Browsing by Subject "Biosensors"

Now showing 1 - 6 of 6
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    Artificial Intelligence in Biomedical Engineering and Its Influence on Healthcare Structure: Current and Future Prospects
    (MDPI, 2025-02-08) Tripathi, Divya; Hajra, Kasturee; Mulukutla, Aditya; Shreshtha, Romi; Maity, Dipak; Chemistry and Chemical Biology, School of Science
    Artificial intelligence (AI) is a growing area of computer science that combines technologies with data science to develop intelligent, highly computation-able systems. Its ability to automatically analyze and query huge sets of data has rendered it essential to many fields such as healthcare. This article introduces you to artificial intelligence, how it works, and what its central role in biomedical engineering is. It brings to light new developments in medical science, why it is being applied in biomedicine, key problems in computer vision and AI, medical applications, diagnostics, and live health monitoring. This paper starts with an introduction to artificial intelligence and its major subfields before moving into how AI is revolutionizing healthcare technology. There is a lot of emphasis on how it will transform biomedical engineering through the use of AI-based devices like biosensors. Not only can these machines detect abnormalities in a patient's physiology, but they also allow for chronic health tracking. Further, this review also provides an overview of the trends of AI-enabled healthcare technologies and concludes that the adoption of artificial intelligence in healthcare will be very high. The most promising are in diagnostics, with highly accurate, non-invasive diagnostics such as advanced imaging and vocal biomarker analyzers leading medicine into the future.
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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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    Optically Switchable Molecular Machine-Inspired Nanoplasmonic Sensing Platforms for Early Cancer Detection
    (2025-05) Langlais, Sarah R.; Sardar, Rajesh; Naumann, Christoph; Deng, Yongming; Goodpaster, John
    Disease diagnostics enable physicians to diagnose cancer and monitor at-risk disease associated pathology sub-populations enabling implementation of lifesaving treatment at the earliest timepoint to improve patient prognosis. However, limitations in biosensing sensitivity and specificity at the point of disease onset and during the early stages of pathogenic progression have hindered identification of biomarkers capable of early clinical diagnostics. Moreover, it has been well documented in literature that the combination of multiple biomarkers from different bimolecular classes, such nucleic acids, proteins, exosomes and exosomal cargo molecules, increases both sensitivity and specificity while mitigating false responses for early cancer diagnostics when marker concentrations and concentration changes occur at extremely low levels. However, to date, scientists have been limited in this endeavor to combining various laboratory techniques in order to pool assay results of a diverse groups of biomarkers from various bimolecular classes. For example, modern bioanalytical techniques such as drop digital or quantitative reverse transcription polymerase chain reaction (ddPCR, qRT-PCR), next generation sequencing (NGS), mass spectrometry (MS) and electrochemistry have been used to assay nucleic acids, while lateral flow assay (LFA), western blot (WB), SERS and enzyme-linked immunosorbent assay (ELISA) are routinely utilized for detection and quantification of proteins. Furthermore, exosomes and exosomal cargo molecules have been assayed using nanopores, microarrays, immunoassays and fluorescence. However, these techniques are also hindered with high occurrences of false positive responses, are extremely labor intensive, require amplification and/or fluorescent labeling, and have extensive sample processing requirements. To overcome these challenges and improve accuracy, diagnostic technology has sought to develop a single platform with multiplex functionality that is also capable of adaptive detection of multiple types of biomarkers simultaneously using a single instrument. Current literature for multiplexed and multiparametric assay capability has been limited to microRNA and Protein detection with nanopores, plasmonics, PCR or mass spectrometry and detection of exosomes and exosomal cargo molecules achieved using microfluidic devices and fluorescence. Unfortunately, there has yet to be a single platform capable for adaptively assaying microRNA, proteins, exosomes and exosomal cargo molecules simultaneously, under identical device constructs in addition to, a device capable of achieving the unprecedented sensitivity and specificity needed for early cancer diagnostics. In this dissertation, a novel localized surface plasmon resonance (LSPR)-based sensing mechanism is introduced and utilized in the development of a photo-switchable molecular machine-inspired diagnostic platform. LSPR is a highly studied nanoscale phenomenon resulting from the oscillations of free electrons on the surface of metallic noble metal nanostructures when irradiated with light. These oscillations can be collected to produce dipole spectral absorption peaks and result in strong electromagnetic near-field enhancements ideal for developing optoelectronic devices. Consequently, this property is highly dependent, and tunable, based on the size, shape and composition of the nanostructure employed. Sensing mechanisms utilizing this phenomenon are conducted by observing a change in absorbance, bulk refractive index, and local refractive index. In this dissertation, a fourth novel mechanism is identified involving the dipole-dipole coupling interactions between the free electrons on the surface of the nanostructure and a zwitterionic spiropyran/merocyanine-based surface ligand. This innovative mechanism is utilized for the fabrication of an optically switchable molecular machine-inspired nanoplasmonic sensor (OSMINS)-based diagnostic platform capable of highly sensitive and specific adaptable assays for multiparametric analysis of patient biofluids. Additionally, the multiplex functionality on the OSMINS platform is ideal for rapid, and both label and amplification-free sample processing. The work presented in this dissertation is presented in five chapters, including: (1) Introduction. (2) Methods. (3) Dipole-dipole coupling mechanism elucidation and utilization in optoelectronic device fabrication to detect microRNA and protein for bladder cancer diagnosis. In this chapter, a new LSPR-based sensing mechanism was identified and explored through the development of a novel single nanostructure-zwitterionic organic molecule coupled plasmonic ruler (PR). A dipole-dipole coupling mechanism is hypothesized and supported through theoretical calculations on dipole polarizability using an inorganic-organic heterodimer model and experimentally by determining work function and interfacial dipole values. A PR is first fabricated utilizing different Au nanostructures (triangular nanoprisms (TNPs), bipyramids (BiPs) and rods (NR)) and then when TNPs and BiPs are found to generate a superior LSPR response, further optimization of the spiropyran (SP) surface concentration via SP-spacer self-assembled monolayer (SAM) ratios is investigated. Given the synergistic relationship between LSPR-based optoelectronic device fabrication and light activated molecular machines, the new dipole-dipole coupling mechanism and PR construct is employed to fabricate an adaptable photo-switching (APS) nanoplasmonic biosensor. The singleplex-based APS biosensor is employed to detect microRNA and protein in human plasma and urine, respectively, for bladder cancer diagnosis. This regenerative and reusable APS biosensor is shown to achieve a femtomolar limit of detection (LOD) assaying 10-healthy control (HC) and 10-metastatic bladder cancer patients attaining p values ranging from 0.0002-0.0001. (4) Fabrication and optimization of an optically-switchable molecular machine-inspired nanoplasmonic sensor (OSMINS)-based diagnostic platform is achieved and utilized in performing microRNA and protein singleplexing assays for early diagnosis of pancreatic cancer (PDAC) from at-risk disease associated pathologies. In this chapter, alkylthiol linker length is optimized for SP bound to TNPs to achieve an ultrasensitive attomolar concentration LOD for detecting circulating microRNA and protein. Two-dimensional conditioned cellular media studies and orthotopically implanted PDAC cell NOD scid gamma (NSG) mouse model study is conducted to assess OSMINS diagnostic potential for early PDAC diagnosis. The OSMINS platform is then deployed to assay oncogenic microRNA and protein in 11-PDAC, 20-chronic pancreatitis (CP), 6-intraductal papillary mucinous neoplasm (IPMN), and 20-HC patients achieving p values of 0.0001 (PDAC vs. HC, IPMN vs. HC), 0.0332 (CP vs. HC) and 0.1234 (PDAC vs. CP, IPMN vs. CP). Biostatistical analysis is used to pool biomarker results, meaning microRNA + protein, to improve CP vs. HC, PDAC vs. CP and IPMN vs. CP comparison p values to 0.0001. Cross-validation of the OSMINS platform is also presented using ddPCR and electrochemiluminescence (ECL) for microRNA and protein assays, respectively, showing excellent correlation. (5) Fabrication of a multiplexed and multiparametric OSMINS-based platform with receptor structure engineered molecular machine-enabled fully customizable assays of circulating microRNA, protein, exosomes and exosomal cargo molecules for early pancreatic cancer detection and prediction of Neoadjuvant chemotherapy (NAC) treatment response. Based on the results, a predictive model is developed for early cancer detection and patient monitoring. In this work, the previously presented OSMINS technology from chapter 4 is expanded and deployed to fabricate a 96 multi-well, high-throughput device for simultaneous assays of multiple biomarkers from various biomolecular classes in a single instrument run, allowing for direct comparison of results for the first time. OSMINS development from a singleplex solid-state biosensor into a multiplexed and multiparametric diagnostic platform is reported and assessed via three-dimensional conditioned cellular media study and a PDAC specific Patient-Derived Xenograft (PDX-21) mouse model study. The multiplexed and multiparametric OSMINS platform is then used to analyze 20-PDAC, 14-low grade IPMN, 6-high grade/invasive IPMN and 20-HC patient plasma samples for direct assay of microRNA, protein and exosomes as well as isolation of exosomes and assay of exosomal lysate for protein and microRNA cargo molecules. This work achieved p values ranging from 0.0001 to 0.1234, which is discussed in detail with regard to type of assay and marker biomolecular classes designation. Validation of multiplexed and multiparametric OSMINS platform is conducted via ddPCR, ELISA, and nanoparticle tracking analysis for microRNAs, proteins and exosomes, respectively. Finally, multiplexed and multiparametric OSMINS-based platform is utilized for 15-PDAC patients before and during NAC treatments to evaluate microRNA and protein biomarkers for their effectiveness in predicting NAC treatment response. Taken together, our multifaceted detection approach utilizing a novel multiplexed and multiparametric OSMINS-based sensing platform represents a paradigm shift in accessing the full diagnostic potential of current and future identified circulating biomarkers and their biomolecular cargo for early cancer diagnosis, monitoring of at-risk associated pathogenic conditions, and as predictive markers for patient treatment response.
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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.