Tuning the Properties of PNIPAm-Based Hydrogel Scaffolds for Cartilage Tissue Engineering
Bibliographic record
Abstract
Poly(N-isopropylacrylamide) (PNIPAm) is a three-dimensional (3D) crosslinked polymer that can interact with human cells and play an important role in the development of tissue morphogenesis in both in vitro and in vivo conditions. PNIPAm-based scaffolds possess many desirable structural and physical properties required for tissue regeneration, but insufficient mechanical strength, biocompatibility, and biomimicry for tissue development remain obstacles for their application in tissue engineering. The structural integrity and physical properties of the hydrogels depend on the crosslinks formed between polymer chains during synthesis. A variety of design variables including crosslinker content, the combination of natural and synthetic polymers, and solvent type have been explored over the past decade to develop PNIPAm-based scaffolds with optimized properties suitable for tissue engineering applications. These design parameters have been implemented to provide hydrogel scaffolds with dynamic and spatially patterned cues that mimic the biological environment and guide the required cellular functions for cartilage tissue regeneration. The current advances on tuning the properties of PNIPAm-based scaffolds were searched for on Google Scholar, PubMed, and Web of Science. This review provides a comprehensive overview of the scaffolding properties of PNIPAm-based hydrogels and the effects of synthesis-solvent and crosslinking density on tuning these properties. Finally, the challenges and perspectives of considering these two design variables for developing PNIPAm-based scaffolds are outlined.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".