Electrochemical and Plasmonic Detection of Myocardial Infarction Using Microfluidic Biochip Incorporated with Mesoporous Nanoscaffolds.

This paper reports a microfluidic device for the electrochemical and plasmonic detection of cardiac myoglobin (cMb) and cardiac troponin I (cTnI) with noticeable limits of detection (LoD) as low as a few picograms per milliliter (pg/mL) ranges, achieved in a short detection time. The device features two working electrodes, each with a mesoporous Ni3V2O8 nanoscaffold grafted with reduced graphene oxide (rGO) that improves the interaction of diffusing analyte molecules with the sensing surface by providing a high surface area and reaction kinetics. Electrochemical studies reveal sensitivities as high as 9.68 µA ng/mL and a LoD of 2.0 pg/mL for cTnI, and 8.98 µA ng/mL and 4.7 pg/mL for cMb. Additionally, the surface plasmon resonance (SPR) studies demonstrate a low-level LoD of 8.8 pg/mL for cMb and 7.3 pg/mL for cTnI. The dual-modality sensor enables dynamic tracking of kinetic antigen-antibody interactions during sensing, self-verification through providing signals of two modes, and reduced false readout. This study demonstrates the complementary nature of the electrochemical and SPR modes in biosensing, with the electrochemical mode being highly sensitive and the SPR mode providing superior tracking of molecular recognition behaviors. The presented sensor represents a significant innovation in cardiovascular disease management and can be applied to monitor other clinically important biomolecules.

Dual-modality microfluidic biosensor based on nanoengineered mesoporous graphene hydrogels.

A dual-modality microfluidic biosensor is fabricated using a mesoporous nanostructured cysteine-graphene hydrogel for the quantification of human cardiac myoglobin (cMb). In this device, the nanoengineered mesoporous l-cysteine-graphene (Cys-RGO) hydrogel performs the role of a dual-modality sensing electrode for the measurements conducted using differential pulse voltammetry and surface plasmon resonance (SPR) techniques. High surface reactivity, mesoporous structure and fast electron transfer combined with good reaction kinetics of the graphene hydrogel in this device indicate excellent performance for the detection of human cardiac myoglobin in serum samples. In electrochemical modality, this microfluidic chip exhibits a high sensitivity of 196.66 µA ng-1 mL cm-2 for a linear range of concentrations (0.004-1000 ng mL-1) with a low limit of detection (LOD) of 4 pg mL-1 while the SPR technique shows a LOD of 10 pg mL-1 for cMb monitoring in the range 0.01-1000 ng mL-1. The intra-assay coefficient of variation was less than 8% for standard samples and 9% for real serum samples, respectively. This Cys-RGO hydrogel-based microfluidic SPR chip allows real-time dynamic tracking of cMb molecules with a high association constant of 4.93 ± 0.2 × 105 M-1 s-1 and a dissociation constant of 1.37 ± 0.08 × 10-4 s-1, self-verification, reduced false readout, and improved detection reliability.

Echocardiogram Analysis Using Motion Profile Modeling.

Echocardiography is a widely used and cost-effective medical imaging procedure that is used to diagnose cardiac irregularities. To capture the various chambers of the heart, echocardiography videos are captured from different angles called views to generate standard images/videos. Automatic classification of these views allows for faster diagnosis and analysis. In this work, we propose a representation for echo videos which encapsulates the motion profile of various chambers and valves that helps effective view classification. This variety of motion profiles is captured in a large Gaussian mixture model called universal motion profile model (UMPM). In order to extract only the relevant motion profiles for each view, a factor analysis based decomposition is applied to the means of the UMPM. This results in a low-dimensional representation called motion profile vector (MPV) which captures the distinctive motion signature for a particular view. To evaluate MPVs, a dataset called ECHO 1.0 is introduced which contains around 637 video clips of the four major views: a) parasternal long-axis view (PLAX), b) parasternal short-axis (PSAX), c) apical four-chamber view (A4C), and d) apical two-chamber view (A2C). We demonstrate the efficacy of motion profile-vectors over other spatio-temporal representations. Further, motion profile-vectors can classify even poorly captured videos with high accuracy which shows the robustness of the proposed representation.