GnRH Agonists and Antagonists in Cancer Therapy
Why this work is in the frame
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Bibliographic record
Abstract
Gonadotropin releasing hormone (GnRH) is a hypothalamic decapeptide that binds to GnRH receptors on pituitary gonadotrope cells to modulate the synthesis and secretion of the gonadotropins, luteinizing hormone (LH) and follicle-stimulating hormone (FSH). These gonadotropins in turn regulate gonadal steroidogenesis and gametogenesis. Chemical characterization and structure-activity analysis of GnRH variants containing systematic amino acid substitutions led to the discovery of GnRH superagonists and antagonists. These peptides are widely used for the treatment of clinical conditions in which modulation of or interference with sex hormone production is beneficial to prevent development or progression of benign conditions (e.g. endometriosis, uterine fibroids) or malignant tumors (e.g. breast, ovarian, endometrial and prostate carcinoma). When compared to native GnRH, GnRH superagonists have increased potency for the short-term release of gonadotropins. However, they show paradoxical action in that chronic treatment with superagonists results in inhibition of gonadotropin production as a result of desensitization of the gonadotropes and down regulation of its receptor. In contrast, GnRH antagonists produce a rapid and dose-dependent suppression of gonadotropin release by competitive blockade of the GnRH receptors without any initial stimulatory effect as seen with superagonists. In recent years, a search for peptidomimetic compounds to replace peptides as therapeutic agents has been undertaken to find compounds with higher affinity for the GnRH receptor but do not have the disadvantages of peptides. Such efforts have resulted in the identification and development of small-molecule non-peptide compounds that are sufficiently stable in vivo and possess favorable pharmacological parameters comparable to peptide antagonists. Some of these compounds are being tested in human volunteers and the preliminary results are very encouraging.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it