Enhancement of the survival of engrafted mesenchymal stem cells in the ischemic heart by TNFR gene transfectionThis paper is one of a selection of papers published in this special issue entitled “Second International Symposium on Recent Advances in Basic, Clinical, and Social Medicine” and has undergone the Journal's usual peer review process.
Bibliographic record
Abstract
Autologous or allogeneic mesenchymal stem cells (MSCs) have been used as one of the potential cell sources for cellular cardiomyoplasty. The adverse microenvironment in acute myocardial infarction, however, is considered a deleterious factor for MSC transplantation and cell survival. Tumor necrosis factor (TNF)-alpha is an inflammatory mediator produced during ischemia that may affect the survival of MSCs. In this study, we investigated the enhancement of MSC survival by transfecting cells with the TNF receptor (TNFR) gene, leading to the overproduction of TNFR and the binding of TNF-alpha. Rats with acute myocardial infarction, induced by the occlusion of the left coronary artery, were transplanted with MSC or MSC-TNFR. After 2 weeks of acute myocardial infarction, cardiac function was assessed. Engrafted MSC survival and localization of TNF-alpha protein in infarction myocardium were evaluated. The levels of TNF-alpha and TNFR in the infarction zone were assessed. The results indicate that MSC-TNFR transplantation (1) improved left ventricular function; (2) enhanced engrafted MSC survival in the infarcted myocardium; (3) attenuated the level of TNF-alpha in serum and cardiac tissue; and (4) increased TNFR protein production in the infarcted myocardium. Our results showed that MSC modified by the TNFR gene improved cell viability and thereby has the potential to improve the efficiency of MSC transplantation therapy in the ischemic heart.
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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".