One Step Into the Future: New iPSC Tools to Advance Research in Parkinson’s Disease and Neurological Disorders
Bibliographic record
Abstract
Studying Parkinson's disease (PD) in the laboratory presents many challenges, the main one being the limited availability of human cells and tissue from affected individuals. As PD is characterized by a loss of dopaminergic (DA) neurons in the brain, it is nearly impossible for researchers to access and extract these cells from living patients. Thus, in the past PD research has focused on the use of patients' post-mortem tissues, animal models, or immortalized cell lines to dissect cellular pathways of interest. While these strategies deepened our knowledge of pathological mechanisms in PD, they failed to faithfully capture key mechanisms at play in the human brain. The emergence of induced pluripotent stem cell (iPSC) technology is revolutionizing PD research, as it allows for the differentiation and growth of human DA neurons in vitro, holding immense potential not only for modelling PD, but also for identifying novel therapies. However, to reproduce the complexity of the brain's environment, researchers are recognizing the need to further develop and refine iPSC-based tools. In this review, we provide an overview of different systems now available for the study of PD, with a particular emphasis on the potential and limitations of iPSC as research tools to generate more relevant models of PD pathophysiology and advance the drug discovery process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".