Clinically Relevant Hormone Treatments Fail to Induce Spinogenesis in Prefrontal Cortex of Aged Female Rhesus Monkeys
Bibliographic record
Abstract
Preclinical animal models have provided strong evidence that estrogen (E) therapy (ET) enhances cognition and induces spinogenesis in neuronal circuits. However, clinical studies have been inconsistent, with some studies revealing adverse effects of ET, including an increased risk of dementia. In an effort to bridge this disconnect between the preclinical and clinical data, we have developed a nonhuman primate (NHP) model of ET combined with high-resolution dendritic spine analysis of dorsolateral prefrontal cortical (dlPFC) neurons. Previously, we reported cyclic ET in aged, ovariectomized NHPs increased spine density on dlPFC neurons. Here, we report that monkeys treated with cyclic E treatment paired with cyclic progesterone (P), continuous E combined with P (either cyclic or continuous), or unopposed continuous E failed to increase spines on dlPFC neurons. Given that the most prevalent form of ET prescribed to women is a combined and continuous E and P, these data bring into convergence the human neuropsychological findings and preclinical neurobiological evidence that standard hormone therapy in women is unlikely to yield the synaptic benefit presumed to underlie the cognitive enhancement reported in animal models.
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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 | 0.001 |
| 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".