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Record W4415880670 · doi:10.1038/s41531-025-01152-3

Cognitive function correlates with gastric alpha-synuclein seeding activity in early Parkinson’s disease

2025· article· en· W4415880670 on OpenAlexaboutno aff
Chaewon Shin, Jong Pil Im, Jung‐Youn Han, Bora Jin, Kyung Ah Woo, Seungmin Lee, HoYoung Jeon, Jae Young Joo, Hee Jin Chang, Jung Hwan Shin, Han‐Joon Kim, Jong‐Min Kim, Young Pyo Choi, Beomseok Jeon

Bibliographic record

Venuenpj Parkinson s Disease · 2025
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
FundersSeoul National University HospitalSeoul National University
KeywordsStomachPathologicalBiomarkerPathophysiologyDiseaseBiopsyCognition

Abstract

fetched live from OpenAlex

Alpha-synuclein (AS) accumulation is more frequently detected in the stomach than in the colon of patients with Parkinson's disease (PD), suggesting its potential as a pathologic biomarker. This study is to evaluate the diagnostic performance of real-time quaking-induced conversion assay on stomach biopsies for early PD and its association with clinical characteristics. Stomach biopsy tissues were prospectively collected from 22 patients with early-stage PD and 17 controls. Pathological AS-seeding activity was assessed, and correlations between kinematic parameters and clinical features were analyzed with age adjustment. Pathological AS-seeding activity was detected in 45.5% of patients with PD and in none of the controls. The Montreal Cognitive Assessment score was correlated with the lag time of positive replicates (Spearman's ρ = 0.742; p = 0.022), after age adjustment. The AS seed amplification assay using the stomach tissue may serve as a biomarker reflecting disease pathophysiology of the gut-brain axis in PD.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.015
GPT teacher head0.258
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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