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Record W2145294243 · doi:10.1002/iub.518

Prolactin and vasoinhibins: Endogenous players in diabetic retinopathy

2011· review· en· W2145294243 on OpenAlexaff
Jakob Triebel, Yazmín Macotela, Gonzalo Martı́nez de la Escalera, Carmen Clapp

Bibliographic record

VenueIUBMB Life · 2011
Typereview
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsTellabs (Canada)
FundersConsejo Nacional de Ciencia y Tecnología
KeywordsDiabetic retinopathyProlactinDiabetes mellitusMedicineInternal medicineRetinopathyEndocrinologyHormoneDiseasePathophysiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.950
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.129
GPT teacher head0.311
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations35
Published2011
Admission routes1
Has abstractyes

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