Reduction of alpha-synuclein oligomers in preclinical models of Parkinson’s disease by electrical stimulation in vitro and deep brain stimulation in vivo
Bibliographic record
Abstract
BACKGROUND: Deep brain stimulation (DBS) has been widely used to manage debilitating neurological symptoms in movement disorders such as Parkinson's disease (PD). Despite its well-established symptomatic benefits, our understanding of the mechanisms underlying DBS and its possible effect on the accumulation of pathological proteins in neurodegeneration remains limited. Accumulation and oligomerization of the protein alpha-synuclein (α-Syn) are implicated in the loss of dopaminergic neurons in the substantia nigra in PD, making α-Syn a potential therapeutic target for disease modification. OBJECTIVE: We examined the effects of high frequency electrical stimulation on α-Syn levels and oligomerization in cell and rodent models. METHODS: High frequency stimulation, mimicking DBS parameters used for PD, was combined with viral-mediated overexpression of α-Syn in cultured rat primary cortical neurons or in substantia nigra of rats. Bimolecular protein complementation with split fluorescent protein reporters was used to detect and quantify α-Syn oligomers. RESULTS: High frequency electrical stimulation reduced the expression of PD-associated mutant α-Syn and mitigated α-Syn oligomerization in cultured neurons. Furthermore, DBS in the substantia nigra, but not the subthalamic nucleus, decreased overall levels of α-Syn, including oligomer levels, in the substantia nigra. CONCLUSIONS: Taken together, our results demonstrate that direct high frequency stimulation can reduce accumulation and pathological forms of α-Syn in cultured neurons in vitro and in substantia nigra in vivo. Thus, DBS therapy could have a role beyond symptomatic treatment, with potential disease-modifying properties that can be exploited to target pathological proteins in neurodegenerative diseases.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".