Paper-Based All-in-One Origami Nanobiosensor for Point-of-Care Detection of Cardiac Protein Markers in Whole Blood
Bibliographic record
Abstract
Rapid and accurate diagnosis of cardiovascular diseases (CVDs) at the earliest stage is of paramount importance to improve the treatment outcomes and avoid irreversible damage to a patient's cardiovascular system. Microfluidic paper-based devices (μPADs) represent a promising platform for rapid CVD diagnosis at the point of care (POC). This paper presents an electrochemical μPAD (E-μPAD) with an all-in-one origami design for rapid and POC testing of cardiac protein markers in whole blood. Based on the label-free, electrochemical impedance spectroscopy (EIS) immunoassay, the E-μPAD integrates all essential components on a single chip, including three electrochemical cells, a plasma separation membrane, and a buffer absorption pad, enabling easy and streamlined operations for multiplexed detection of three cardiac protein markers [cardiac troponin I (cTnI), brain natriuretic peptide (BNP)-32, and D-Dimer] on a finger-prick whole blood sample within 46 min. Superior analytical performance is achieved through sensitive EIS measurement on carbon electrodes decorated with semiconductor zinc oxide nanowires (ZnO NWs). Using spiked human plasma samples, ultralow limits of detection (LODs) of E-μPAD are achieved at 4.6 pg/mL (190 fM) for cTnI, 1.2 pg/mL (40 fM) for BNP-32, and 146 pg/mL (730 fM) for D-Dimer. Real human blood samples spiked with purified proteins are also tested, and the device's analytical performance was proven to be comparable to commercial ELISA kits. The all-in-one E-μPAD will allow rapid and sensitive testing of cardiac protein markers through easy operations, which holds great potential for on-site screening of acute CVDs in nonlaboratory settings such as emergency rooms, doctor's offices, or patient homes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".