Multilineage Constructs for Scaffold‐Based Tissue Engineering: A Review of Tissue‐Specific Challenges
Bibliographic record
Abstract
There is a growing interest in the regeneration of tissue in interfacial regions, where biological, physical, and chemical attributes vary across tissue type. The simultaneous use of distinct cell lineages can help in developing in vitro structures, analogous to native composite tissues. This literature review gathers the recent reports that have investigated multiple cell types of various sources and lineages in a coculture system for tissue-engineered constructs. Such studies aim at mimicking the native organization of tissues and their interfaces, and/or to improve the development of complex tissue substitutes. This paper thus distinguishes itself from those focusing on technical aspects of coculturing for a single specific tissue. The first part of this review is dedicated to variables of cocultured tissue engineering such as scaffold, cells, and in vitro culture environment. Next, tissue-specific coculture methods and approaches are covered for the most studied tissues. Finally, cross-analysis is performed to highlight emerging trends in coculture principles and to discuss how tissue-specific challenges can inspire new approaches for regeneration of different interfaces to improve the outcomes of various tissue engineering strategies.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".