Abstract 6950: Precision Quantification of Cardiac Biomarkers Using Reagent-Free Electrochemical Aptasensors
Bibliographic record
Abstract
Introduction: Precision quantification of cardiac biomarkers in unprocessed patient samples could reveal critical information in the event of acute myocardial infarction and chronic heart failure. Consequential research efforts have been made in recent years to develop self-contained analytical devices for accurate and rapid quantification of molecular analytes in unprocessed biological fluids, however, their poor sensitivity and compromised robustness in complex biofluids greatly limit their clinical usability. We aimed to investigate the utility of a new biomolecular analysis technique for accurately quantifying BNP and NT-pro BNP directly in whole blood using only a sensor-modified electrode chip. Methods and Results: Recently, we have developed a new class of reagentless biosensors using a molecular pendulum (MP) for monitoring physiologically relevant proteins directly in unprocessed body fluids. The sensing strategy is based on the kinetics of a MP tethered to an electrode surface where the motion of the MP is modulated by the presence of target analyte. Using an antibody as the bioreceptor unit, our MP sensors demonstrated to detect 1 pg/mL of cardiac troponin I in several bio-fluids. Since aptamers hold great promise for developing next-generation of diagnostics and therapeutic tools, we employed them to develop reagent-free MP aptasensors that quantify different heart failure biomarkers including BNP and NT-pro BNP directly in whole human blood. Our MP aptasensors show excellent performance matrices, such as a wide dynamic range (10 fg/mL to 10 ng/mL of BNP), a limit of quantification of 93.5 fg/mL, excellent specificity, and long-term stability, measured directly in whole human blood. Moreover, the analytical resolving capability of the sensors can distinguish samples that differ by only 20 pg/mL of BNP, which is critical for the BNP guided therapy for heart failure. We were also able to quantify BNP and NT-pro BNP simultaneously from the same sample by multiplexing the sensors on the same electronic chip. Conclusion: The analytical strength of our reagent-free MP aptasensors warrants their accelerated development as they could play crucial roles for earlier identification and better risk stratification of heart failure patients.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".