3D Printing of Neural Tissues Derived from Human Induced Pluripotent Stem Cells Using a Fibrin-Based Bioink
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
3D bioprinting offers the opportunity to automate the process of tissue engineering, which combines biomaterial scaffolds and cells to generate substitutes for diseased or damaged tissues. These bioprinting methods construct tissue replacements by positioning cells encapsulated in bioinks into specific locations in the resulting constructs. Human induced pluripotent stem cells (hiPSCs) serve as an important tool when engineering neural tissues. These cells can be expanded indefinitely and differentiated into the cell types found in the central nervous systems, including neurons. One common method for differentiating hiPSCs into neural tissue requires the formation of aggregates inside of defined diameter microwells cultured in chemically defined media. However, 3D bioprinting of such hiPSC-derived aggregates has not been previously reported in the literature, as it requires the development of specialized bioinks for supporting cell survival and differentiation into mature neural phenotypes. Here we detail methods including preparing base material components of the bioink, producing the bioink, and the steps involved in printing 3D neural tissues derived from hiPSC-derived neural aggregates using Aspect Biosystems' novel RX1 printer and their lab-on-a-printer (LOP) technology.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it