Additional file 1 of Alpha-synuclein seeding shows a wide heterogeneity in multiple system atrophy
Bibliographic record
Abstract
Additional file 1. Fig. S1. The differential interaction of ThT with α-synuclein aggregates derived from patients with PD or MSA depends on the RT-QuIC reaction buffer. Fig. S2. The levels of total and aggregated α-synuclein in the Substantia Nigra are relatively uniform between the MSA and the LBD patients included in the validation phase. Fig. S3. End-point dilution alpha-synuclein RT-QuIC analysis of the cerebellar PBS-soluble fraction from two MSA cases classified as high and low seeders. Fig. S4. Dissection of brain regions for protein extraction and α-synuclein seeding evaluation. Fig. S5. The inter-individual α-synuclein seeding and intra-individual α-synuclein seeding behavior is distinct between MSA patients and brain regions. Fig. S6. The levels of total α-synuclein in each brain region are relatively uniform but the burden of aggregated α-synuclein varies across patients and brain regions in MSA patients. Fig. S7. The inter-individual but not the intra-individual α-synuclein seeding heterogeneity is preserved using the sarkosyl insoluble fraction. Fig. S8. The SI fraction promotes a faster aggregation and reaches a higher fluorescence plateau than the PBS-soluble fraction. Fig. S9. The extent of pathology detected by different α-synuclein antibodies is not uniform. Fig. S10. GCIs and NCIs deposition across different brain regions in MSA patients. Fig. S11. The burden of GCIs and NCIs varies across different brain regions in MSA. Table S1. Demographic and neuropathological diagnosis of the subjects included in this study.
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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 | 0.002 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.886 | 0.134 |
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".