Discrimination of MSA-P and MSA-C by RT-QuIC analysis of olfactory mucosa: the first assessment of assay reproducibility between two specialized laboratories
Bibliographic record
Abstract
BACKGROUND: can be detected in the olfactory mucosa (OM) of MSA and PD patients. For this reason, we have performed the first interlaboratory study based on α-synuclein Real-Time Quaking-Induced Conversion (αSyn_RT-QuIC) analysis of OM samples collected from PD and MSA patients with the parkinsonian (MSA-P) and cerebellar (MSA-C) phenotypes. METHODS: OM samples were prospectively collected from patients with a probable diagnosis of MSA-P (n = 20, mean disease duration 4.4 years), MSA-C (n = 10, mean disease duration 4 years), PD (n = 13, mean disease duration 8 years), and healthy control subjects (HS) (n = 11). Each sample was analyzed by αSyn_RT-QuIC in two independent specialized laboratories, one located in Italy (ITA-lab) and one located in the USA (USA-lab). Both laboratories have developed and used harmonized αSyn_RT-QuIC analytical procedures. Results were correlated with demographic and clinical data. RESULTS: The αSyn_RT-QuIC analysis reached a 96% interrater agreement of results (IAR) between laboratories (Kappa = 0.93, 95% CI 0.83-1.00). In particular, αSyn_RT-QuIC seeding activity was found in the OM of 9/13 patients with PD (sensitivity 69%, IAR 100%) and 18/20 patients with MSA-P (sensitivity 90%, IAR 100%). Interestingly, samples collected from patients with MSA-C did not induce αSyn_RT-QuIC seeding activity, except for one subject in USA-lab. Therefore, we found that MSA-P and MSA-C induced opposite effects. Regardless of disease diagnosis, the αSyn_RT-QuIC seeding activity correlated with some clinical parameters, including the rigidity and postural instability. CONCLUSIONS: may serve as a novel biomarker for accurate clinical diagnoses of PD, MSA-P, and MSA-C. Moreover, αSyn_RT-QuIC represents a reliable assay that can distinguish patients with MSA-P from those with MSA-C, and may lead to significant advancements in patients stratification and selection for emerging pharmacological treatments and clinical trials.
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".