Caspase Activation in an In-Vitro Model of Parkinson’s Disease
Bibliographic record
Abstract
Parkinson’s disease (PD) is the second most prevalent neurodegenerative disorder worldwide with a key hallmark of its pathology being the degeneration of dopaminergic neurons in the substantia nigra (SNc) due to intraneuronal accumulation of Lewy bodies. Lewy bodies form through the aggregation of the monomer alpha-synuclein protein (aSyn), which can misfold into toxic fibrils that disrupt cellular homeostasis and cause apoptosis. While there is no cure for PD, establishing good in-vitro models is essential to further research of the disease and to developing possible treatments and therapies. This study’s goal was to develop an in-vitro model of PD using SH-SY5Y cells that would allow us to test the properties of our DNA-aptamer, a-syn-1, in preventing aSyn aggregation and development of pathology. This model was created by differentiating the SH-SY5Y cells into N-type neuroblast-like cells using retinoic acid following either Protocol A (full media with 10% FBS) or Protocol B (reduced media with 1% FBS) before adding aSyn filbrils. The cells were then stained with a Caspase-3/7 Green reagent and a NucBlue counterstain and were imaged at 24 h, 48 h, 72 h, and 96 h time points. We found that Protocol A showed no significant caspase activation at any of the timepoints and became overgrown with S-type epithelial-like cells after 94 h. Protocol B showed less cell proliferation and less S-type cells at the 94 h time point. Future studies will perform caspase testing on Protocol B to determine if it is the ideal in-vitro PD model before adding a-syn-1.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 |
| 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".