP), underlying cognition, was measured in hippocampal slices from older 3×Tg Alzheimer's transgenic mice; vasodilation was measured in rat aortic strips; and anticoagulant activity was compared. Pharmacologic blockade of GPR30 and NOS; denudation of endothelium; measurement of NO; and genetic knockout of eNOS were used to probe mechanism. Comparison of the three chemical probes indicates key roles for GPR30 and eNOS in mediating therapeutic activity. Procognitive, vasodilator and anticoagulant activities of DMA were found to be eNOS dependent, while neuroprotection and restoration of LTP were both shown to be dependent upon GPR30, a G-protein coupled receptor mediating estrogenic function. Finally, the observation that an NO-SERM shows enhanced vasodilation and anticoagulant activity, while retaining the positive attributes of SERMs even in the presence of NOS dysfunction, indicates a potential therapeutic approach without the increased risk of thrombotic events.
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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 | 0.000 |
| 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".