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

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 [Formula: see text] 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.

Hemoglobin Microbubbles and the Prediction of Different Oxygen Levels Using RF Data and Deep Learning.

Ultrasound contrast agents (UCA) are gas-encapsulated microspheres that oscillate volumetrically when exposed to an ultrasound field producing backscattered signals efficiently, which can be used for improved ultrasound imaging and drug delivery applications. We developed a novel oxygen-sensitive hemoglobin-shell microbubble designed to acoustically detect blood oxygen levels. We hypothesize that structural change in hemoglobin caused due to varying oxygen levels in the body can lead to mechanical changes in the shell of the UCA. This can produce detectable changes in the acoustic response that can be used for measuring oxygen levels in the body. In this study, we have shown that oxygenated hemoglobin microbubbles can be differentiated from deoxygenated hemoglobin microbubbles using a 1D convolutional neural network using radiofrequency (RF) data. We were able to classify RF data from oxygenated and deoxygenated hemoglobin microbubbles into the two classes with a testing accuracy of 90.15%. The results suggest that oxygen content in hemoglobin affects the acoustical response and may be used for determining oxygen levels and thus could open many applications, including evaluating hypoxic regions in tumors and the brain, among other blood-oxygen-level-dependent imaging applications.