Tuning the Properties of PNIPAm-Based Hydrogel Scaffolds for Cartilage Tissue Engineering
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it