Development of reverse phase protein microarrays for the validation of clusterin, a mid-abundant blood biomarker
Bibliographic record
Abstract
BACKGROUND: Many putative disease blood biomarkers discovered in genomic and proteomic studies await validation in large clinically annotated cohorts of patient samples. ELISA assays require large quantities of precious blood samples and are not high-throughput. The reverse phase protein microarray platform has been developed for the high-throughput quantification of protein levels in small amounts of clinical samples. RESULTS: In the present study we present the development of reverse-phase protein microarrays (RPPMs) for the measurement of clusterin, a mid-abundant blood biomarker. An experimental protocol was optimized for the printing of serum and plasma on RPPMs using epoxy coated microscope slides and a non-denaturing printing buffer. Using fluorescent-tagged secondary antibodies, we achieved the reproducible detection of clusterin in spotted serum and plasma and reached a limit of detection of 780 ng/mL. Validation studies using both spiked clusterin and clinical samples showed excellent correlations with ELISA measurements of clusterin. CONCLUSION: Serum and plasma spotted in the reverse phase array format allow for reliable and reproducible high-throughput validation of a mid-abundant blood biomarker such as clusterin.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".