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Dynamic Insights into Lower Urinary Tract Function: Exploring Intraindividual and Interindividual Variability through Personalized Mathematical Modeling

Dynamic Insights into Lower Urinary Tract Function: Explorin...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Mohamed Zaid Caleb Hendrick Elie Alhajjar John Yin Zachary C. Danziger Giovanna Guidoboni Graduate School of Biomedical Science and Engineering University of Maine Orono ME USA Maine College of Engineering and Computing Orono ME USA Engineering and Applied Sciences Department RAND Arlington VA USA Chemical and Biological Engineering Wisconsin Institute for Discovery University of Wisconsin–Madison Madison Wisconsin USA Department of Rehabilitation Medicine Division of Physical Therapy Emory University Georgia USA W.H. Coulter Department of Biomedical Engineering Emory University Georgia USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

This study proposes a novel approach to obtain personalized estimates of Lower Urinary Tract Function leveraging on the simulated and experimentally acquired bladder pressure. Our findings reveal the model’s adaptability across intraindividual cycles and consistency within specific cohorts, shedding light on the importance of variability in the micturition process. The study emphasizes the significance of intraindividual and interindividual variability, paving the way for improved diagnostics and therapeutic strategies in the field of lower urinary function.

关键词： Neurological diseases Biomechanics Biological system modeling Bladder Rats Biomarkers Mathematical models Data models Complexity theory engineering in medicine and biology

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Robust Unified Graphical Lasso Based on t Distribution

Robust Unified Graphical Lasso Based on t Distribution

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Computational Intelligence in Health and Medicine (CIHM), IEEE Symposium on

作者： Ryohei Moriya Kensuke Tanioka Mariko Takagishi Satoru Hiwa Tomoyuki Hiroyasu Graduate School of Life and Medical Sciences Doshisha University Kyoto Japan Department of Biomedical Sciences and Informatics Doshisha University Kyoto Japan School of Engineering Okayama University Okayama Japan

ISBN: (数字)9798331508333

ISBN: (纸本)9798331508340

Estimating networks among variables from multi-variate data is often important to clarify relationships among variables. Graphical Lasso (GL) is a useful tool for network exploration and is used in genetic data analysis. However, when dealing with genetic data, there are cases where multi-block data observed from several different databases for the same set of variables are handled. Unified Graphical Lasso (UGL) is a method to estimate the common network structure of each block data from such multi-block data. However, it is known that the presence of noise in such data can affect the results of network structure estimation using ordinary UGL. To solve such problems, we propose Robust Unified Graphical Lasso based on t-distribution (robust UGL). In robust UGL, we also developed an algorithm to estimate parameters based on the EM algorithm. We compared and evaluated the results of UGL and robust UGL on artificial and genetic data. Experimental results show that robust UGL performs better than UGL on both artificial and genetic data. These results suggest that robust UGL may be effective for noisy data such as biometric data.

关键词： Data analysis Databases Noise Estimation Network analyzers Genetics Numerical simulation Noise measurement Unsupervised learning Computational intelligence

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Robust Unified Graphical Lasso based on t distribution

Robust Unified Graphical Lasso based on t distribution

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2025 IEEE Symposium on Computational Intelligence in Health and Medicine, CIHM 2025

作者： Moriya, Ryohei Tanioka, Kensuke Takagishi, Mariko Hiwa, Satoru Hiroyasu, Tomoyuki Graduate School of Life and Medical Sciences Doshisha University Kyoto Japan Department of Biomedical Sciences and Informatics Doshisha University Kyoto Japan School of Engineering Okayama University Okayama Japan

ISBN: (纸本)9798331508333

Estimating networks among variables from multivariate data is often important to clarify relationships among variables. Graphical Lasso (GL) is a useful tool for network exploration and is used in genetic data analysis. However, when dealing with genetic data, there are cases where multi-block data observed from several different databases for the same set of variables are handled. Unified Graphical Lasso (UGL) is a method to estimate the common network structure of each block data from such multi-block data. However, it is known that the presence of noise in such data can affect the results of network structure estimation using ordinary UGL. To solve such problems, we propose Robust Unified Graphical Lasso based on t-distribution (robust UGL). In robust UGL, we also developed an algorithm to estimate parameters based on the EM algorithm. We compared and evaluated the results of UGL and robust UGL on artificial and genetic data. Experimental results show that robust UGL performs better than UGL on both artificial and genetic data. These results suggest that robust UGL may be effective for noisy data such as biometric data. © 2025 IEEE.

关键词： Genetic algorithms

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Peripheral pulse harmonic analysis and its clinical application: A systematic review

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Journal of Traditional and Complementary Medicine 2024年第2期14卷 223-236页

作者： Hsiao, Kun-Yuan Kang, Jiunn-Horng Wu, Yeong-Shing Chang, Hen-Hong Yang, Cheng-Ta Institute of Allied Health Sciences National Cheng Kung University Tainan Taiwan Department of Chinese Medicine Cishan Hospital Ministry of Health and Welfare Kaohsiung Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan Graduate Institute of Nanomedicine and Medical Engineering College of Biomedical Engineering Taipei Medical University Taipei Taiwan Department of Physical Medicine and Rehabilitation Taipei Medical University Hospital Taipei Taiwan Graduate Institute of Integrated Medicine College of Chinese Medicine and Chinese Medicine Research Center China Medical University Taichung Taiwan Department of Chinese Medicine China Medical University Hospital Taichung Taiwan Department of Psychology National Cheng Kung University Tainan Taiwan Graduate Institute of Mind Brain and Consciousness Taipei Medical University Taipei Taiwan Graduate Institute of Health and Biotechnology Law Taipei Medical University Taipei City Taiwan

Introduction: Pulse harmonic analysis is a quantitative and objective methodology within traditional Chinese medicine (TCM) used to evaluate pulse characteristics. However, interpreting pulse wave data is challenging due to its inherent complexity. This study aims to provide a comprehensive review and comparison of existing human pulse wave harmonic analysis methods to elucidate their patterns and characteristics. Methods: A systematic review of clinical research reports published from 1990 to 2021 was conducted, focusing on variations in harmonic characteristics across different medical conditions and physiological states. Keyword searches included terms related to analysis methods (e.g., “Pulse Spectrum,” "harmonic analysis," “harmonic index”) and measured indicators (e.g., “vascular response,” "PPG," “Photoplethysmography,” "aortic," “arterial,” "blood pressure"). Supplementary research using PubMed's Mesh terms specifically targeted “Pulse wave analysis” within the methods and statistical analysis domain. Articles were filtered based on predefined criteria, including human participants and research related to pulse pressure or vascular volume changes. Conference papers, animal studies, and irrelevant research were excluded, with literature evaluation scales selected based on the retrieved research reports. Results: Initially, 6487 research reports were identified, and after screening, 50 reports were included in the review. The analysis revealed that low-frequency harmonics increase following vigorous activity or sympathetic excitation but decrease during rest or parasympathetic excitation. Cardiovascular patients exhibited elevated first harmonics associated with the liver meridian, while diabetes patients displayed weakened third harmonics related to the spleen meridian. Liver dysfunction was linked to changes in the first harmonic, and cancer patients showed signs of liver and kidney yin deficiency in the first and second harmonics. These findings underscore

关键词： Diagnostic tools Harmonic characteristics Pulse diagnosis Pulse harmonic analysis Traditional Chinese medicine

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System for the analysis of human balance based on accelerometers and support vector machines

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Computer Methods and programs in Biomedicine Update 2023年 4卷

作者： Pinheiro, V.C. do Carmo, J.C. Nascimento, F.A. de O. Miosso, C.J. Electrical Engineering Graduate Program Department of Electrical Engineering University of Brasília DF Brasília 70910-900 Brazil Biomedical Engineering Graduate Program University of Brasília at Gama Área Especial Projeção A UnB - Setor Leste Gama DF Brasília 72444-240 Brazil

Disturbances in balance control lead to movement impairment and severe discomfort, dizziness, vertigo and may also lead to serious accidents. It is important to monitor the level of balance in order to determine the risk of a fall and to evaluate progress during treatment. Some solutions exist, but they are generally restricted to indoor environments. We propose and evaluate a system, based on accelerometers and support vector machines, that indicates the user's postural balance variation which can be used in indoor and outdoor environments. For the training phase of the system, we used the accelerometer signals acquired from a single subject under monitored conditions of balance and intentional imbalance, and used the scores provided by the SWAY®software for establishing the reference target values. Based on these targets, we trained a support vector machine to classify the signal into n levels of balance and later evaluated the performance using cross validation by random resampling. We also developed a support vector machine approach for estimating the center of pressure, by using as reference targets the results from a force platform. For validation, we performed experiments with a subject who was performing determined movements. Later other experiments were executed, so the different centers of pressure could be computed by our system and compared to the results from the force platform. We also performed tests with a dummy and a John Doe doll, in order to observe the system's behavior in the presence of a sudden drop or a lack of balance. The results show that the system can classify the acquired signals into two to seven levels of balance, with significant accuracy, and was also able to infer the centroid of each center of pressure region with an error lower than 0.9 cm. The tests performed with the dolls show that the system is able to distinguish between the conditions of a sudden drop and of a recovery of balance after losing one's balance. The results sugg

关键词： Accelerometers Balance signals processing Images processing Postural balance Support vector machines

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Noggin expression in human nucleus pulposus cells: A comparative study of degenerative and traumatic intervertebral discs

Advanced Orthopaedics

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Advanced Orthopaedics 2025年 1卷 55-62页

作者： Shuimu Chen Sebastian F. Bigdon Sonja Häckel Zhen Li Christoph E. Albers Benjamin Gantenbein Department of Orthopaedic Surgery & Traumatology Inselspital Bern University Hospital Faculty of Medicine University of Bern Bern CH-3010 Switzerland Tissue Engineering for Orthopaedics & Mechanobiology (TOM) Bone & Joint Program Department for BioMedical Research (DBMR) Faculty of Medicine University of Bern Bern CH-3008 Switzerland Graduate School for Cellular and Biomedical Sciences (GCB) University of Bern Bern CH-3012 Switzerland Graduate School for Health Sciences (GHS) University of Bern CH-3012 Bern Switzerland AO Research Institute Davos Davos CH-7270 Switzerland

Low back pain (LBP) is a health issue that leads to considerable disability and socioeconomic burdens. The degeneration of the human intervertebral disc (IVD) is a key factor in the pathogenesis of LBP. During disc degeneration, the nucleus pulposus (NP), a critical component of IVD, undergoes significant alterations in structural composition and function. Recent studies have highlighted the importance of various genes in IVD health. Noggin has been described as an essential gene in nucleus pulposus cells (NPCs) and a central role in inhibiting osteogenesis. In this study, we conducted a comparative analysis of Noggin expression in human NPCs from degenerative and healthy IVD. Twenty-six IVD samples (nineteen from degeneration and seven from trauma) were collected to separate NPCs for RNA extraction. Noggin expression was quantified at the transcript level by qPCR to analyze the difference between degenerative and traumatic IVD. Results indicated that Noggin expression was lower in NPCs from degenerated IVD compared to trauma cases. The severity of IVD degeneration correlated with decreased Noggin levels. Traumatic males exhibited higher Noggin expression than traumatic females and those with IVD degeneration. Noggin expression peaked in young adults (18–45 years), decreased in middle age (46–69 years), and rose again in the elderly (over 69 years), but remained lower than in the youth. Furthermore, upper lumbar discs (T12/L1-L2/3) showed higher Noggin levels than lower lumbar discs (L3/4/-L5/S1). This research elucidates valuable insights into the variability of Noggin concerning sex, age, degree of disc degeneration, and disc segment. It provides implications for IVD health and LBP treatment.

关键词： Low back pain Human intervertebral disc Nucleus pulposus cells Noggin

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Adaptive Gait Model with Hybrid Control for Mass Production Oriented Exoskeleton

Adaptive Gait Model with Hybrid Control for Mass Production ...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Chang-Wen Wang Yun-Tian Wang Keisuke Osawa Kei Nakagawa Eiichiro Tanaka Graduate School of Information Production and Systems Waseda University Fukuoka Japan Department of Mechanical Engineering Faculty of Engineering Kyushu University Fukuoka Japan Graduate School of Biomedical and Health Sciences Hiroshima University 1-2-3 Kasumi Minami-ku Hiroshima Japan

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Exoskeletons are one of the technologies that can change the human future in many directions. However, they are not used in practice due to being expensive to produce. Currently, experts have to tune exoskeletons before they are delivered to users. As a result, it cannot be mass-produced and is limited to a specific environment. To allow the exoskeleton to be more friendly to different types of users, this study aims to find a model that can adjust according to the user’s body parameters. This work first built the reference gait model based on a real human dataset with 138 patients’ casual walking data. We discovered that although every person has a different walking speed, the angle change at each joint is the same. The walking speed difference is created due to different limb lengths. Based on the findings discovered in the dataset, a method to transfer the reference gait model to the one suitable for the user is proposed. Later, a hybrid control is presented to control the ankle individually. For walking, the foot is an essential part that will contact the ground. However, the terrain is not always totally flat for the exoskeleton to walk on. As a result, finding a correct contact foot angle can reduce hazards when the user is in the contact phase of a gait cycle. Additionally, a walking speed adjustment algorithm is integrated into this work so that the system can adjust the ankle’s angle to respond to the user’s motion if the user walks faster or slower than the reference gait speed. The proposed hybrid control allows the exoskeleton to adapt to different users with minimal human interference, which brings exoskeletons closer to being mass-produced and more accessible to access by the public.

关键词： Legged locomotion Ankle Adaptation models Mass production Mechatronics Exoskeletons Interference Hazards Robots Foot

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A Computationally Efficient Algorithm for Estimating Respiratory Rate from Seismocardiogram

SSRN

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SSRN 2024年

作者： Ku, Tienhsiung Lin, Yue-Der Department of Anesthesiology Changhua Christian Hospital Changhua50051 Taiwan Artificial Intelligence Development Center Changhua Christian Hospital Changhua50051 Taiwan Department of Automatic Control Engineering Feng Chia University Taichung40724 Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung40724 Taiwan

Respiratory rate (RR) monitoring is crucial in clinical and healthcare settings. With the advancements in microelectromechanical system (MEMS) technology, monitoring respiration using seismocardiogram (SCG) is emerging as a promising approach. This study presents a computationally efficient algorithm for estimating RR from SCG signal. The proposed algorithm consists of two primary steps: firstly applying Gaussian averaging filter for pre-processing and next computing the averaged spectrum from the scalogram based on complex Morlet wavelet within the specified frequency range. RR is then estimated from the dominant frequency appeared in the averaged spectrum. This study also establishes the analytical relationship between the parameters of Gaussian averaging filter and the sampling frequency of SCG signal, which serves as the design criterion for pre-processing filter. The proposed algorithm undergoes testing on two SCG datasets and has demonstrated its feasibility for estimating RR from SCG signal. Furthermore, the algorithm is also tested on both finger and wrist PPG datasets for RR estimation. The test results present high agreement between the RR estimations from SCG (or PPG) signal and those from respiration signal. Based on these testing results, the proposed algorithm shows significant potential in clinical and healthcare applications. © 2024, The Authors. All rights reserved.

关键词： Computational efficiency

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Unsupervised Domain Adaptation for Human and Animal Chest X-Ray Bone Suppression

Unsupervised Domain Adaptation for Human and Animal Chest X-...

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IEEE International Symposium on biomedical Imaging

作者： Tomohiro Hashimoto Ayaka Maruo Shoichiro Sekiguchi Yoshitaka Ushiku Genya Shimbo Ridge-I Inc. Tokyo Japan Graduate School of Biomedical Science and Engineering Hokkaido University Hokkaido Japan Veterinary Teaching Hospital Graduate School of Veterinary Medicine Hokkaido University Hokkaido Japan

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Bone suppression of chest X-ray images is an effective method for improving the reliability of diagnosis of lung diseases by X-ray reading. Deep learning-based methods for bone suppression, which learn the difference between a pair of images with and without bone, have attracted much attention due to their high performance. However, boneless images are not always available especially for animals, making it hard to use existing training methods. Therefore, we propose an unsupervised bone suppression method that does not require any boneless X-ray images. Unsupervised domain adaptation is performed at the input and feature levels using CT data as the source domain and X-ray images as the target domain. We verified the effectiveness of the proposed method by qualitative evaluation on an original animal dataset and quantitative evaluation on public human dataset.

关键词： Training Learning systems Histograms Animals Pulmonary diseases Impedance matching Bones

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Multiplex Magnetic Biosensor Microarrays Facilitate Personalized Vaccination Schedules for Sars-Cov-2 Variants

SSRN

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SSRN 2024年

作者： Um, Hyun-Kyung Kim, Songeun Wang, Shan X. Lee, Jung-Rok Department of Mechanical and Biomedical Engineering Ewha Womans University Seoul03760 Korea Republic of Graduate Program in Smart Factory Ewha Womans University Seoul03760 Korea Republic of Department of Materials Science and Engineering Stanford University Stanford CA94305 United States Department of Electrical Engineering Stanford University Stanford CA94305 United States

Rapid, sensitive, multiplexed antibody detection technologies are essential for assessing vaccine efficacy and recipient immunity against viruses. As severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants continue to emerge, and the fact that novel viruses may pose potential threats in the near future, personalized vaccines can be precisely tailored to an individual’s measured immunity to help mitigate the symptoms of infection and viral spread. Here, we present giant magnetoresistive (GMR) biosensor microarrays for the quantitative multiplexed detection of antibodies against SARS-CoV-2 variants. The multiplexed GMR biosensor microarrays demonstrated high sensitivity, comparable to that of the single-plex enzyme-linked immunosorbent assay, enabling multiplexed measurements using blood obtained from finger pricks. Additionally, the GMR biosensor microarrays demonstrated compatibility with clinical samples and could detect variant-specific antibodies in serum, allowing for the assessment of current immunity status. Notably, an increased concentration of antibodies against the Omicron variant was clearly observed two weeks after receiving an Omicron-based booster vaccination. These results indicate that GMR biosensor microarrays offer a practical point-of-care tool for monitoring humoral immunity and guiding personalized vaccination schedules, thereby supporting immunity management against SARS-CoV-2 and future viral threats. © 2024, The Authors. All rights reserved.

关键词： Giant magnetoresistance

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