Fiber-based microphysiological systems: a powerful tool for high throughput drug screening
Bibliographic record
Abstract
Abstract: The growing demand for improvements in in vitro drug screening, disease modelling, and regenerative tissue therapies poses an urgent clinical need for the continued development of innovative tissue engineering strategies. Several promising biofabrication and aggregation solutions have been developed to overcome these challenges, such as three-dimensional (3D) bioprinting and chip-based physiological models. However, replicating the structural and cytoarchitectural complexities of native tissue types to achieve biomimicry of epithelial, nervous, muscular, and connective tissues remains a significant area of investigation. Innovative research efforts focused on the thread-like morphologies of bodily tissues have been developed to overcome these challenges. This review features the recent and current work done in fiber fabrication methods and the associated textile assembly techniques utilized to create fiber-based microphysiological systems, specifically for applications in disease modelling and high throughput drug testing. The advantages and disadvantages of these different fiber fabrication and assembly approaches are compared. Finally, the applications of these textile-based biofabrication approaches in epithelial, nervous, connective, and muscle tissue engineering are reviewed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".