Investigation of foldable characteristics and C2C12 cell viability in stimuli-responsive pNIPAM hydrogel matrices
Bibliographic record
Abstract
Stimuli-responsive structures have emerged as pioneering tools by integrating responsiveness to external stimuli into traditional 3D processes. This advancement offers numerous advantages across various fields, with tissue engineering particularly benefiting from incorporating biomimetics into its manufacturing process. By utilizing thermo-responsive bio-hydrogels, which can transform their properties and shapes in response to external stimuli, tissue engineering can potentially overcome significant challenges in producing 3D bio-scaffolds. In this study, we conducted preliminary investigations into the potential of poly (N-isopropyl acrylamide) (pNIPAM)-based hydrogels as substrates for C2C12 cells and evaluated their folding behaviour under different temperatures. We formulated a base composition of pNIPAM hydrogel and examined its suitability as thin substrates for cell sheet production and thicker substrates for potential 3D scaffolding. Our findings revealed that cells adhered and developed on thin hydrogel layers, achieving full confluence while maintaining metabolic activity comparable to those cultured on standard substrates. However, this behaviour was not replicable on thicker pNIPAM substrates due to their low stiffness, an extensive property that needs improvement. In conclusion, our current pNIPAM composition shows strong potential for printing hydrogels to be used as cell substrates for cell sheets, eliminating the need for enzymes in cell detachment. While it also demonstrates promise for application as a 3D scaffold, enhancements in stiffness are necessary to fully realize this potential.
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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 | 0.000 | 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 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".