Uterine and placental KISS1 regulate pregnancy: what we know and the challenges that lie ahead
Bibliographic record
Abstract
Hypothalamic KISS1 and its derivatives (kisspeptins) are now well recognized as potent stimulators of GnRH secretion and thereby major regulators of the neuroendocrine-reproductive axis. Recent studies in the mouse strongly suggest that independent of the hypothalamus and pituitary, peripherally derived KISS1 also regulates fertility, and disruption of local KISS1 signaling in the ovary and uterus is sufficient to trigger infertility. With this increasing recognition that peripherally derived KISS1 regulates fertility, the first goal of this review is to critically discuss the data that have led to this conclusion, focusing on uterine- and placental-derived KISS1. Given that a significant amount of this data was generated in animals such as the mouse and rat, a second goal of this review is to identify and discuss the limitations of the animal data in the context of better understanding KISS1 as a regulator of human pregnancy. The growing evidence suggests that in both man and mouse, KISS1 plays an important role in regulating very early pregnancy events such as embryo implantation. However, as pregnancy advances, although it seems that KISS1 continues to play important roles in regulating human pregnancy, it might not do so in the mouse. This surprising functional dichotomy between human females and mice appears also to exist between women and a large number of animal species, including lower primates. These findings are of tremendous significance and will greatly shape how KISS1 will be developed as a therapeutic agent in augmenting the reproductive potential of both women and important livestock species.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".