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Record W4396823126 · doi:10.1093/stcltm/szae025

Endothelial progenitor cells for diabetic cardiac and kidney disease

2024· article· en· W4396823126 on OpenAlexaff
Matthew J. Raleigh, Sachin Vidur Pasricha, Aaron Nauth, Michael R. Ward, Kim A. Connelly

Bibliographic record

VenueStem Cells Translational Medicine · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsWestern UniversityLondon Health Sciences CentreUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsDiabetes mellitusProgenitor cellMedicineDiseaseKidney diseaseEndothelial dysfunctionKidneyEndothelial progenitor cellEndothelial stem cellBioinformaticsProgenitorIntensive care medicineStem cellPathologyInternal medicineBiologyEndocrinologyCell biology

Abstract

fetched live from OpenAlex

The management of diabetes mellitus and its resultant end organ dysfunction represents a major challenge to global health-care systems. Diabetic cardiac and kidney disease commonly co-occur and are significant contributors to the morbidity and mortality of patients with diabetes, carrying a poor prognosis. The tight link of these parallel end organ manifestations suggests a deeper common underlying pathology. Here, we outline the mechanistic link between diabetic cardiac and kidney disease, providing evidence for the role of endothelial dysfunction in both processes and the potential for cellular therapy to correct these disorders. Specifically, we review the preclinical and clinical evidence for endothelial progenitor cell therapy in cardiac, kidney, and cardio-renal disease applications. Finally, we outline novel approaches to endothelial progenitor cell therapy through cell enhancement and the use of extracellular vesicles, discussing published and future work.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.684
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.256
Teacher spread0.244 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations9
Published2024
Admission routes1
Has abstractyes

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