3-D Bioprinting of Neural Tissue for Applications in Cell Therapy and Drug Screening
Bibliographic record
Abstract
Neurodegenerative diseases affect millions of individuals in North America and cost the health-care industry billions of dollars for treatment. Current treatment options for degenerative diseases focus on physical rehabilitation or drug therapies, which temporarily mask the effects of cell damage, but quickly lose their efficacy. Cell therapies for the central nervous system remain an untapped market due to the complexity involved in growing neural tissues, controlling their differentiation, and protecting them from the hostile environment they meet upon implantation. Designing tissue constructs for the discovery of better drug treatments are also limited due to the resolution needed for an accurate cellular representation of the brain, in addition to being expensive and difficult to translate to biocompatible materials. 3-D printing offers a streamlined solution for engineering brain tissue for drug discovery or, in the future, for implantation. New microfluidic and bioplotting devices offer increased resolution, little impact on cell viability and have been tested with several bioink materials including fibrin, collagen, hyaluronic acid, poly(caprolactone), and poly(ethylene glycol). This review details current efforts at bioprinting neural tissue and highlights promising avenues for future work.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".