Pure chitosan microfluidic spinning affords modular core-sheath fibers and hand-crafted 3D scaffolds with enhanced fibroblasts compatibility
Bibliographic record
Abstract
Microfluidic spun hydrogel fibers are appealing for tissue engineering and cell transplantation applications because they can feature hierarchical organization, have the ability to be woven or self-assembled into macro-objects, and can be easily functionalized or used for cell or chemical encapsulation. They have been developed as templates for reconstructing fiber-shaped tissues and mimic blood vessels, muscle fibers or neural networks in vivo. Alginates are overwhelmingly employed its core material to fabricate continuous hydrogel microfiber substrates, because of the simplicity of their processing. They, however, suffer from poor cell adherence and weak cell-matrix interactions. Alginates also need crosslinking, and thus easily lose shape and leach ions in the physiological environment. To overcome these challenges, we are reporting herein the first synthesis of pure chitosan fibers by microfluidics, avoiding the use of any crosslinking agent. These fibers have smooth surfaces and excellent cell viability of 85%, in the absence of any special coating, and a comparatively higher mechanical strength (695 MPa) than known alginate ones (2-4MPa). Our system also demonstrated the synthesis of chitin nanocrystals/chitosan composite microfibers with a smooth surface and crystals visible in the cross-section. Chitosan is a natural biopolymer obtained from the deacetylation of chitin, which is found in crustacean shells, insect cuticles, and fungi, and thus represents a sustainable source. Therefore, working with chitosan-derived materials helps us to address United Nations Sustainable Development Goals (UN SDGs) 6 and 14. The fibers of pure chitosan and chitosan composite exhibit high processability and can be woven into a variety of structures. These micro structured chitosan fibers have the potential to be used as templates to create fiber-shaped tissues or to develop into live building blocks for the assembly of very complex artificial tissues.
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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) | 0.001 | 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) | 0.001 | 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 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".