Material design and photo-regulated hydrolytic degradation behavior of tissue engineering scaffolds fabricated via 3D fiber deposition
Bibliographic record
Abstract
An ideal behavior of a tissue engineering scaffold is that it degrades and reshapes at a rate that matches the formation of new tissues. However, this ideal situation may not occur as the scaffold often undergoes too slow or too fast degradation. To test the promise of the active control of scaffold degradation, in this work, a photo/water dual-degradable porous scaffold was designed and fabricated using a 3D fiber deposition (3DF) system from a linear biopolymer (named PLANB) that combined the o-nitrobenzyl linkages and hydrolysable ester bone in the polymer chains. The chemical structure, molecular weight and polydispersity of PLANB were characterized by IR, NMR, GPC and MALDI-TOF-MS. The thermal properties of PLANB evaluated by DSC and TGA assays enabled a 3DF printing at the temperature around its melting point without chemical changes. The introduction of a real-time IR (RTIR) technique not only facilitated the determination of photolysis kinetics and quantum yield, but also enabled the capture of intermediate products during the photo-cleavage process of PLANB scaffolds. A minute-scale daily photolysis combined with a continuous hydrolysis process was implemented to test the photo-regulated hydrolytic degradation behavior of PLANB scaffolds in vitro, and the results obtained from both the SEM image and the mass loss profile demonstrated a porous-void microstructure along the strands of scaffolds and an apparent increase of the mass loss amount compared with the control group without photo-irradiation. Furthermore, PLANB scaffolds showed low cytotoxicity to L929 cells and performed well in promoting cell adhesion. It can therefore be concluded that such scaffolds have great potential in offering a diverse range of control over degradation kinetics of tissue engineering scaffolds to be tailored to individual tissue regeneration situations.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".