Alleviation of Estrogen-Induced Hyperprolactinemia through Intracerebral Transplantation of Hypothalamic Tissue Containing Dopaminergic Neurons
Bibliographic record
Abstract
Prolactin-secreting pituitary tumors can be induced in young rats through prolonged estrogen treatment. Recent evidence suggests that such tumors are associated with a degeneration of tuberoinfundibular dopaminergic (TI-DA) neurons, which normally inhibit prolactin secretion by the anterior pituitary's lactrophs. For this study, chronic hyperprolactinemia was induced in young, ovariectomized Fisher 344 rats through Silastic capsule implants of 17 beta-estradiol, placed subcutaneously for 1 month prior to removal. Rats with such estrogen-induced hyperprolactinemia then received transplants of neonatal arcuate-median eminence (ME) tissue (containing TI-DA neurons) or amygdala (control) tissue, placed either within the third ventricle or bilaterally within the hypothalamus. Blood samples were obtained 1 month after transplantation and prolactin concentrations measured by radioimmunoassay. Two of 4 animals receiving ventricularly-placed arcuate-ME transplants and 4 of 7 animals receiving bilateral arcuate-ME transplants showed substantial reductions in plasma prolactin levels compared to mean values in control animals. Follow-up catecholamine (CA) histochemistry indicated a bright fluorescence intensity in the median eminence of animals remaining hyperprolactinemic with ineffective transplants. Furthermore, in sharp contrast to the very low, nonpulsatile LH levels found during a second bleeding in recipients bearing ineffective transplants, recipients with effective arcuate-ME transplants had the high, pulsatile levels of LH characteristic of normal, ovariectomized rats. These data suggest that developing TI-DA neurons, within effective arcuate-ME transplants, became functional to reinstate or accentuate DA inhibition of prolactin secretion and, in so doing, indirectly normalized LH secretion as well.
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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) | 0.001 | 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".