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Record W4406759011 · doi:10.1016/j.stemcr.2024.102394

Microvessel co-transplantation improves poor remuscularization by hiPSC-cardiomyocytes in a complex disease model of myocardial infarction and type 2 diabetes

2025· article· en· W4406759011 on OpenAlexafffund
Xuetao Sun, Jun Wu, Omar Mourad, Ren‐Ke Li, Sara S. Nunes

Bibliographic record

VenueStem Cell Reports · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiac Ischemia and Reperfusion
Canadian institutionsToronto Rehabilitation InstituteToronto General HospitalUniversity Health Network
FundersCanadian Institutes of Health Research
KeywordsBiologyMyocardial infarctionDiseaseTransplantationType 2 diabetesMicrovesselInternal medicineDiabetes mellitusCardiologyCancer researchAngiogenesisEndocrinologyMedicine

Abstract

fetched live from OpenAlex

People with type 2 diabetes (T2D) are at a higher risk for myocardial infarction (MI) than age-matched healthy individuals. Here, we studied cell-based cardiac regeneration post MI in T2D rats modeling the co-morbid conditions in patients with MI. We recapitulated the T2D hallmarks and clinical aspects of diabetic cardiomyopathy using high-fat diet and streptozotocin in athymic rats, which were then subjected to MI and intramyocardial implantation of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) with or without rat adipose-derived microvessels (MVs). hiPSC-CM alone engrafted poorly. Co-delivery of hiPSC-CMs with MVs yielded a smaller infarct area and a thicker left ventricle wall. Additionally, MVs robustly integrated into the infarcted hearts, improved the survival of hiPSC-CMs, and improved cardiac function. MV-conditioned media also promoted hiPSC-CM maturation in vitro, increasing cardiomyocyte (CM) size in an interleukin (IL)-6-dependent manner. Given the availability of MVs from human adipose tissue, MVs present great translational potential for the treatment of heart failure in people with T2D.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.507

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 head0.007
GPT teacher head0.235
Teacher spread0.228 · 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

Citations4
Published2025
Admission routes2
Has abstractyes

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