Nature Inspired Anti-Biofouling Strategy for the Detection of Protein Biomarkers Directly in a Drop of Blood
Bibliographic record
Abstract
Electrode biofouling in complex biological fluids is one of the major problems in analytical electrochemistry. Direct electrochemical detection in human blood remains challenging due to quick surface passivation by proteins present in blood, degrading sensor’s sensitivity. This challenge has dramatically slowed down commercialization efforts of point-of-care devices. Here we have developed a simple nature-inspired electrochemical DNA hybridization (e-hyb) assay based on steric hindrance which enable electrodes to detect nanomolar concentration of proteins (i.e. antibody) directlywithin a drop ofblood without any sample preparation. This e-hyb assay involves two DNA strands; one-captured DNA immobilized on electrode surface and another complementary reporter DNA labelled with an electroactive molecule (methylene blue) at one end and a specific recognition element at the another end. In presence of a specific large analyte binding to the small recognition element, the reporter DNA cannot reach to the electrode surface due to steric hindrance. This simple strategy enables us to quantify different protein biomarkers (HIV antibody, anti-DNP antibody, anti-DIG antibodyetc) in human blood without being limited affected by biofouling. Figure 1: e-DNA hybridization based anti-biofouling strategy for the detection of antibody in human blood Figure 1
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".