Angioadaptive Allies: Relationship Between Human Primary Endothelial Cells And Human Skeletal Muscle Myofibroblasts
Bibliographic record
Abstract
Angioadaptation is the ability of capillaries to adapt to physiological changes. This is influenced by interactions between endothelial cells (EC) and supporting cells such as myofibroblasts (MF). Objective We examined a possible pro‐angiogenic paracrine interaction between newly identified MF progenitors in human skeletal muscle and primary EC, and how this interaction may be altered under hyperglycemic conditions. Methods Human skeletal muscle MF progenitor cells (CD90+) were sorted by FACS and differentiated into MF using TGFb under normo‐(NG) or hyperglycemic (HG) conditions. The expression level of 55 angioadaptive proteins was measured by proteome array in the conditioned media (secretome). Human microvascular EC were treated with secretome. VEGF‐A and TSP‐1 mRNA levels were measured by qPCR in these cells. EC migration was evaluated in the Boyden Chamber assay. Results The secretome from differentiated MF was enriched in pro‐angiogenic factors compared to CD90 + cells. VEGF‐A mRNA expression was increased in EC when treated with MF secretome and their migration was stimulated. In contrast, CD90 + cell differentiation under HG resulted in decreased pro‐angiogenic factor secretion in MF secretome and reduced EC migration. Conclusion MF exert some pro‐angiogenic stimulation on primary EC compared to progenitor cells. This pro‐angiogenic effect is attenuated when cell differentiation occurs under HG. Funding: CIHR/NSERC.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".