Protein Detection Using Arrayed Microsensor Chips: Tuning Sensor Footprint to Achieve Ultrasensitive Readout of CA-125 in Serum and Whole Blood
Bibliographic record
Abstract
Multiplexed assays that can measure protein biomarkers and internal standards are highly desirable given the potential to reduce false positives and negatives. We report here the use of a chip-based platform that achieves multiplexed immunosensing of the ovarian cancer biomarker CA-125 without the need for covalent labeling or sandwich complexes. The sensor chips allow the straightforward comparison of detectors of different sizes, and we used this feature to scan the microscale size regime for the best sensor size and optimize the limit of detection exhibited down to 0.1 U/mL. The assay has a straightforward design, with readout being performed in a single step involving the introduction of a noncovalently attached redox reporter group. The detection system reported exhibits excellent specificity, with analysis of a specific cancer biomarker, CA-125, performed in human serum and whole blood. The multiplexing of the system allows the analysis of the biomarker to be performed in parallel with an abundant serum protein for internal calibration.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".