Can We Engineer a Human Cardiac Patch for Therapy?
Bibliographic record
Abstract
Some of the most significant leaps in the history of modern civilization-the development of article in China, the steam engine, which led to the European industrial revolution, and the era of computers-have occurred when science converged with engineering. Recently, the convergence of human pluripotent stem cell technology with biomaterials and bioengineering have launched a new medical innovation: functional human engineered tissue, which promises to revolutionize the treatment of failing organs including most critically, the heart. This compendium covers recent, state-of-the-art developments in the fields of cardiovascular tissue engineering, as well as the needs and challenges associated with the clinical use of these technologies. We have not attempted to provide an exhaustive review in stem cell biology and cardiac cell therapy; many other important and influential reports are certainly merit but already been discussed in several recent reviews. Our scope is limited to the engineered tissues that have been fabricated to repair or replace components of the heart (eg, valves, vessels, contractile tissue) that have been functionally compromised by diseases or developmental abnormalities. In particular, we have focused on using an engineered myocardial tissue to mitigate deficiencies in contractile function.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".