Directed assembly of cell-laden hydrogels for engineering functional tissues
Bibliographic record
Abstract
Tissue engineering aims to develop functionalized tissues for organ replacement or restoration. Biodegradable scaffolds have been used in tissue engineering to support cell growth and maintain mechanical and biological properties of tissue constructs. Ideally cells on these scaffolds adhere, proliferate, and deposit matrix at a rate that is consistent with scaffold degradation. However, the cellular rearrangement within these scaffolds often does not recapitulate the architecture of the native tissues. Directed assembly of tissue-like structures is an attractive alternative to scaffold-based approach for tissue engineering which potentially can build tissue constructs with biomimetic architecture and function. In directed assembly, shape-controlled microstructures are fabricated in which organized structures of different cell types can be used as tissue building blocks. To fabricate tissue building blocks, hydrogels are commonly used as biomaterials for cell encapsulation to mimic the matrix in vivo. The hydrogel-based tissue building blocks can be arranged in pre-defined architectures by various directed tissue assembly techniques. In this paper, recent advances in directed assembly-based tissue engineering are summarized as an emerging alternative to meet challenges associated with scaffold-based tissue engineering and future directions are addressed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".