Reduction of brain stem pathology and transient amelioration of early cognitive symptoms in transgenic mice treated with a monoclonal antibody against α-synuclein oligomers/protofibrils
Bibliographic record
Abstract
Immunotherapy against alpha-synuclein (α-syn) is a promising novel treatment strategy for Parkinson's disease (PD) and related α-synucleinopathies. We have previously shown that systemic treatment with the monoclonal oligomer/protofibril-selective antibody mAb47 targeting cytotoxic α-syn leads to reduced central nervous system levels of such species as well as an indication of reduced late-stage symptoms in aged (Thy-1)-h[A30P] α-syn transgenic mice. Here, we performed an early-onset long-term treatment study with this antibody to evaluate effects on brain pathology and behavioral outcomes in the same mouse model. Compared to the placebo group, the treatment strongly reduced phosphorylated α-syn (pS129 α-syn) pathology in the upper brain stem. Moreover, a preserved recognition memory and risk assessment behavior could be seen in antibody-treated mice at six months of age, even although these effects were no longer significant at eleven months of age. Importantly, no evidence of inflammatory responses or other potential toxic effects was seen with the treatment. Taken together, this study supports the strategy to target α-syn oligomers/protofibrils with monoclonal antibodies to counteract early symptoms and slow down the progression of PD and other α-synucleinopathies.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".