MétaCan
Menu
Back to cohort
Record W2147937205 · doi:10.1002/biof.1016

Endothelial progenitor cells in diabetes mellitus

2012· review· en· W2147937205 on OpenAlexaff
Lisa Menegazzo, Mattia Albiero, Angelo Avogaro, Gian Paolo Fadini

Bibliographic record

VenueBioFactors · 2012
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsTellabs (Canada)
Fundersnot available
KeywordsVasculogenesisProgenitor cellDiabetes mellitusContext (archaeology)MedicineEndotheliumBone marrowDiseasePopulationEndothelial stem cellType 2 Diabetes MellitusMacrovascular diseaseImmunologyVascular diseaseHomeostasisStem cellInternal medicineBiologyType 2 diabetesEndocrinologyCell biologyIn vitroGenetics

Abstract

fetched live from OpenAlex

Diabetes mellitus is associated with an increased risk of cardiovascular disease due to its negative impact on the vascular endothelium. The damaged endothelium is repaired by resident cells also through the contribution of a population of circulating cells derived from bone marrow. These cells, termed endothelial progenitor cells (EPCs) are involved in maintaining endothelial homeostasis and contributes to the formation of new blood vessels with a process called postnatal vasculogenesis. The mechanisms whereby these cells allow for protection of the cardiovascular system are still unclear; nevertheless, consistent evidences have shown that impairment and reduction of EPCs are hallmark features of type 1 and type 2 diabetes. Therefore, EPC alterations might have a pathogenic role in diabetic complications, thus becoming a potential therapeutic target. In this review, EPC alterations will be examined in the context of macrovascular and microvascular complications of diabetes, highlighting their roles and functions in the progression of the disease.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.036
GPT teacher head0.297
Teacher spread0.261 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations86
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueBioFactorsSame topicAngiogenesis and VEGF in CancerFrench-language works237,207