Aging, Parkinson’s Disease, and Models: What Are the Challenges?
Bibliographic record
Abstract
Parkinson's disease (PD) is a chronic, neurodegenerative condition characterized by motor symptoms such as bradykinesia, rigidity, and tremor, alongside multiple nonmotor symptoms. The appearance of motor symptoms is linked to progressive dopaminergic neuron loss within the substantia nigra. PD incidence increases sharply with age, suggesting a strong association between mechanisms driving biological aging and the development and progression of PD. However, the role of aging in the pathogenesis of PD remains understudied. Numerous models of PD, including cell models, toxin-induced models, and genetic models in rodents and nonhuman primates (NHPs), reproduce different aspects of PD, but preclinical studies of PD rarely incorporate age as a factor. Studies using patient neurons derived from stem cells via reprogramming methods retain some aging features, but their characterization, particularly of aging markers and reproducibility of neuron type, is suboptimal. Investigation of age-related changes in PD using animal models indicates an association, but this is likely in conjunction with other disease drivers. The biggest barrier to drawing firm conclusions is that each model lacks full characterization and appropriate time-course assessments. There is a need to systematically investigate whether aging increases the susceptibility of mouse, rat, and NHP models to develop PD and understand the role of cell models. We propose that a significant investment in time and resources, together with the coordination and sharing of resources, knowledge, and data, is required to accelerate progress in understanding the role of biological aging in PD development and improve the reliability of models to test interventions.
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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 | 0.062 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.012 | 0.027 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.012 | 0.023 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".