<i>In situ</i>-forming, mechanically resilient hydrogels for cell delivery
Bibliographic record
Abstract
Injectable, in situ-forming hydrogels can improve cell delivery in tissue engineering applications by facilitating minimally invasive delivery to irregular defect sites and improving cell retention and survival. Tissues targeted for cell delivery often undergo diverse mechanical loading including high stress, high strain, and repetitive loading conditions. This review focuses on the development of hydrogel systems that meet the requirements of mechanical resiliency, cytocompatibility, and injectability for such applications. First, we describe the most important design considerations for maintaining the viability and function of encapsulated cells, for reproducing the target tissue morphology, and for achieving degradation profiles that facilitate tissue replacement. Models describing the relationships between hydrogel structure and mechanical properties are described, focusing on design principles necessary for producing mechanically resilient hydrogels. The advantages and limitations of current strategies for preparing cytocompatible, injectable, and mechanically resilient hydrogels are reviewed, including double networks, nanocomposites, and high molecular weight amphiphilic copolymer networks. Finally, challenges and opportunities are outlined to guide future research in this developing field.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".