Towards label-free, wash-free and quantitative B-type natriuretic peptide detection for heart failure diagnosis
Bibliographic record
Abstract
Current diagnostic systems used in clinical settings to detect protein biomarkers require highly trained experts, large volumes of blood samples, and long turnaround times. There is an immense need for a low-cost and accurate point-of-care-testing (POCT) device for home monitoring of protein biomarkers for global pandemics such as heart failure (HF). The integration of highly sensitive carbon nanotube (CNT) thin film (CNT-TF) impedance sensors with the electrochemical impedance spectroscopy (EIS) technique is a promising platform to actualize POCT systems for home use. Herein, we report such a system, NanoBot, which allows the label-free and wash-free detection of the HF antigen biomarker B-type natriuretic peptide (BNP) in blood plasma. The NanoBot system consists of two parts: a disposable test strip and a miniature electronic readout unit. The NanoBot exhibited reasonable accuracy and precision, a clinically relevant limit of detection (LOD) as low as 16 pg mL-1, a linear detection range from 0-4000 pg mL-1 and excellent correlation with a reference standard fluorescent immunoassay (FIA). A pilot clinical study with patient-derived blood plasma validated the NanoBot's strong performance compared to that of Alere Triage®, with an interclass correlation coefficient (ICC) of 98% and a square of correlation coefficient (CC) of 0.95. Furthermore, unlike the Alere Triage® that requires more than 250 μL of blood collected via venipuncture, the NanoBot only requires 50 μL of blood. Collectively, the NanoBot's high sensitivity, accuracy, precision, and self-calibration characteristics signify its promising potential as a POCT platform for heart failure diagnosis in home use.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".