Disposable Reagentless Electrochemical Immunosensor Array Based on a Biopolymer/Sol-Gel Membrane for Simultaneous Measurement of Several Tumor Markers
Bibliographic record
Abstract
BACKGROUND: A reagentless sensor array for simultaneous multianalyte testing (SMAT) may enable accurate diagnosis and be applicable for point-of-care testing. We developed a disposable reagentless immunosensor array for simple immunoassay of panels of tumor markers. METHODS: We carried out SMAT with a direct capture format, in which colloidal gold nanoparticles with bound horseradish peroxidase (HRP)-labeled antibodies were immobilized on screen-printed carbon electrodes with biopolymer/sol-gel to trap their corresponding antigens from sample solution. Upon formation of immunocomplex, the direct electrochemical signal of the HRP decreased owing to increasing spatial blocking, and the analytes could be simultaneously determined by monitoring the signal changes. RESULTS: The proposed reagentless immunosensor array allowed simultaneous detection of carcinoma antigen 153, carcinoma antigen 125, carbohydrate antigen 199, and carcinoembryonic antigen in clinical serum samples in the ranges of 0.4-140 kU/L, 0.5-330 kU/L, 0.8-190 kU/L, and 0.1-44 microg/L, respectively, with detection limits of 0.2 kU/L, 0.5 kU/L, 0.3 kU/L, and 0.1 microg/L corresponding to the signals 3 SD above the mean of a zero standard. The interassay imprecision of the arrays was <9.5%, and they were stable for 35 days. The positivity detection rate of panels of tumor markers was >95.5% for 95 cases of cancer-positive sera. CONCLUSIONS: The immunosensor array provides a SMAT with short analytical time, small sampling volume, no need for substrate, and, no between-electrode cross-talk. This method not only proved the capability of the array in point-of-care testing, but also allowed simultaneous testing of several tumor markers.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".