Detection of pathological heart murmurs by feature extraction of phonocardiogram signals

Cardiac auscultation can be perceived as method of determining the human heart condition by listening to the heart sounds. These heart sounds contain vital information related to a person’s heart condition. Any departure from the normal cardiac auscultation readings in terms of presence of additional heart sounds is indicative of an unhealthy heart. The use of Phonocardiogram (PCG)signals (i.e. the electronic recording of heart sounds) completely dismisses the limitation of relying solely on the physician’s hearing ability. At the same time, they provide with a high-fidelity representation of the heart sounds in the most cost-effective way as compared to the methods like Electrocardiogram (ECG). In this paper, a method of detection of heart ailments by extracting the features of PCG signals is proposed. The normal heart sounds, gallop rhythms and the most common pathological murmurs have been used for analysis. By analyzing these signals, early detection and diagnosis of heart diseases can be done reliably. This will not only confirm health and longevity by early diagnosis and pin-pointed prognosis, but will also be economically suitable for those who can hardly afford tests like ECG. It can also be practicable in the case of infants wherein the other non-invasive diagnosis techniques like ECG fail

Passively Addressable Ultra-Low Volume Sweat Chloride Sensor

This work demonstrates a novel electrochemical biosensor for the detection of chloride ion levels in ultra-low volumes (1–3 microliters) of passively expressed human sweat. We present here a hydration monitor that the pediatric, geriatric, and other immune-compromised or physically inactive/sedentary population cohort can utilize, for whom the current methods of chloride quantification of active stimulation of sweat glands through iontophoresis or treadmill runs are unsuitable. In this work, non-faradaic electroanalysis using gold microelectrodes deposited on a flexible nanoporous substrate, for high nanoscale surface area to volume enhancement, was leveraged to operate in ultra-low sweat volumes of <3 µL eluted at natural rates. The specific chloride ionophore-based affinity of chloride ions resulted in the modulation of charge transfer within the electrical double layer at the electrode–sweat buffer interface, which was transduced using electrochemical impedance spectroscopy (EIS) and chronoamperometry (CA). Linear calibration dose responses with R-squared values of 0.9746 and 0.9403 for EIS and CA respectively were obtained for a dynamic range of 10–100 mM. The surface charge and the binding chemistry of the capture probe were studied using zeta potential studies and UV-Vis. The dynamic sweat chloride-tracking capability of the sensor was evaluated for a duration of 180 min. Studies were conducted to probe the efficacy of the developed sensor for passive ultra-low sweat chloride assessment on human subjects (n = 3)

Pysanka-Inspired Electrode Modification with Aptamer Encapsulation in ZIF-8 for Urine Creatinine Electrochemical Biosensing

Drawing inspiration from the several thousand beautiful Pysanky egg art of Ukraine, we have developed a novel material, Aptamer–Gold Nanoparticles (AuNPs)@ZIF-8, that can be used for building sensitive and highly stable POC biosensors for longitudinal health mapping. Here, we demonstrate a sensitive and specific novel electrochemical biosensor, made of a novel synthesized in situ encapsulated aptamer-AuNPs@ZIF-8 composite, for monitoring levels of creatinine (0.1–1000 μg/mL). In this work, we have reported the synthetic protocol for the first-of-a-kind in situ encapsulation of aptamer and AuNPs together in a ZIF-8 matrix, and explored the characteristic properties of this novel material composite using standard analytical techniques and its application for biosensor application. The as-synthesized material, duly characterized using various physicochemical analytical methods, portrays the characteristics of the unique encapsulation strategy to develop the first-of-a-kind aptamer and AuNP encapsulation. Non-faradaic Electrochemical Impedance Spectroscopy (EIS) and Chronoamperometry were used to characterize the interfacial electrochemical properties. The biosensor performance was first validated using artificial urine in a controlled buffer medium. The stability and robustness were tested using a real human urine medium without filtration or sample treatment. Being versatile, this Ukrainian-art-inspired biosensor can potentially move the needle towards developing the next generation of sample-in-result-out robust POC diagnostics

Inflammatory Stimuli Responsive Non-Faradaic, Ultrasensitive Combinatorial Electrochemical Urine Biosensor

In this work, we propose a novel diagnostic biosensor that can enable stratification of disease states based on severity and hence allow for clear and actionable diagnoses. The scheme can potentially boost current Point-Of-Care (POC) biosensors for diseases that require time-critical stratification. Here, two key inflammatory biomarkers—Interleukin-8 and Interleukin-6—have been explored as proof of concept, and a four-class stratification of inflammatory disease severity is discussed. Our method is superior to traditional lab techniques as it is faster (<4 minutes turn-around time) and can work with any combination of disease biomarkers to categorize diseases by subtypes and severity. At its core, the biosensor relies on electrochemical impedance spectroscopy to transduce subtle inflammatory stimuli at the input for IL-8 and IL-6 for a limit of detection (LOD) of 1 pg/mL each. The biosensing scheme utilizes a two-stage random forest machine learning model for 4-state output disease classification with a 98.437% accuracy. This scheme can potentially boost the diagnostic power of current electrochemical biosensors for better precision therapy and improved patient outcomes

H.O.S.T.: Hemoglobin microbubble-based Oxidative stress Sensing Technology

Abstract In this work, we discuss the development of H.O.S.T., a novel hemoglobin microbubble-based electrochemical biosensor for label-free detection of Hydrogen peroxide (H2O2) towards oxidative stress and cancer diagnostic applications. The novelty of the constructed sensor lies in the use of a sonochemically prepared hemoglobin microbubble capture probe, which allowed for an extended dynamic range, lower detection limit, and enhanced resolution compared to the native hemoglobin based H2O2 biosensors. The size of the prepared particles Hemoglobin microbubbles was characterized using Coulter Counter analysis and was found to be 4.4 microns, and the morphology of these spherical microbubbles was shown using Brightfield microscopy. The binding chemistry of the sensor stack elements of HbMbs’ and P.A.N.H.S. crosslinker was characterized using Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy and UV–Vis Spectroscopy. The electrochemical biosensor calibration (R2 > 0.95) was done using Electrochemical Impedance Spectroscopy, Cyclic Voltammetry, and Square Wave Voltammetry. The electrochemical biosensor calibration (R2 > 0.95) was done using Electrochemical Impedance Spectroscopy, Cyclic Voltammetry, and Square Wave Voltammetry. The specificity of the sensor for H2O2 was analyzed using cross-reactivity studies using ascorbic acid and glucose as interferents (p < 0.0001 for the highest non-specific dose versus the lowest specific dose). The developed sensor showed good agreement in performance with a commercially available kit for H2O2 detection using Bland Altman Analysis (mean bias = 0.37 for E.I.S. and − 24.26 for CV). The diagnostic potential of the biosensor was further tested in cancerous (N.G.P.) and non-cancerous (H.E.K.) cell lysate for H2O2 detection (p = 0.0064 for E.I.S. and p = 0.0062 for CV). The Michaelis Menten constant calculated from the linear portion of the sensor was found to be $${K}_{m}^{app}$$ K m app of 19.44 µM indicating that our biosensor has a higher affinity to Hydrogen peroxide than other available enzymatic sensors, it is attributed to the unique design of the hemoglobin polymers in microbubble

Label Free, Lateral Flow Prostaglandin E2 Electrochemical Immunosensor for Urinary Tract Infection Diagnosis

A label-free, rapid, and easy-to-use lateral flow electrochemical biosensor was developed for urinary tract infection (UTI) diagnosis in resource challenged areas. The sensor operates in non-faradaic mode and utilizes Electrochemical Impedance Spectroscopy for quantification of Prostaglandin E2, a diagnostic and prognostic urinary biomarker for UTI and recurrent UTI. To achieve high sensitivity in low microliter volumes of neat, unprocessed urine, nanoconfinement of assay biomolecules was achieved by developing a three-electrode planar gold microelectrode system on top of a lateral flow nanoporous membrane. The sensor is capable of giving readouts within 5 min and has a wide dynamic range of 100–4000 pg/mL for urinary PGE2. The sensor is capable of discriminating between low and high levels of PGE2 and hence is capable of threshold classification of urine samples as UTI positive and UTI negative. The sensor through its immunological response (directly related to host immune response) is superior to the commercially available point-of-care UTI dipsticks which are qualitative, have poor specificity for UTI, and have high false-positive rates. The developed sensor shows promise for rapid, easy and cost-effective UTI diagnosis for both clinical and home-based settings. More accurate point-of-care UTI diagnosis will improve patient outcomes and allow for timely and appropriate prescription of antibiotics which can subsequently increase treatment success rates and reduce costs

DigEST: Digital plug‐n‐probe disease Endotyping Sensor Technology

Abstract In this work, we propose a novel diagnostic workflow—DigEST—that will enable stratification of disease states based on severity using multiplexed point of care (POC) biosensors. This work can boost the performance of current POC tests by enabling clear, digestible, and actionable diagnoses to the end user. The scheme can be applied to any disease model, which requires time‐critical disease stratification for personalized treatment. Here, urinary tract infection is explored as the proof‐of‐concept disease model and a four‐class classification of disease severity is discussed. Our method is superior to traditional enzyme‐linked immunosorbent assay (ELISA) as it is faster and can work with multiple disease biomarkers and categorize diseases by endotypes (or disease subtype) and severity. To map the nonlinear nature of biochemical pathways of complex diseases, the method utilizes an established supervised machine learning model for digital classification. This scheme can potentially boost the diagnostic power of current electrochemical biosensors for better precision therapy and improved patient outcomes