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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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 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".