Mesenchymal stromal cell therapy to promote cardiac tissue regeneration and repair
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review focuses on articles published from January 2015 to June 2016 on mesenchymal stromal cell (MSC) therapy for cardiac regeneration and repair. RECENT FINDINGS: During this period, reports published on MSCs address the best MSC tissue source for cellular therapy, mechanisms of MSC activity and improving MSC longevity, and homing in vivo. Currently, there is no definitive therapeutic advantage of any one tissue-derived MSC over another, and even combination therapies struggle with conflicting outcomes. MSC activity, persistence in vivo, or homing can be improved by priming strategies, genetic modification, or biomaterials. Despite numerous studies showing improvement in heart function after acute cardiac injury, the reproducibility and efficacy of the therapy remains elusive and falls short of expectations in clinical trials. Although the safety of MSCs is undisputed, the success of MSC preparations in improving cardiac function clinically remains uncertain due to challenges in correlating MSC potency with clinical outcomes, donor-related variation in MSC function, and a profusion of culture methodologies. SUMMARY: Several strategies are available to advance MSC cell therapy for acute cardiac injury to promote cardiac regeneration and repair in rigorous preclinical and clinical studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".