Evaluation of alpha-synuclein immunohistochemical methods for the detection of Lewy-type synucleinopathy in gastrointestinal biopsies
Bibliographic record
Abstract
The observation showing that Lewy type synucleinopathy (LTS), the pathological hallmark of Parkinson's disease (PD), is found in the gut of almost all PD subjects led to a substantial amount of research to develop a diagnostic procedure in living patients based on endoscopically obtained gastrointestinal biopsies. However, the existing studies have provided conflicting results regarding the sensitivity and specificity of gastrointestinal biopsies for the detection of LTS. We therefore undertook a multi-center staining and blinded judging of a common set of slides from colonic biopsies to determine the optimal protocol for the detection of LTS. Four different immunohistochemical methods, developed in four separate expert laboratories, were evaluated for their sensitivity and specificity to detect enteric LTS. Test sets of formalin-fixed, paraffin-embedded sections from biopsies of 9 PD subjects and 3 controls were stained with the 4 methods and graded by 4 different observers. Four types of staining morphology (granular staining in the lamina propria, perivascular/vascular wall staining in the submucosa, lacy-granular pattern in the submucosa and epithelial cell nuclear staining) were variably observed in the slides stained by the 4 methods. Positive alpha-synuclein staining was observed by all 5 judges in most of the slides from control cases, regardless of the staining methods that were used. Moreover, none of the tested method or staining pattern had a specificity and sensitivity more than 80 % regarding to PD. Overall, our study suggest that the tested methods are not adequate for the prediction of PD using gastrointestinal biopsies. Future studies are warranted to test new immunostaining methods.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".