Optimizing Differentiation Protocols for Producing Dopaminergic Neurons from Human Induced Pluripotent Stem Cells for Tissue Engineering Applications
Bibliographic record
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder that results when the dopaminergic neurons (DNs) present in the substantia nigra necessary for voluntary motor control are depleted, making patients with this disorder ideal candidates for cell replacement therapy. Human induced pluripotent stem cells (hiPSCs), obtained by reprogramming adult cells, possess the properties of pluripotency and immortality while enabling the possibility of patient-specific therapies. An effective cell therapy for PD requires an efficient, defined method of DN generation, as well as protection from the neuroinflammatory environment upon engraftment. Although similar in pluripotency to human embryonic stem cells (hESCs), hiPSCs differentiate less efficiently into neuronal subtypes. Previous work has shown that treatment with guggulsterone can efficiently differentiate hESCs into DNs. Our work shows that guggulsterone is able to derive DNs from hiPSCs with comparable efficiency, and furthermore, this differentiation can be achieved inside three-dimensional fibrin scaffolds that could enhance cell survival upon engraftment.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".