Inflammatory Microenvironment of Acute Myocardial Infarction Prevents Regeneration of Heart with Stem Cells Therapy
Bibliographic record
Abstract
Over the past years, the benefits of stem cell therapy approach for treatment of the cardiovascular diseases have been shown through the rebuilding of new cardiomyocytes and blood vessels. while a successful regeneration of the myocardium has been proven on the animal models of acute myocardial injuries resulted from the stem cells transplantation, no significant long-term regenerative with autologous stem cell therapy in patients with acute myocardial infarction have been reported based on recent meta-analyses. It seems that the inflammatory microenvironment of acute myocardial infarction has an inhibitory effect on the stem cells potential for regenerating the injured myocardium. Secretion of critical cytokines with pro-inflammatory properties including tumor necrosis factor-α, interleukin-1β, and interleukin-6 as well as induction of hypoxic condition and finally formation of cytotoxic elements cause the cellular death and hinder the stem cells proliferation and differentiation. Based on the evidence, application of some approaches like co-delivery of mesenchymal stem cells with the other useful cells, using the stem cells derived productions, administration of preconditioned and modified cells, and also using the anti-inflammatory agents besides the cell therapy are hypothesized as the primary developed safe and practical approaches for decreasing destructive effects of the inflammation on the implanted stem/progenitor cells. In this review, we critically discuss the quiddity of the inflammatory microenvironment and its promoted mechanisms as the main elements to hinder the efficacy of stem cell therapy in the cases of acute myocardial infarction. Also, we finally propose some applied solutions to the problem of cardiac regeneration with stem cells therapy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".