Prolactin and vasoinhibins: Endogenous players in diabetic retinopathy
Bibliographic record
Abstract
Diabetic retinopathy is a disease of the retinal microvasculature that develops as a complication of diabetes mellitus and constitutes a major cause of blindness in adults of all ages. Diabetic retinopathy is characterized by the loss of capillary cells leading to increased vasopermeability, ischemia, and hypoxia that trigger the excessive formation of new blood vessels in the retina. The influence of the pituitary gland in the pathophysiology of diabetic retinopathy was recognized nearly six decades ago, but the contribution of pituitary hormones to this disease remains unclear. Recent studies have shown that the pituitary hormone prolactin is proteolytically cleaved to vasoinhibins, a family of peptides with potent antivasopermeability, vasoconstrictive, and antiangiogenic actions that can protect the eye against the deleterious effects of the diabetic state. In this review, we summarize what is known about the changes in the circulating levels of prolactin and vasoinhibins during diabetes and diabetic retinopathy as well as the implications of these changes for the development and progression of the disease with particular attention to hyperprolactinemia in pregnancy and postpartum. We discuss the effects of prolactin and vasoinhibins that may impact diabetic retinopathy and suggest these hormones as important targets for therapeutic interventions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".