Novel and less invasive biomarker assays to measure liver ATP7B in Wilson disease patients
Bibliographic record
Abstract
Novel therapies for Wilson disease (WD) will require appropriate biomarkers and clinically relevant endpoints to demonstrate therapeutic efficacy. We aimed to develop robust, minimally invasive biomarker assays to assess target engagement in future clinical trials for WD therapeutics. We conducted a single-center, sample collection biomarker study in 21 patients with WD and 6 control participants. Serum, liver fine needle aspiration biopsy (FNA), and liver core needle biopsy (CNB) samples were collected from participants. RNA and protein were isolated from serum exosome and biopsy samples. Samples were analyzed for mRNA expression by quantitative PCR and for protein expression by a novel electrochemiluminescence (ECL) immunoassay. ATP7B mRNA was detectable in FNA, CNB, and serum exosome samples. However, serum exosomes are not yet a viable method for ATP7B quantification. ATP7B protein was only detectable in CNB samples. We compared the FNA and CNB results for five WD patients and found mRNA expression levels to be comparable with an R2 of 0.64 with statistical significance. The methods we developed may be useful in clinical settings to quantify hepatocyte-specific expression of ATP7B for the development of novel therapeutics for Wilson disease.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".