Expression of gonadotropin receptors in the rat hypothalamo‐pituitary axis
Bibliographic record
Abstract
The gonadotropins luteinizing hormone (LH) and follicle stimulating hormone (FSH) control numerous aspects of neural function. They also inhibit gonadotropin secretion at hypothalamic and possibly pituitary sites. Surprisingly, little attention has been focused on the possible involvement of neural LH and FSH receptors in these effects. In partial support of this hypothesis, Lei et al. detected LH receptor transcripts in brain but not pituitary tissue. The possibility that FSH and LH receptor transcripts may be present in the brain and pituitary gland of the rat was therefore investigated, using the polymerase chain reaction. LH receptor cDNAs of expected size (600 bp) were generated from ovarian or testicular reverse‐transcribed RNA. Conversely, a smaller cDNA moiety of 520 bp was amplified from reverse‐transcribed RNA isolated from the hypothalamus, pituitary gland, and extrahypothalamic brain regions. Similarly, several FSH receptor cDNA moieties were amplified in reverse‐transcribed RNA from the testis and ovary, including the 990 bp moiety predicted by the full‐length FSH receptor sequence and a 600 bp cDNA. Only the 600 bp cDNA was detected in the brain and pituitary gland. These results therefore suggest that the FSH and LH receptor genes are expressed in neural and pituitary tissue. However, these receptor transcripts may not encode full‐length receptors. This work was supported by NSERC.
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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 | 0.001 |
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".