Cell Therapy for Age-Related Disorders: Myocardial Infarction and Stroke – A Mini-Review
Bibliographic record
Abstract
BACKGROUND: The leading causes of death and disability in the elderly are from cardiovascular and cerebrovascular diseases. The biological role of stem cells in the hematopoietic system has been well characterized and has led to the development of hematopoietic stem and progenitor cell (HSPC) transplantation for the treatment of numerous malignant and nonmalignant diseases. More recently, stem cells have been found in many other tissues of the body including the heart and brain. As the field of stem cell biology has progressed, cell therapies for the treatment of myocardial infarction and stroke have been tested in early stage clinical trials using a variety of cellular agents. OBJECTIVE: To review the clinical evidence supporting the role of cell therapies for myocardial infarction and stroke. METHODS: A systematic review of the literature was conducted to identify clinical trials using cell therapies for myocardial infarction and stroke. RESULTS: Clinical trials of granulocyte colony-stimulating factor to mobilize HSPC after percutaneous coronary intervention for acute myocardial infarction have not shown clinical benefit. Direct delivery of HSPC to coronary arteries supplying the infarcted region using percutaneous coronary intervention does improve hemodynamic endpoints such as left ventricular ejection fraction in many studies. One randomized trial demonstrated improvement in clinically meaningful endpoints such as death, recurrence of myocardial infarction and re-hospitalization for heart failure. Several small trials of cell therapy for stroke have been reported, including cytokine-mobilized HSPC, mesenchymal stromal cells and cell lines transplanted stereotactically into the region affected by stroke. CONCLUSIONS: In some prospective randomized trials, cell therapy for myocardial infarction leads to improvement in hemodynamic parameters. Cell therapy for stroke is a relatively new area of translational and clinical research with preliminary studies showing safety and some measurable benefit in small numbers of subjects.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".