Thermoelectric Freeze-Casting of Biopolymer Blends: Fabrication and Characterization of Large-Size Scaffolds for Nerve Tissue Engineering Applications
Bibliographic record
Abstract
Peripheral nerve injuries (PNIs) are detrimental to the quality of life of affected individuals. Patients are often left with life-long ailments that affect them physically and psychologically. Autologous nerve transplant is still the gold standard treatment for PNIs despite limited donor site and partial recovery of nerve functions. Nerve guidance conduits are used as a nerve graft substitute and are efficient for the repair of small nerve gaps but require further improvement for repairs exceeding 30 mm. Freeze-casting is an interesting fabrication method for the conception of scaffolds meant for nerve tissue engineering since the microstructure obtained comprises highly aligned micro-channels. The present work focuses on the fabrication and characterization of large scaffolds (35 mm length, 5 mm diameter) made of collagen/chitosan blends by freeze-casting via thermoelectric effect instead of traditional freezing solvents. As a freeze-casting microstructure reference, scaffolds made from pure collagen were used for comparison. Scaffolds were covalently crosslinked for better performance under load and laminins were further added to enhance cell interactions. Microstructural features of lamellar pores display an average aspect ratio of 0.67 ± 0.2 for all compositions. Longitudinally aligned micro-channels are reported as well as enhanced mechanical properties in traction under physiological-like conditions (37 °C, pH = 7.4) resulting from crosslinking treatment. Cell viability assays using a rat Schwann cell line derived from sciatic nerve (S16) indicate that scaffold cytocompatibility is similar between scaffolds made from collagen only and scaffolds made from collagen/chitosan blend with high collagen content. These results confirm that freeze-casting via thermoelectric effect is a reliable manufacturing strategy for the fabrication of biopolymer scaffolds for future peripheral nerve repair applications.
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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".