The impact of patient co-morbidities on the regenerative capacity of cardiac explant-derived stem cells
Bibliographic record
Abstract
BACKGROUND: Although patient-sourced cardiac stem cells repair damaged myocardium, the extent to which medical co-morbidities influence cardiac-derived cell products is uncertain. Therefore, we investigated the influence of atherosclerotic risk factors on the regenerative performance of human cardiac explant-derived cells (EDCs). METHODS: In this study, the Long Term Stratification for survivors of acute coronary syndromes model was used to quantify the burden of cardiovascular risk factors within a group of patients with established atherosclerosis. EDCs were cultured from human atrial appendages and injected into immunodeficient mice 7 days post-left coronary ligation. Cytokine arrays and enzyme linked immunoassays were used to determine the release of cytokines by EDCs in vitro, and echocardiography was used to determine regenerative capabilities in vivo. RESULTS: EDCs sourced from patients with more cardiovascular risk factors demonstrated a negative correlation with production of pro-healing cytokines (such as stromal cell derived factor 1α) and exosomes which had negative effects on the promotion of angiogenesis and chemotaxis. Reductions in exosomes and pro-healing cytokines with accumulating medical co-morbidities were associated with increases in production of the pro-inflammatory cytokine interleukin-6 (IL-6) by EDCs. Increased patient co-morbidities were also correlated with significant attenuation in improvements of left ventricular ejection fraction. CONCLUSIONS: The regenerative performance of the earliest precursor cell population cultured from human explant tissue declines with accumulating medical co-morbidities. This effect is associated with diminished production of pro-cardiogenic cytokines and exosomes while IL-6 is markedly increased. Predictors of cardiac events demonstrated a lower capacity to support angiogenesis and repair injured myocardium in a mouse model of myocardial infarction.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".