Normalization of hormonal imbalances, ovarian follicular dynamics and metabolic effects in follitrophin receptor knockout mice
Bibliographic record
Abstract
Genetically modified follitrophin receptor knockout female mice with total FSH-receptor (FSH-R) deletion are sterile and their combined estrogen deficiency-hyperandrogenemic status provides an experimental paradigm to study the effect of hormonal imbalances on ovarian function and metabolic alterations. Elevated LH levels causing hyperandrogenemia perturb normal folliculogenesis. To control diverse pathophysiology associated with hormonal imbalances, we investigated the effects of transplanting a single normal mouse ovary in young mutants. An intact FSH-R signalling system in the graft responded promptly to the up-regulated pituitary gonadotrophins circulating in the host mutant. Resumption of regular estrous cycles validated stimulation of uterine functions. Secretions from the viable functioning grafts partially corrected follicular abnormalities originally present in host ovaries. Stromal hyperplasia responsible for high ovarian LH-receptor and key enzymes in host thecal/interstitial complex and hyperandrogenemia was reduced in host ovaries. Increases in plasma estradiol and reduced LH and free testosterone re-established the negative-feedback system. Reduced android obesity and activation of mammary glands indicated the combined beneficial effects of normalized steroid hormones on target organs. These data provide evidence that ovarian transplantation in mutants corrects estrogen loss and hyperandrogenemia. However, correction of hormonal imbalances is not sufficient to fully restore effects of FSH-R loss in host granulosa cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".