Investigating the ageing-Parkinson’s disease nexus: standardisation of in vitro models and techniques by the PD-AGE network
Bibliographic record
Abstract
Ageing is the primary risk factor for Parkinson's disease, yet the intricate interplay between these processes remains ambiguous. This position paper, a collaborative output from the PD-AGE consortium, addresses the urgent need for standardising methods in in vitro modelling. A panel of international experts recommends human induced pluripotent stem cell (iPSC)-derived models, with chemically induced ageing methods, such as the SLO cocktail, as a robust system. Furthermore, the consortium highlights the value of direct and semi-direct reprogramming for retaining donor-specific ageing phenotypes. The paper also outlines a prioritised panel of measurable parameters, categorised into senescence, inflammaging, omics profiling, and mitochondrial dysfunction, providing a consistent framework to enhance research reproducibility, investigating the nexus of ageing and Parkinson's. In addition, we provide links to SOPs ( https://doi.org/10.5281/zenodo.15056603 ) [1] to measure the key measurable ageing parameters outlined in this review to facilitate consistency and reproducibility within the field.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".