Serum heat shock protein concentrations are not associated with amyotrophic lateral sclerosis risk or survival in three European populations
Bibliographic record
Abstract
Introduction: Serum heat shock protein (HSP) concentrations have been reported as potential biomarkers for amyotrophic lateral sclerosis (ALS). Here, we investigate the role of serum HSP70, HSP90, and DNAJC7 as biomarkers for ALS. Methods: Serum samples were collected from ALS patients and volunteer controls from three different clinical cohorts (in Germany, Ireland, and Italy). Serum HSP concentrations were determined using enzyme-linked immunosorbent assay. Descriptive statistics, generalized logistic regression, and Cox proportional hazards models were used to model associations between log serum HSP concentrations and ALS risk. Results: In total, 251 ALS patients and 184 healthy volunteers were included. Logistic regression models failed to find associations between ALS risk and log serum concentration of HSP70 (OR 0.43, 95% CI: 0.10–1.78, p = 0.242), HSP90 (OR 0.95, 95% CI: 0.39–2.37, p = 0.904), or DNAJC7 (OR 1.55, 95% CI: 0.90–2.68, p = 0.118). Survival of ALS patients was not associated with log serum concentration of HSP HSP70 (HR1.06, 95% CI: 0.36–3.14, p = 0.916), HSP90 (HR 1.17, 95% CI: 0.67–2.02, p = 0.584), or DNAJC7 (HR 0.83, 95% CI: 0.57–1.21, p = 0.337). Discussion: We did not replicate previous findings that serum HSP70 and HSP90 concentrations were associated with risk of ALS. DNAJC7 was not associated with ALS risk, and there were no obvious longitudinal patterns in log serum concentrations of HSP70, HSP90, or DNAJC7. In addition, serum HSP concentrations were not associated with ALS survival.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".