3D Bioprinting Human Induced Pluripotent Stem Cell-Derived Neural Tissues Using a Novel Lab-on-a-Printer Technology
Bibliographic record
Abstract
Most neurological diseases and disorders lack true cures, including spinal cord injury (SCI). Accordingly, current treatments only alleviate the symptoms of these neurological diseases and disorders. Engineered neural tissues derived from human induced pluripotent stem cells (hiPSCs) can serve as powerful tools to identify drug targets for treating such diseases and disorders. In this work, we demonstrate how hiPSC-derived neural progenitor cells (NPCs) can be bioprinted into defined structures using Aspect Biosystems’ novel RX1 bioprinter in combination with our unique fibrin-based bioink in rapid fashion as it takes under 5 min to print four tissues. This printing process preserves high levels of cell viability (>81%) and their differentiation capacity in comparison to less sophisticated bioprinting methods. These bioprinted neural tissues expressed the neuronal marker, βT-III (45 ± 20.9%), after 15 days of culture and markers associated with spinal cord (SC) motor neurons (MNs), such as Olig2 (68.8 ± 6.9%), and HB9 (99.6 ± 0.4%) as indicated by flow cytometry. The bioprinted neural tissues expressed the mature MN marker, ChaT, after 30 days of culture as indicated by immunocytochemistry. In conclusion, we have presented a novel method for high throughput production of mature hiPSC-derived neural tissues with defined structures that resemble those found in the SC.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".