Multicenter Assessment of Immunohistochemical Methods for Pathological Alpha-Synuclein in Sigmoid Colon of Autopsied Parkinson’s Disease and Control Subjects
Bibliographic record
Abstract
BACKGROUND: Conflicting results from studies of Lewy-type α-synucleinopathy (LTS) in colonic biopsies of subjects with Parkinson's disease (PD) prompted a two-part multicenter assessment. The first assessment, now published (Acta Neuropathol Commun 4 : 35, 2016), examined archived colonic biopsies and found that none of the tested methods was adequately sensitive or specific. OBJECTIVE: As the amount of nervous tissue in typical colonic biopsies may be insufficient, and the clinical diagnosis of PD not completely accurate, the objective of the current study was to use instead full-thickness sections of sigmoid colon from autopsy-proven PD and normal subjects. METHODS: Seven different immunohistochemical (IHC) methods were used, employing five different primary antibodies and four different combinations of epitope exposure and signal development protocols. Specific staining was defined as being restricted to morphological features consistent with neuronal elements. Stained slides from each subject were independently categorized as being positive or negative for LTS, and their density semi-quantitatively graded, by four raters blinded to diagnosis. RESULTS: Agreement and mean diagnostic performance varied markedly between raters. With the two most accurate raters, 5 methods achieved diagnostic accuracies of 70% or greater; one method had 100% accuracy and 100% inter-rater agreement. The submucosa had the highest prevalence of pathological LTS staining, followed by the muscularis and mucosa. CONCLUSIONS: The major conclusion of this study is that, when sufficient submucosa and LTS is present, and when specific staining is defined as being consistent with neuronal morphology, adequately-trained raters may reliably distinguish PD colon from control using suitable IHC methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".