3D Bioprinting cell-laden bioinks for engineering neural tissues and potential models for Parkinson’s disease
Bibliographic record
Abstract
Parkinson's disease (PD) is the second most common age-related neurodegenerative disorder after Alzheimer's disease, affecting over ten million people worldwide. It is characterized by motor symptoms such as tremors, rigidity, and gait disturbances. Current treatments focus on alleviating symptoms and slowing down brain degeneration, but no cure exists, leading to a progressive decline in patients' quality of life. Three-dimensional (3D) bioprinting has emerged as a powerful technique for developing constructs that engineer neural tissues with complexities mimicking physiological conditions. These constructs can serve as vehicles for controlled drug delivery and potential substitutes for neurodegeneration. This article aims to compile new research data and review the current state of PD models engineered by 3D bioprinting, focusing on the desired biochemical features of bioinks for cell protection during printing, cell behavior, and differentiation into 3D constructs. Additionally, it discusses the physical, mechanical, and chemical characterization of bioprinted scaffolds and the importance of post-printing assessment to ensure printability, shape fidelity, appropriate construct degradation, and extracellular matrix production rates for developing complex 3D bioprinted constructs. Finally, it proposes opportunities for models that can be used to study novel therapeutics and immunomodulatory responses in tissues engineered for PD and other neurodegenerative diseases.
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How this classification was reachedexpand
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".