Modeling Parkinson’s Disease in <i>C. elegans</i>
Bibliographic record
Abstract
Parkinson's disease (PD) is an adult onset neurodegenerative disease that is characterized by selective degeneration of neurons primarily in the substantia nigra. At present, the pathogenesis of PD is incompletely understood and there are no neuroprotective treatments available. Accurate animal models of PD provide the opportunity to elucidate disease mechanisms and identify therapeutic targets. This review focuses on C. elegans models of PD, including both genetic and toxicant models. This microscopic worm offers several advantages for the study of PD including ease of genetic manipulation, ability to complete experiments rapidly, low cost, and ability to perform large scale screens for disease modifiers. A number of C. elegans models of PD have been generated including transgenic worms that express α-synuclein or LRRK2, and worms with deletions in PRKN/pdr-1, PINK1/pink-1, DJ-1/djr-1.1/djr-1.2 and ATP13A2/catp-6. These worms have been shown to exhibit multiple phenotypic deficits including the loss of dopamine neurons, disruption of dopamine-dependent behaviors, increased sensitivity to stress, age-dependent aggregation, and deficits in movement. As a result, these phenotypes can be used as outcome measures to gain insight into disease pathogenesis and to identify disease modifiers. In this way, C. elegans can be used as an experimental tool to elucidate mechanisms involved in PD and to find novel therapeutic targets that can subsequently be validated in other models.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".