z‐Wire: A Microscaffold That Supports Guided Tissue Assembly and Intramyocardium Delivery for Cardiac Repair
Bibliographic record
Abstract
Tissue engineering holds promise to replace damaged tissues for repair of vital organs in the human body. In cardiac repairs specifically, approaches are developed for intramyocardial delivery of cells and the epicardial delivery of tissue-engineered cardiac patches, providing benefit of cell localization and tissue structure, respectively. However, to improve cell retention and integration, there is a need for the intramyocardial delivery of functional tissues while preserving anisotropic muscle alignment. Here, a biodegradable z-wire scaffold that supports the scalable gel-free production of an array of functional cardiac tissues in a 384-well plate format is developed. The z-wire scaffold design supports cellular alignment, provides tunable mechanical support, and allows for tissue contraction. When the scaffold is imparted with magnetic properties, individual tissues can be assembled with macroscopic alignment under magnetic guidance. When used in combination with a customized surgical delivery tool, z-wire tissues can be injected directly into the myocardial wall, with controlled tissue orientation according to the injection path. This modular tissue engineering approach, in combination with the use of smart scaffolds, can expand opportunity in functional tissue delivery.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".