Bibliographic record
Abstract
Functional cardiac tissue was prepared using a modular tissue engineering approach with the goal of creating vascularized tissue. Rat aortic endothelial cells (RAEC) were seeded onto submillimeter-sized modules made of type I bovine collagen supplemented with Matrigel™ (25% v/v) embedded with cardiomyocyte (CM)-enriched neonatal rat heart cells and assembled into a contractile, macroporous, sheet-like construct. Modules (without RAEC) cultured in 10% bovine serum (BS) were more contractile and responsive to external stimulus (lower excitation threshold, higher maximum capture rate, and greater en face fractional area changes) than modules cultured in 10% fetal BS. Incorporating 25% Matrigel in the matrix reduced the excitation threshold and increased the fractional area change relative to collagen only modules (without RAEC). A coculture medium, containing 10% BS, low Mg2+ (0.814mM), and normal glucose (5.5mM), was used to maintain RAEC junction morphology (VE-cadherin) and CM contractility, although the responsiveness of CM was attenuated with RAEC on the modules. Macroporous, sheet-like module constructs were assembled by partially immobilizing a layer of modules in alginate gel until day 8, with or without RAEC. RAEC/CM module sheets were electrically responsive; however, like modules with RAEC this responsiveness was attenuated relative to CM-only sheets. Muscle bundles coexpressing cardiac troponin I and connexin-43 were evident near the perimeter of modules and at intermodule junctions. These results suggest the potential of the modular approach as a platform for building vascularized cardiac tissue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".