Developing a bioprinted scaffold‐based model of neurodegeneration for high throughput screening in Drug Development
Bibliographic record
Abstract
Abstract Background A major component of the failure to bring novel drugs for Alzheimer’s disease (AD) to market is the translational gap between in vitro, animal and human studies. Using the novel technology of 3D bio‐printing, this project aims to develop a scaffold‐based 3D quad‐culture model of AD for drug screening using human induced pluripotent stem cells. Method A cell‐hydrogel suspension is “printed” onto 96‐ and 394‐well plates using a 3D bioprinter, which uses drop‐on‐demand technology to allow high resolution placement of the cells. The culture is suspended in a hydrogel of extracellular matrix proteins, which has been modulated to mimic brain biomechanics. Result Protocols to differentiate human neural progenitor cells into cortical neurons, astrocytes and oligodendrocytes have been established from AD mutation lines and healthy controls which express desired markers. These cell types, alongside iPSC‐derived microglia, will form the quad‐culture of cells within the model. Conclusion This model will exercise the benefits of a 3D model, while maintaining the benefits of 2D systems, including reproducibility, reliability, and suitability for high throughput screening.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".