Colonic mucosal α-synuclein lacks specificity as a biomarker for Parkinson disease
Bibliographic record
Abstract
OBJECTIVE: To determine the utility of detecting a-synuclein (aSyn) in colonic mucosal biopsy tissue as a potential diagnostic biomarker for Parkinson disease (PD). METHODS: We used the paraffin-embedded tissue (PET) blot, which degrades physiologic nonaggregated aSyn using proteinase K and enhances antigen retrieval allowing sensitive and selective detection of remaining protein aggregates, to detect aSyn in colonic mucosal biopsies from 15 patients with early PD (,3 years), 7 patients with later PD (.5 years), and 11 individuals without PD. aSyn and serine 129–phosphorylated aSyn (Ser129p-aSyn) were assessed by PET blot and conventional immunohistochemistry. RESULTS: PET blot–resistant aggregated aSyn and Ser129p-aSyn was present in 12 of 15 individuals with early PD, 7 of 7 individuals with later PD, and 11 of 11 control subjects. The number of biopsies positive by PET blot relative to conventional immunohistochemistry was significantly lower in both PD groups compared with the control group for both aSyn and Ser129p-aSyn,whereas routine immunohistochemistry was positive more often in PD, but was positive in as many as 9 of 11 control individuals. CONCLUSION: Strong evidence of the presence of aggregated hyperphosphorylated aSyn in individuals with and without PD, using such a sensitive and specific method as the PET blot, suggests that colonic deposition of aSyn is not a useful diagnostic test for PD. The utility of detecting aSynin the colon as a biomarker in combination with other assessments remains to be determined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".