REVIEW: Oxytocin: Crossing the Bridge between Basic Science and Pharmacotherapy
Bibliographic record
Abstract
Is oxytocin the hormone of happiness? Probably not. However, this small nine amino acid peptide is involved in a wide variety of physiological and pathological functions such as sexual activity, penile erection, ejaculation, pregnancy, uterus contraction, milk ejection, maternal behavior, osteoporosis, diabetes, cancer, social bonding, and stress, which makes oxytocin and its receptor potential candidates as targets for drug therapy. In this review, we address the issues of drug design and specificity and focus our discussion on recent findings on oxytocin and its heterotrimeric G protein-coupled receptor OTR. In this regard, we will highlight the following topics: (i) the role of oxytocin in behavior and affectivity, (ii) the relationship between oxytocin and stress with emphasis on the hypothalamo-pituitary-adrenal axis, (iii) the involvement of oxytocin in pain regulation and nociception, (iv) the specific action mechanisms of oxytocin on intracellular Ca²(+) in the hypothalamo neurohypophysial system (HNS) cell bodies, (v) newly generated transgenic rats tagged by a visible fluorescent protein to study the physiology of vasopressin and oxytocin, and (vi) the action of the neurohypophysial hormone outside the central nervous system, including the myometrium, heart and peripheral nervous system. As a short nine amino acid peptide, closely related to its partner peptide vasopressin, oxytocin appears to be ideal for the design of agonists and antagonists of its receptor. In addition, not only the hormone itself and its binding to OTR, but also its synthesis, storage and release can be endogenously and exogenously regulated to counteract pathophysiological states. Understanding the fundamental physiopharmacology of the effects of oxytocin is an important and necessary approach for developing a potential pharmacotherapy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".