Electrochemical Detection of Human Antibodies Directly in Whole Blood Using Nanostructured Electrodes.
Bibliographic record
Abstract
Rapid diagnosis of infectious pathogens in patients can be performed by analyzing specific antibodies produced by the immune system. The ability to specifically capture these antibodies directly in the body fluids such as whole blood, is a huge advancement toward rapid point-of-care diagnostics. We introduce a highly selective strategy for antibody detection based on integrating an electrochemical DNA-based assay with peptide-antibody bioconjugate to specifically capture human antibodies on the surface of electrodeposited nanostructures. We have shown that one-pot detection of macromolecules such as antibodies can be done by sterically inhibiting the hybridization of complementary DNA strand carrying the antibody to the surface bound DNA strand. We also showed that using our electrodeposited nanostructured electrode, we can overcome the limitations of surface toward capturing of peptide recognition, but enhance the inhibition of macro-size molecules for the surface hybridization. Here, we introduce a peptide-assisted electrochemical assay implemented on nanostructured electrode that enables the capturing of real human antibodies directly in whole blood. We improved the linear range and limit of detection by applying the assay using a particular nanostructured platform with tunable roughness. We present the application of the proposed platform to detect HIV-1 human antibodies in real patient samples.
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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) | 0.000 | 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 | 0.000 |
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".