Browsing by Subject "Biomedical engineering"
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Item A model of tension-induced fiber growth predicts white matter organization during brain folding(Springer Nature, 2021-11-18) Garcia, Kara E.; Wang, Xiaojie; Kroenke, Christopher D.; Radiology and Imaging Sciences, School of MedicineThe past decade has experienced renewed interest in the physical processes that fold the developing cerebral cortex. Biomechanical models and experiments suggest that growth of the cortex, outpacing growth of underlying subcortical tissue (prospective white matter), is sufficient to induce folding. However, current models do not explain the well-established links between white matter organization and fold morphology, nor do they consider subcortical remodeling that occurs during the period of folding. Here we propose a framework by which cortical folding may induce subcortical fiber growth and organization. Simulations incorporating stress-induced fiber elongation indicate that subcortical stresses resulting from folding are sufficient to induce stereotyped fiber organization beneath gyri and sulci. Model predictions are supported by high-resolution ex vivo diffusion tensor imaging of the developing rhesus macaque brain. Together, results provide support for the theory of cortical growth-induced folding and indicate that mechanical feedback plays a significant role in brain connectivity.Item A neurotechnological aid for semi-autonomous suction in robotic-assisted surgery(Springer, 2022-03-16) Barragan, Juan Antonio; Yang, Jing; Yu, Denny; Wachs, Juan P.; Surgery, School of MedicineAdoption of robotic-assisted surgery has steadily increased as it improves the surgeon’s dexterity and visualization. Despite these advantages, the success of a robotic procedure is highly dependent on the availability of a proficient surgical assistant that can collaborate with the surgeon. With the introduction of novel medical devices, the surgeon has taken over some of the surgical assistant’s tasks to increase their independence. This, however, has also resulted in surgeons experiencing higher levels of cognitive demands that can lead to reduced performance. In this work, we proposed a neurotechnology-based semi-autonomous assistant to release the main surgeon of the additional cognitive demands of a critical support task: blood suction. To create a more synergistic collaboration between the surgeon and the robotic assistant, a real-time cognitive workload assessment system based on EEG signals and eye-tracking was introduced. A computational experiment demonstrates that cognitive workload can be effectively detected with an 80% accuracy. Then, we show how the surgical performance can be improved by using the neurotechnological autonomous assistant as a close feedback loop to prevent states of high cognitive demands. Our findings highlight the potential of utilizing real-time cognitive workload assessments to improve the collaboration between an autonomous algorithm and the surgeon.Item A Qualitative Study of Biomedical Engineering Student Critical Reflection During Clinical Immersion Experiences(Springer Nature, 2024) Tabassum, Nawshin; Higbee, Steven; Miller, Sharon; Economics, School of Liberal ArtsPurpose: Clinical immersion experiences provide engineering students with opportunities to identify unmet user needs and to interact with clinical professionals. These experiences have become common features of undergraduate biomedical engineering curricula, with many published examples in the literature. There are, however, few or no published studies that describe rigorous qualitative analysis of biomedical engineering student reflections from immersion programs. Methods: Fifteen reflection prompts that align with program learning goals were developed and structured based on the DEAL model for critical reflection. Undergraduate participants in a summer immersion program responded to these prompts throughout five weeks of clinical rotations. Data from two summer cohorts of participants (n = 20) were collected, and thematic analysis was performed to characterize student responses. Results: Students reported learning about key healthcare topics, such as medical insurance, access to healthcare (and lack thereof), stakeholder perspectives, and key medical terminology and knowledge. Most reflections also noted that students could apply newly gained medical knowledge to biomedical engineering design. Further, clinical immersion provided students with a realistic view of the biomedical engineering profession and potential areas for future professional growth, with many reflections identifying the ability to communicate with a variety of professionals as key to student training. Some students reflected on conversations with patients, noting that these interactions reinvigorated their passion for the biomedical engineering field. Finally, 63% of student reflections identified instances in which patients of low socioeconomic status were disadvantaged in health care settings. Conclusions: Clinical immersion programs can help close the gap between academic learning and the practical experience demands of the field, as design skills and product development experience are becoming increasingly necessary for biomedical engineers. Our work initiates efforts toward more rigorous analysis of students' reactions and experiences, particularly around socioeconomic and demographic factors, which may provide guidance for continuous improvement and development of clinical experiences for biomedical engineers.Item 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 ScienceArtificial 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.Item Computational Methods and Models in Circulatory and Reproductive Systems(Hindawi, 2016) Tian, Fang-Bao; Sui, Yi; Zhu, Luoding; Shu, Chang; Sung, Hyung J.; Department of Mathematical Sciences, School of ScienceItem Designing spatial and temporal control of vaccine responses(Springer Nature, 2022) Roth, Gillie A.; Picece, Vittoria C. T. M.; Ou, Ben S.; Luo, Wei; Pulendran, Bali; Appel, Eric A.; Microbiology and Immunology, School of MedicineVaccines are the key technology to combat existing and emerging infectious diseases. However, increasing the potency, quality and durability of the vaccine response remains a challenge. As our knowledge of the immune system deepens, it becomes clear that vaccine components must be in the right place at the right time to orchestrate a potent and durable response. Material platforms, such as nanoparticles, hydrogels and microneedles, can be engineered to spatially and temporally control the interactions of vaccine components with immune cells. Materials-based vaccination strategies can augment the immune response by improving innate immune cell activation, creating local inflammatory niches, targeting lymph node delivery and controlling the time frame of vaccine delivery, with the goal of inducing enhanced memory immunity to protect against future infections. In this Review, we highlight the biological mechanisms underlying strong humoral and cell-mediated immune responses and explore materials design strategies to manipulate and control these mechanisms.Item Developing a Neural Signal Processor Using the Extended Analog Computer(2013-08-21) Soliman, Muller Mark; Yoshida, Ken; Eberhart, Russell C.; Mills, Jonathan W. (Jonathan Wayne); Berbari, Edward J.Neural signal processing to decode neural activity has been an active research area in the last few decades. The next generation of advanced multi-electrode neuroprosthetic devices aim to detect a multiplicity of channels from multiple electrodes, making the relatively time-critical processing problem massively parallel and pushing the computational demands beyond the limits of current embedded digital signal processing (DSP) techniques. To overcome these limitations, a new hybrid computational technique was explored, the Extended Analog Computer (EAC). The EAC is a digitally confgurable analog computer that takes advantage of the intrinsic ability of manifolds to solve partial diferential equations (PDEs). They are extremely fast, require little power, and have great potential for mobile computing applications. In this thesis, the EAC architecture and the mechanism of the formation of potential/current manifolds was derived and analyzed to capture its theoretical mode of operation. A new mode of operation, resistance mode, was developed and a method was devised to sample temporal data and allow their use on the EAC. The method was validated by demonstration of the device solving linear diferential equations and linear functions, and implementing arbitrary finite impulse response (FIR) and infinite impulse response (IIR) linear flters. These results were compared to conventional DSP results. A practical application to the neural computing task was further demonstrated by implementing a matched filter with the EAC simulator and the physical prototype to detect single fiber action potential from multiunit data streams derived from recorded raw electroneurograms. Exclusion error (type 1 error) and inclusion error (type 2 error) were calculated to evaluate the detection rate of the matched filter implemented on the EAC. The detection rates were found to be statistically equivalent to that from DSP simulations with exclusion and inclusion errors at 0% and 1%, respectively.Item Effects of Collagen Gel Stiffness on Cdc42 Activities of Endothelial Colony Forming Cells during Early Vacuole Formation(2013-08-14) Kim, Seung Joon; Na, Sungsoo; Xie, Dong; Li, JiliangRecent preclinical reports have provided evidence that endothelial colony forming cells (ECFCs), a subset of endothelial progenitor cells, significantly improve vessel formation, largely due to their robust vasculogenic potential. While it has been known that the Rho family GTPase Cdc42 is involved in this ECFC-driven vessel formation process, the effect of extracellular matrix (ECM) stiffness on its activity during vessel formation is largely unknown. Using a fluorescence resonance energy transfer (FRET)-based Cdc42 biosensor, we examined the spatio-temporal activity of Cdc42 of ECFCs in three-dimensional (3D) collagen matrices with varying stiffness. The result revealed that ECFCs exhibited an increase in Cdc42 activity in a soft (150 Pa) matrix, while they were much less responsive in a rigid (1 kPa) matrix. In both soft and rigid matrices, Cdc42 was highly activated near vacuoles. However, its activity is higher in a soft matrix than that in a rigid matrix. The observed Cdc42 activity was closely associated with vacuole formation. Soft matrices induced higher Cdc42 activity and faster vacuole formation than rigid matrices. However, vacuole area is not dependent on the stiffness of matrices. Time courses of Cdc42 activity and vacuole formation data revealed that Cdc42 activity proceeds vacuole formation. Collectively, these results suggest that matrix stiffness is critical in regulating Cdc42 activity in ECFCs and its activation is an important step in early vacuole formation.Item Electro-Quasistatic Animal Body Communication for Untethered Rodent Biopotential Recording(Springer Nature, 2021-02-08) Sriram, Shreeya; Avlani, Shitij; Ward, Matthew P.; Sen, Shreyas; Medicine, School of MedicineContinuous multi-channel monitoring of biopotential signals is vital in understanding the body as a whole, facilitating accurate models and predictions in neural research. The current state of the art in wireless technologies for untethered biopotential recordings rely on radiative electromagnetic (EM) fields. In such transmissions, only a small fraction of this energy is received since the EM fields are widely radiated resulting in lossy inefficient systems. Using the body as a communication medium (similar to a ’wire’) allows for the containment of the energy within the body, yielding order(s) of magnitude lower energy than radiative EM communication. In this work, we introduce Animal Body Communication (ABC), which utilizes the concept of using the body as a medium into the domain of untethered animal biopotential recording. This work, for the first time, develops the theory and models for animal body communication circuitry and channel loss. Using this theoretical model, a sub-inch3 [1″ × 1″ × 0.4″], custom-designed sensor node is built using off the shelf components which is capable of sensing and transmitting biopotential signals, through the body of the rat at significantly lower powers compared to traditional wireless transmissions. In-vivo experimental analysis proves that ABC successfully transmits acquired electrocardiogram (EKG) signals through the body with correlation >99% when compared to traditional wireless communication modalities, with a 50× reduction in power consumption.Item Exploring arterial tissue microstructural organization using non-Gaussian diffusion magnetic resonance schemes(Springer Nature, 2021-11-15) Shahid, Syed Salman; Johnston, Robert D.; Smekens, Celine; Kerskens, Christian; Gaul, Robert; Tornifoglio, Brooke; Stone, Alan J.; Lally, Caitríona; Radiology and Imaging Sciences, School of MedicineThe purpose of this study was to characterize the alterations in microstructural organization of arterial tissue using higher-order diffusion magnetic resonance schemes. Three porcine carotid artery models namely; native, collagenase treated and decellularized, were used to estimate the contribution of collagen and smooth muscle cells (SMC) on diffusion signal attenuation using gaussian and non-gaussian schemes. The samples were imaged in a 7 T preclinical scanner. High spatial and angular resolution diffusion weighted images (DWIs) were acquired using two multi-shell (max b-value = 3000 s/mm2) acquisition protocols. The processed DWIs were fitted using monoexponential, stretched-exponential, kurtosis and bi-exponential schemes. Directionally variant and invariant microstructural parametric maps of the three artery models were obtained from the diffusion schemes. The parametric maps were used to assess the sensitivity of each diffusion scheme to collagen and SMC composition in arterial microstructural environment. The inter-model comparison showed significant differences across the considered models. The bi-exponential scheme based slow diffusion compartment (Ds) was highest in the absence of collagen, compared to native and decellularized microenvironments. In intra-model comparison, kurtosis along the radial direction was the highest. Overall, the results of this study demonstrate the efficacy of higher order dMRI schemes in mapping constituent specific alterations in arterial microstructure.