Browsing by Subject "Biosensors"

Now showing 1 - 4 of 4
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    Characterization of Membrane Patch-Ion Channel Probes for Scanning Ion Conductance Microscopy
    (Wiley, 2018-05) Shi, Wenqing; Zeng, Yuhan; Zhu, Cheng; Xiao, Yucheng; Cummins, Theodore R.; Hou, Jianghui; Baker, Lane A.; Biology, School of Science
    Integration of dual‐barrel membrane patch‐ion channel probes (MP‐ICPs) to scanning ion conductance microscopy (SICM) holds promise of providing a revolutionized approach of spatially resolved chemical sensing. A series of experiments are performed to further the understanding of the system and to answer some fundamental questions, in preparation for future developments of this approach. First, MP‐ICPs are constructed that contain different types of ion channels including transient receptor potential vanilloid 1 and large conductance Ca2+‐activated K+ channels to establish the generalizability of the methods. Next, the capability of the MP‐ICP platforms in single ion channel activity measurements is proved. In addition, the interplay between the SICM barrel and the ICP barrel is studied. For ion channels gated by uncharged ligands, channel activity at the ICP barrel is unaffected by the SICM barrel potential; whereas for ion channels that are gated by charged ligands, enhanced channel activity can be obtained by biasing the SICM barrel at potentials with opposite polarity to the charge of the ligand molecules. Finally, a proof‐of‐principle experiment is performed and site‐specific molecular/ionic flux sensing is demonstrated at single‐ion‐channel level, which show that the MP‐ICP platform can be used to quantify local molecular/ionic concentrations.
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    Integrated sensing and delivery of oxygen for next-generation smart wound dressings
    (Springer Nature, 2020-05-18) Ochoa, Manuel; Rahimi, Rahim; Zhou, Jiawei; Jiang, Hongjie; Yoon, Chang Keun; Maddipatla, Dinesh; Narakathu, Binu Baby; Jain, Vaibhav; Oscai, Mark Michael; Morken, Thaddeus Joseph; Oliveira, Rebeca Hannah; Campana, Gonzalo L.; Cummings, Oscar W.; Zieger, Michael A.; Sood, Rajiv; Atashbar, Massood Z.; Ziaie, Babak; Pathology and Laboratory Medicine, School of Medicine
    Chronic wounds affect over 6.5 million Americans and are notoriously difficult to treat. Suboptimal oxygenation of the wound bed is one of the most critical and treatable wound management factors, but existing oxygenation systems do not enable concurrent measurement and delivery of oxygen in a convenient wearable platform. Thus, we developed a low-cost alternative for continuous O2 delivery and sensing comprising of an inexpensive, paper-based, biocompatible, flexible platform for locally generating and measuring oxygen in a wound region. The platform takes advantage of recent developments in the fabrication of flexible microsystems including the incorporation of paper as a substrate and the use of a scalable manufacturing technology, inkjet printing. Here, we demonstrate the functionality of the oxygenation patch, capable of increasing oxygen concentration in a gel substrate by 13% (5 ppm) in 1 h. The platform is able to sense oxygen in a range of 5–26 ppm. In vivo studies demonstrate the biocompatibility of the patch and its ability to double or triple the oxygen level in the wound bed to clinically relevant levels.
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    Mobile devices for the remote acquisition of physiological and behavioral biomarkers in psychiatric clinical research
    (Elsevier, 2017-02) Adams, Zachary; McClure, Erin A.; Gray, Kevin M.; Danielson, Carla Kmett; Treiber, Frank A.; Ruggiero, Kenneth J.; Psychiatry, School of Medicine
    Psychiatric disorders are linked to a variety of biological, psychological, and contextual causes and consequences. Laboratory studies have elucidated the importance of several key physiological and behavioral biomarkers in the study of psychiatric disorders, but much less is known about the role of these biomarkers in naturalistic settings. These gaps are largely driven by methodological barriers to assessing biomarker data rapidly, reliably, and frequently outside the clinic or laboratory. Mobile health (mHealth) tools offer new opportunities to study relevant biomarkers in concert with other types of data (e.g., self-reports, global positioning system data). This review provides an overview on the state of this emerging field and describes examples from the literature where mHealth tools have been used to measure a wide array of biomarkers in the context of psychiatric functioning (e.g., psychological stress, anxiety, autism, substance use). We also outline advantages and special considerations for incorporating mHealth tools for remote biomarker measurement into studies of psychiatric illness and treatment and identify several specific opportunities for expanding this promising methodology. Integrating mHealth tools into this area may dramatically improve psychiatric science and facilitate highly personalized clinical care of psychiatric disorders.
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    Utilizing multimodal AI to improve genetic analyses of cardiovascular traits
    (medRxiv, 2024-03-20) Zhou, Yuchen; Cosentino, Justin; Yun, Taedong; Biradar, Mahantesh I.; Shreibati, Jacqueline; Lai, Dongbing; Schwantes-An, Tae-Hwi; Luben, Robert; McCaw, Zachary; Engmann, Jorgen; Providencia, Rui; Schmidt, Amand Floriaan; Munroe, Patricia; Yang, Howard; Carroll, Andrew; Khawaja, Anthony P.; McLean, Cory Y.; Behsaz, Babak; Hormozdiari, Farhad; Medical and Molecular Genetics, School of Medicine
    Electronic health records, biobanks, and wearable biosensors contain multiple high-dimensional clinical data (HDCD) modalities (e.g., ECG, Photoplethysmography (PPG), and MRI) for each individual. Access to multimodal HDCD provides a unique opportunity for genetic studies of complex traits because different modalities relevant to a single physiological system (e.g., circulatory system) encode complementary and overlapping information. We propose a novel multimodal deep learning method, M-REGLE, for discovering genetic associations from a joint representation of multiple complementary HDCD modalities. We showcase the effectiveness of this model by applying it to several cardiovascular modalities. M-REGLE jointly learns a lower representation (i.e., latent factors) of multimodal HDCD using a convolutional variational autoencoder, performs genome wide association studies (GWAS) on each latent factor, then combines the results to study the genetics of the underlying system. To validate the advantages of M-REGLE and multimodal learning, we apply it to common cardiovascular modalities (PPG and ECG), and compare its results to unimodal learning methods in which representations are learned from each data modality separately, but the downstream genetic analyses are performed on the combined unimodal representations. M-REGLE identifies 19.3% more loci on the 12-lead ECG dataset, 13.0% more loci on the ECG lead I + PPG dataset, and its genetic risk score significantly outperforms the unimodal risk score at predicting cardiac phenotypes, such as atrial fibrillation (Afib), in multiple biobanks.