Enhanced cell transplantation: preventing apoptosis increases cell survival and ventricular function
Bibliographic record
Abstract
Cell transplantation prevents cardiac dysfunction after myocardial infarction. However, because most implanted cells are lost to ischemia and apoptosis, the benefits of cell transplantation on heart function could be improved by increasing cell survival. To examine this possibility, male Lewis rat aortic smooth muscle cells (SMCs; 4 x 10(6)) were pretreated with antiapoptotic Bcl-2 gene transfection or heat shock and then implanted into the infarcted myocardium of anesthetized, syngenic female rats (n = 23 per group). On the first day after transplantation, apoptotic SMCs were quantified by using transferase-mediated dUTP nick-end labeling staining. On days 7 and 28, grafted cell survival was quantified by using real-time PCR, and heart function was assessed with the use of echocardiography and the Langendorff apparatus. SMCs given antiapoptotic pretreatments exhibited improvements in each measure relative to controls. Apoptosis was reduced in Bcl-2-treated cells relative to all other groups (P < 0.05), whereas survival (P < 0.01) was increased. Heat shock also significantly decreased apoptosis and increased survival relative to control groups (P < 0.05 for group effect), although these effects were less pronounced than in the Bcl-2-treated group. Further, scar areas were reduced in both Bcl-2- and heat shock-treated groups relative to controls (P < 0.05), and fractional area change and cardiac function were greater (P < 0.05 for both measures). These results indicate that antiapoptosis pretreatments reduced grafted SMC loss after transplantation and enhanced grafted cell survival and ventricular function, which was directly related (r = 0.72; P = 0.002) to the number of surviving engrafted cells.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".