Direct detection of alpha synuclein oligomers in vivo
Bibliographic record
Abstract
BACKGROUND: Rat models of Parkinson's disease are widely used to elucidate the mechanisms underlying disease etiology or to investigate therapeutic approaches. Models were developed using toxins such as MPTP or 6-OHDA to specifically target dopaminergic neurons resulting in acute neuronal loss in the substantia nigra or by using viral vectors to induce the specific and gradual expression of alpha synuclein in the substantia nigra. The detection of alpha- synuclein oligomers, the presumed toxic species, in these models and others has been possible using only indirect biochemical approaches to date. Here we coinjected AAVs encoding alpha-synuclein fused to the N- or C-terminal half of VenusYFP in rat substantia nigra pars compacta and describe for the first time a novel viral vector rodent model with the unique ability to directly detect and track alpha synuclein oligomers ex vivo and in vivo. RESULTS: Viral coinjection resulted in widespread VenusYFP signal within the nigrostriatal pathway, including cell bodies in the substantia nigra and synaptic accumulation in striatal terminals, suggestive of in vivo alpha-synuclein oligomers formation. Transduced rats showed alpha-synuclein induced dopaminergic neuron loss in the substantia nigra, the appearance of dystrophic neurites, and gliosis in the striatum. Moreover, we have applied in vivo imaging techniques in the living mouse to directly image alpha-synuclein oligomers in the cortex. CONCLUSION: We have developed a unique animal model that provides a tool for the Parkinson's disease research community with which to directly detect alpha- synuclein oligomers in vivo and screen therapeutic approaches targeting alpha-synuclein oligomers.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".