Reverse-Phase versus Sandwich Antibody Microarray, Technical Comparison from a Clinical Perspective
Bibliographic record
Abstract
Protein microarrays are powerful tools to quantify and characterize proteins in multiplex assays. They have great potential within clinical diagnostics and prognostics, as they minimize consumption of both analyte and biological sample. Assays that do not require labeling of the biological specimen, henceforth called label-free, are vital for ease of clinical sample processing. Here, we evaluate two label-free techniques, reverse-phase and sandwich antibody assays, using microarrays on high-performance porous silicon surfaces and fluorescence detection. In view of increasing interest in reverse microarrays, this paper focuses on analytical sensitivity of the reverse assays compared to the more complex but highly sensitive sandwich assay. Sensitivity, linear range, and reproducibility of the two assays were compared using prostate-specific antigen (PSA) in buffer. The sandwich assay displayed 5 orders of magnitude lower detection limit (0.7 ng/mL) compared to the reverse assay (70 microg/mL). PSA at 50 nM (1.5 microg/mL) in cell lysates was detected by the sandwich assay but not by the reverse assay, demonstrating again a far lower detection limit for sandwich microarrays. In independent assay runs of PSA spiked in female serum, the sandwich assay had good linearity (R2 > 0.99) and reproducibility (coefficient of variation < or =15%), and the detection limit could be improved to 0.14 ng/mL. Without further signal amplification, the sandwich assay would be our choice for PSA analysis of clinical samples using a microarray technology platform.
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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 | 0.021 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".