Parkinson's Disease
Bibliographic record
Abstract
Parkinson’s disease (PD) is a neurodegenerative disease that involves a progressive loss of dopaminergic neurons within the substantia nigra. Patients with PD experience a loss of motor function over time. In normal patients, muscle contractions are driven by action potentials through the binding of the neurotransmitter dopamine. Disruptions to dopaminergic activity would thus result in less efficient contractions. Patients with Parkinson’s can experience akinesia, tremors and other motor dysfunction. Glucocerebrosidase (GCase), specifically β‐GCase, is a lysosomal enzyme involved in sphingolipid metabolism. β‐GCase falls under the hydrolase classification, allowing it to catabolize glucosylceramide (GlcCer) into glucose and ceramide. β‐GCase consists of 497 amino acid glycoproteins within its four domains. The glucocerebrosidase gene (GBA) mutations are often associated with Gaucher disease. However, β‐GCase synthesized from mutated GBA1 is often studied as a biomarker for PD susceptibility. In a healthy cell with the non‐mutated GBA gene, the GBA gene would be transcribed into mRNA and then transported out to the rough Endoplasmic Reticulum, where GCase is then synthesized. Lysosomal integral membrane protein‐2 (LIMP2) is a protein that transports GCase through the Golgi Apparatus and transfers the β‐GCase into a late endosome. When the late endosome fuses with a lysosome, where β‐GCase can hydrolyze its substrates. Mutated β‐GCase may affect this autophagy pathway. This research will explore how mutated β‐GCase may disturb lysosomal functions, which would consequently lead to the aggregation of alpha‐synuclein (ASN), a protein that regulates synaptic vesicle trafficking and subsequently releases dopamine. Therefore, the detection and accumulation of mutated lysosomal enzymes, such as β‐GCase, in the cerebrospinal fluid might serve as biomarkers for Parkinson’s Disease.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.018 |
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".