EFFECTS OF LONG-TERM DIETARY ADMINISTRATION OF ER-β AGONIST DIARYLPROPIONITRILE ON OVARIECTOMIZED CD-1 MICE
Bibliographic record
Abstract
Diarylpropionitrile (DPN) is an estrogen receptor-β specific agonist with neuroprotective and anti-cancer properties, including improved maintenance of cognitive function with advanced age, the suppression of anxiety-like behaviours, and the inhibition of cancer cell growth. We hypothesized that DPN would affect lifespan. This hypothesis was tested in ovariectomized female mice, which model post-reproductive women. Estrogen receptor-β agonists are already in use to treat symptoms of menopause in this population. DPN was delivered in the diet at a dose corresponding to approximately 3mg/kg mouse body mass/day. During the study, we monitored DPN’s effect on body mass, anxiety-like behaviours, learning, memory, frailty, and survivorship. In comparison to controls, DPN-treated mice showed reduced anxiety-like effects at two months of treatment (9 months of age) and there was a possibility (p = 0.08) that this effect was maintained at more advanced ages. DPN-fed mice also exhibited improved spatial learning and memory at 28 months of age. They gained more weight over the course of the study and showed differences in the relationship between age and frailty. Despite increased weight gain, DPN-treated mice showed no deficits in the propensity to run or mean velocity during running events when tested in single wheel chambers. Although there was no significant effect on lifespan parameters, DPN-fed mice took longer to reach the 25%, 50%, 75% and 100% mortality quartiles (n=25). Based on these findings, we believe that dietary DPN administration shows promise as an anti-aging intervention and warrants further investigation in a larger study.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".