Gene therapy for Parkinson’s disease: a new frontier in neurodegenerative diseases
Bibliographic record
Abstract
Parkinson’s disease (PD) is a devastating neurodegenerative condition characterized primarily by the degeneration of the dopaminergic neurons in the substantia nigra, causing motor dysfunction and many non-motor symptoms. Available pharmacological treatments and therapies provide symptomatic relief but do not halt the progression of PD. Gene therapy has been recognized as a valuable therapeutic frontier, providing the possibility of disease modification by targeting the underlying molecular and cellular mechanisms of PD. The parts of the methodology used for gene therapy entail the delivery of genetic material into particular regions of the brain with the aid of viral vectors to improve the synthesis of dopamine, maintain the integrity of neurons, or control pathological pathways. Recent clinical trials have shown promising efficacy and safety profiles for many gene therapy methods, consisting of those targeting enzymes in the biosynthesis of dopamine [e.g., L-amino acid decarboxylase (AADC)], synuclein alpha pathology, and neurotrophic factors [e.g., growth-derived neurotrophic factor (GDNF)]. However, in spite of these developments, there are limitations in vector delivery and prolonged expression of genes, as well as patient-specific responses. This review highlights the present landscape of gene therapy in PD, discussing the latest successes, ongoing clinical trials, and future perspectives that could shape therapeutic paradigms for PD.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".