Direct cellular reprogramming techniques for cardiovascular regenerative therapeutics
Bibliographic record
Abstract
Cardiovascular diseases remain a leading cause of hospitalization affecting approximately 38 million people worldwide. While pharmacological and revascularization techniques can improve the patient's survival and quality of life, they cannot help reversing myocardial infarction injury and heart failure. Direct reprogramming of somatic cells to cardiomyocyte and cardiac progenitor cells offers a new approach to cellular reprogramming and paves the way for translational regenerative medicine. Direct reprogramming can bypass the pluripotent stage with the potential advantage of non-immunogenic cell products, reduced carcinogenic risk, and no requirement for embryonic tissue. The process of directly reprogramming cardiac cells was first achieved through the overexpression of transcription factors such as GATA4, MEF2C, and TBX5. However, over the past decade, significant work has been focused on enhancing direct reprogramming using a mixture of transcription factors, microRNAs, and small molecules to achieve cardiac cell fate. This review discusses the evolution of direct reprogramming, recent progress in achieving efficient cardiac cell fate conversion, and describes the reprogramming mechanisms at a molecular level. We also explore various viral and non-viral delivery methods currently being used to aid in the delivery of reprogramming factors to improve efficiency. However, further studies will be needed to overcome molecular and epigenetic barriers to successfully achieve translational cardiac regenerative therapeutics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".