Corticotropin-releasing hormone as a candidate biomarker for parkinsonian disorders
Bibliographic record
Abstract
Abstract Disease-specific fluid biomarkers are in demand for parkinsonian syndromes (PS). Corticotropin-releasing hormone (CRH) was proposed as a biomarker for Lewy body disease. As such, this project aimed to confirm CRH as a potential biomarker for different PS. CRH and misfolded α-synuclein (αSyn) were measured in CSF. The primary cohort included Lewy body disease patients (i.e. Parkinson’s disease or dementia with Lewy bodies, n = 77), atypical PS (n = 37) and non-parkinsonian neurodegenerative diseases (n = 164), as well as controls (n = 354). A replication cohort included Lewy body disease (n = 27), atypical PS (n = 58) and controls (n = 58). CRH was downregulated in αSyn positive Lewy body disease, αSyn positive controls and in all atypical PS compared with αSyn negative controls (P = 3.3e−05, P = 3.1e−10, P = 2.9e−03). CRH was also decreased in αSyn positive Lewy body disease compared with αSyn negative non-PS (P = 2e−03) and correlated with cognitive impairment and inflammation in αSyn positive Lewy body disease. We show that CRH is a promising biomarker for Lewy body disease and atypical PS and its association with inflammation and cognitive decline. Reductions in CRH in Lewy body disease and other PS suggest this decrease may relate to dopaminergic degeneration instead of αSyn pathology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".