Electrochemical Immuno-Biosensors on Nanostructured Electrodes for Rapid Sensitive Detection of Disease Biomarkers
Bibliographic record
Abstract
Development of rapid tests for quantitative detection of analytes in complex matrices, plays an important role in medical diagnostics, prognostics and therapeutics. Current methods for molecular diagnostics are time-consuming and multiple-step reliant on well-trained technicians and fully-equipped laboratories to provide quantitative detection of biomarkers. In this case, the challenges to overcome the sensitivity and selectivity of working directly in biological fluids in a timely manner have always been the main step for the development of such rapid tests. Electrochemical sensors, analogous to glucometer, are known to be rapid with the ability to provide direct electronic signal without any interference from the biological phenomena. When functionalized with specific biological substances, they intend to provide the required specificity in addition to signal selectivity in biological fluids. Inspired by nature, we design biorecognition probes that are combined with newest advancement in the engineering for the development of electrochemical immune-biosensors. These molecular recognition probes are designed using the ability of antibodies to bind antigens, and DNA construct to hybridize to the complementary construct, to create specificity for direct detection in complex media. On the other hand, we rely on the innovative approaches in nanomaterials, surface sciences, and self-assembly techniques to improve the conductivity and sensitivity of electrode surface. This includes the incorporation of high-curvature nanostructured microelectrodes that helps with the efficiency of molecular interactions at the surface and the transfer of electrons for the ease of signal transduction. In this regard, we fabricate small-scale nanostructured electrodes and engineer the surface for the immobilization of the biomolecular monolayer at the interface to provide the desired specificity for biomarkers in real biological samples, e.g. whole blood. We adapted our nano-bio-sensors for translational applications such as (1) at-line monitoring of signaling proteins in hematopoietic stem cell expansion, (2) rapid diagnosis of pathogenic infections through quantitative detection of antibodies directly in patient samples, and (3) rapid diagnosis of inner ear disorders based on the detection of blood biomarkers. We showed that our electrochemical immune-biosensors are capable of detecting at therapeutic concentrations with tunable ranges while performing in a 10 µL sample within less than 30 minutes.
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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 | 0.001 |
| 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".