Microtechnologies in the Fabrication of Fibers for Tissue Engineering
Bibliographic record
Abstract
Engineering tissues and organs for implantation in the human body or research require the fabrication of constructs that reproduce a physiological environment. Moreover, the construction of complex and sizable three-dimensional tissues requires a precise control over cell distribution and an effective vasculature network to supply oxygen and nutrients, and remove waste. Fiber-based tissue engineering that forms 3D structures using fibers can address many of these challenges, but depends on the quality of the fibers. Recent progresses in microtechnologies have enabled researchers to fabricate biocompatible fibers with advanced biochemical and physical properties, including cell-laden fibers that are pre-seeded with cells. In this chapter, we discuss fiber fabrication techniques including co-axial flow spinning, wetspinning, meltspinning, and electrospinning, which have leveraged microtechnologies to improve their performance. We compare the properties of the fibers fabricated with these methods and discuss their strengths and weaknesses in the context of tissue engineering.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".