Smart Hydrogels in Tissue Engineering and Regenerative Medicine
Bibliographic record
Abstract
The field of regenerative medicine has tremendous potential for improved treatment outcomes and has been stimulated by advances made in bioengineering over the last few decades. The strategies of engineering tissues and assembling functional constructs that are capable of restoring, retaining, and revitalizing lost tissues and organs have impacted the whole spectrum of medicine and health care. Techniques to combine biomimetic materials, cells, and bioactive molecules play a decisive role in promoting the regeneration of damaged tissues or as therapeutic systems. Hydrogels have been used as one of the most common tissue engineering scaffolds over the past two decades due to their ability to maintain a distinct 3D structure, to provide mechanical support for the cells in the engineered tissues, and to simulate the native extracellular matrix. The high water content of hydrogels can provide an ideal environment for cell survival, and structure which mimics the native tissues. Hydrogel systems have been serving as a supportive matrix for cell immobilization and growth factor delivery. This review outlines a brief description of the properties, structure, synthesis and fabrication methods, applications, and future perspectives of smart hydrogels in tissue engineering.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".