Development and Characterization of Hemoglobin Microbubbles for Acoustic Blood Oxygen Level Dependent Imaging.

Oxygen levels in tissues and organs are crucial for their normal functioning, and approaches to monitor them non-invasively have wide biological and clinical applications. In this study, we developed a method of acoustically detecting oxygenation using contrast-enhanced ultrasound (CEUS) imaging. Our approach involved the use of specially designed hemoglobin-based microbubbles (HbMBs) that reversibly bind to oxygen and alter the state-dependent acoustic response. We confirmed that the bioactivity of hemoglobin remained intact after the microbubble shell was formed, and we did not observe any significant loss of heme. We conducted passive cavitation detection (PCD) experiments to confirm whether the acoustic properties of HbMBs vary based on the level of oxygen present. The experiments involved driving the HbMBs with a 1.1 MHz focused ultrasound transducer. Through the PCD data collected, we observed significant differences in the subharmonic and harmonic responses of the HbMBs when exposed to an oxygen-rich environment versus an oxygen-depleted one. We used a programmable ultrasound system to capture high-frame rate B mode videos of HbMBs in both oxy and deoxy conditions at the same time in a two-chambered flow phantom and observed that the mean pixel intensity of deoxygenated HbMB was greater than in the oxygenated state using B-mode imaging. Finally, we demonstrated that HbMBs can circulate in vivo and are detectable by a clinical ultrasound scanner. To summarize, our results indicate that CEUS imaging with HbMB has the potential to detect changes in tissue oxygenation and could be a valuable tool for clinical purposes in monitoring regional blood oxygen levels.

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.

Identifying Unique Acoustic Signatures from Chemically-Crosslinked Microbubble Clusters Using Deep Learning.

Ultrasound contrast agents (UCA) are gas encapsulated microspheres that oscillate volumetrically when exposed to an ultrasound field producing a backscattered signal which can be used for improved ultrasound imaging and drug delivery. UCA's are being used widely for contrast-enhanced ultrasound imaging, but there is a need for improved UCAs to develop faster and more accurate contrast agent detection algorithms. Recently, we introduced a new class of lipid based UCAs called Chemically Cross-linked Microbubble Clusters (CCMCs). CCMCs are formed by the physical tethering of individual lipid microbubbles into a larger aggregate cluster. The advantages of these novel CCMCs are their ability to fuse together when exposed to low intensity pulsed ultrasound (US), potentially generating unique acoustic signatures that can enable better contrast agent detection. In this study, our main objective is to demonstrate that the acoustic response of CCMCs is unique and distinct when compared to individual UCAs using deep learning algorithms. Acoustic characterization of CCMCs and individual bubbles was performed using a broadband hydrophone or a clinical transducer attached to a Verasonics Vantage 256. A simple artificial neural network (ANN) was trained and used to classify raw 1D RF ultrasound data as either from CCMC or non-tethered individual bubble populations of UCAs. The ANN was able to classify CCMCs at an accuracy of 93.8% for data collected from broadband hydrophone and 90% for data collected using Verasonics with a clinical transducer. The results obtained suggest the acoustic response of CCMCs is unique and has the potential to be used in developing a novel contrast agent detection technique.