Regulation of Transplanted Cell Homing by FGF1 and PDGFB after Doxorubicin Myocardial Injury
Bibliographic record
Abstract
There is no effective treatment for the total recovery of myocardial injury caused by an anticancer drug, doxorubicin (Dox). In this study, using a Dox-induced cardiac injury model, we compared the cardioprotective effects of ventricular cells harvested from 11.5-day old embryonic mice (E11.5) with those from E14.5 embryos. Our results indicate that tail-vein-infused E11.5 ventricular cells are more efficient at homing into the injured adult myocardium, and are more angiogenic, than E14.5 ventricular cells. In addition, E11.5 cells were shown to mitigate the cardiomyopathic effects of Dox. In vitro, E11.5 ventricular cells were more migratory than E14.5 cells, and RT-qPCR analysis revealed that they express significantly higher levels of cytokine receptors Fgfr1, Fgfr2, Pdgfra, Pdgfrb and Kit. Remarkably, mRNA levels for Fgf1, Fgf2, Pdgfa and Pdgfb were also found to be elevated in the Dox-injured adult heart, as were the FGF1 and PDGFB protein levels. Addition of exogenous FGF1 or PDGFB was able to enhance E11.5 ventricular cell migration in vitro, and, whereas their neutralizing antibodies decreased cell migration. These results indicate that therapies raising the levels of FGF1 and PDGFB receptors in donor cells and or corresponding ligands in an injured heart could improve the efficacy of cell-based interventions for myocardial repair.
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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) | 0.001 | 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".