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

作者： Amr Al Abed Evelyn Foster Jeremy L. Pinyon Chung Hon Li Gary J. Cowin Gary D. Housley Nigel H. Lovell Graduate School of Biomedical Engineering UNSW Sydney NSW 2052 Australia Translational Neuroscience Facility and the Department of Physiology UNSW Sydney NSW 2052 Australia National Imaging Facility Centre for Advanced Imaging UQ Brisbane Queensland 4072 Australia

We present a computational model for predicting electric field distributions following array-based closed-loop electroporation in the cochlea. The model geometry was reconstructed from magnetic resonance images of the guinea pig cochlea and an eight-channel electrode array embedded within this geometry. The model's electrode voltage output waveform was obtained from electric potential mapping conducted in physiological solution following constant-current stimulation using the electrode array. Our simulations predict that a tandem electrode configuration with four ganged cathodes and four ganged anodes produces a larger area in target tissue where the electric field is within the range for successful gene transfer compared to an alternate paired anode-cathode electrode configuration. These findings corroborate published in vivo evidence comparing the two configurations and support the utility of the developed model as a tool to optimize the efficacy of electroporation electrodes.

关键词： Electric fields Computational modeling Electric potential Predictive models Cathodes Geometry

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Clinical trials of new drugs for Alzheimer disease: a 2020-2023 update

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Journal of biomedical science 2023年第1期30卷 83页

作者： Li-Kai Huang Yi-Chun Kuan Ho-Wei Lin Chaur-Jong Hu PhD Program in Medical Neuroscience College of Medical Science and Technology Taipei Medical University No. 291 Zhong Zheng Road Zhonghe District New Taipei City Taiwan. Taipei Neuroscience Institute Taipei Medical University New Taipei City Taiwan. Dementia Center and Department of Neurology Shuang-Ho Hospital Taipei Medical University New Taipei City Taiwan. Department of Neurology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan. Department of Biomedical Engineering National Taiwan University Taipei Taiwan. School of Medicine College of Medicine Taipei Medical University Taipei Taiwan. PhD Program in Medical Neuroscience College of Medical Science and Technology Taipei Medical University No. 291 Zhong Zheng Road Zhonghe District New Taipei City Taiwan. chaurjongh@tmu.edu.tw. Taipei Neuroscience Institute Taipei Medical University New Taipei City Taiwan. chaurjongh@tmu.edu.tw. Dementia Center and Department of Neurology Shuang-Ho Hospital Taipei Medical University New Taipei City Taiwan. chaurjongh@tmu.edu.tw. Department of Neurology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan. chaurjongh@tmu.edu.tw.

Alzheimer's disease (AD) is the leading cause of dementia, presenting a significant unmet medical need worldwide. The pathogenesis of AD involves various pathophysiological events, including the accumulation of amyloid and tau, neuro-inflammation, and neuronal injury. Clinical trials focusing on new drugs for AD were documented in 2020, but subsequent developments have emerged since then. Notably, the US-FDA has approved Aducanumab and Lecanemab, both antibodies targeting amyloid, marking the end of a nearly two-decade period without new AD drugs. In this comprehensive report, we review all trials listed in ***, elucidating their underlying mechanisms and study designs. Ongoing clinical trials are investigating numerous promising new drugs for AD. The main trends in these trials involve pathophysiology-based, disease-modifying therapies and the recruitment of participants in earlier stages of the disease. These trends underscore the significance of conducting fundamental research on pathophysiology, prevention, and intervention prior to the occurrence of brain damage caused by AD.

关键词： Alzheimer disease Anti-amyloid Anti-tau Clinical trials Cognitive enhancement Neuroinflammation Neuroprotection

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The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

arXiv

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arXiv 2023年

作者： Moawad, Ahmed W. Janas, Anastasia Baid, Ujjwal Ramakrishnan, Divya Saluja, Rachit Ashraf, Nader Jekel, Leon Amiruddin, Raisa Adewole, Maruf Albrecht, Jake Anazodo, Udunna Aneja, Sanjay Anwar, Syed Muhammad Bergquist, Timothy Calabrese, Evan Chiang, Veronica Chung, Verena Conte, Gian Marco Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Kazerooni, Anahita Fathi Kofler, Florian Krantchev, Kiril LaBella, Dominic Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Link, Katherine E. Liu, Xinyang Maleki, Nazanin Meier, Zeke Menze, Bjoern H. Moy, Harrison Osenberg, Klara Piraud, Marie Reitman, Zachary Shinohara, Russel Takeshi Tahon, Nourel hoda Nada, Ayman Velichko, Yuri S. Wang, Chunhao Wiestler, Benedikt Wiggins, Walter Shafique, Umber Willms, Klara Avesta, Arman Bousabarah, Khaled Chakrabarty, Satrajit Gennaro, Nicolo Holler, Wolfgang Kaur, Manpreet LaMontagne, Pamela Lin, MingDe Lost, Jan Marcus, Daniel S. Maresca, Ryan Merkaj, Sarah Nada, Ayaman Pedersen, Gabriel Cassinelli von Reppert, Marc Sotiras, Aristeidis Teytelboym, Oleg Tillmans, Niklas Westerhoff, Malte Youssef, Ayda Godfrey, Devon Floyd, Scott Rauschecker, Andreas Villanueva-Meyer, Javier Pflüger, Irada Cho, Jaeyoung Bendszus, Martin Brugnara, Gianluca Cramer, Justin Perez-Carillo, Gloria Guzman J. Johnson, Derek R. Kam, Anthony Kwan, Benjamin Yin Ming Lai, Lillian Lall, Neil U. Memon, Fatima Patro, Satya Narayana Petrovic, Bojan So, Tiffany Y. Thompson, Gerard Wu, Lei Schrickel, E. Brooke Bansal, Anu Barkhof, Frederik Besada, Cristina Chu, Sammy Druzgal, Jason Dusoi, Alexandru Farage, Luciano Feltrin, Fabricio Fong, Amy Fung, Steve H. Gray, R. Ian Ikuta, Ichiro Iv, Michael Postma, Alida A. Mahajan, Amit Joyner, David Krumpelman, Chase Letourneau-Guillon, Laurent Lincoln, Christie M. Maros, Mate E. Miller, Elka Morón, Fanny Nimchinsky, Esther A. Ozsarlak, Ozkan Patel, Uresh Rohatgi, Saurabh Saha, Atin Sayah, Anousheh Schwartz, Eric D. Shih, Robert Shiroishi, Mark Trinity health Mid Atlantic Hosiptals DarbyPA United States Department of Radiology and Biomedical Imaging Yale School of Medicine New HavenCT United States Division of Computational Pathology Department of Pathology and Laboratory Medicine School of Medicine Indiana University IndianapolisIN United States Department of Electical and Computer Engineering Cornell University Cornell Tech New YorkNY United States Department of Radiology Weill Cornell Medicine New YorkNY United States ImagineQuant Children’s Hospital of Philadelphia PhiladelphiaPA United States College of Medicine Alfaisal University Riyadh Saudi Arabia DKFZ Division of Translational Neurooncology The WTZ German Cancer Consortium DKTK Partner Site University Hospital Essen Essen Germany Medical Artificial Intelligence Lab Crestview Radiology Lagos Nigeria Sage Bionetworks SeattleWA United States Montreal Neurological Institute McGill University Montreal Canada lab Crestview Radiology Lagos Nigeria Department of Therapeutic Radiology Yale School of Medicine New HavenCT United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Department of Radiology Mayo Clinic RochesterMN United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Neurosurgery Yale School of Medicine New HavenCT United States Center for Global Health Perelman School of Medicine University of Pennsylvania PA United States Department of Informatics Technical University Munich Germany Children’s Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Children’s National Hospital WashingtonDC United States PrecisionFDA U.S. Food and Drug Administration S

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well-annotated, diverse d

关键词： Image segmentation

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Corrigendum to "Synergistic acceleration in the osteogenic and angiogenic differentiation of human mesenchymal stem cells by calcium silicate-graphene composites" [Mater. Sci. Eng. C 73 (2017) 726-735]

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Materials science & engineering. C, Materials for biological applications 2019年 98卷 1308-1309页

作者： Ming-You Shie Wei-Hung Chiang I-Wen Peter Chen Wen-Yi Liu Yi-Wen Chen 3D Printing Medical Research Center China Medical University Hospital Taichung City Taiwan School of Dentistry China Medical University Taichung City Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung City Taiwan. Department of Chemical Engineering National Taiwan University of Science and Technology Taipei Taiwan. Department of Applied Science National Taitung University Taitung City Taiwan. Department of Laboratory Science and Technology China Medical University Taichung City Taiwan. Graduate Institute of Biomedical Sciences China Medical University Taichung City Taiwan 3D Printing Medical Research Institute Asia University Taichung City Taiwan. Electronic address: evinchen@mail.cmu.edu.tw.

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Notice of Removal: Prediction of thermal coagulation by short-pulse pre-exposure for cavitation-enhanced ultrasonic heating

Notice of Removal: Prediction of thermal coagulation by shor...

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IEEE Symposium (IUS) Ultrasonics

作者： Ryosuke Iwasaki Ryo Takagi Shin Yoshizawa Shin-ichiro Umemura Graduate School of Biomedical Engineering Tohoku University Sendai Japan Graduate School of Engineering Tohoku University Sendai Japan

Removed.

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Conjugated Oligoelectrolytes: A Chain‐Elongated Oligophenylenevinylene Electrolyte Increases Microbial Membrane Stability (Adv. Mater. 18/2019)

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Advanced Materials 2019年第18期31卷

作者： Cheng Zhou Geraldine W. N. Chia James C. S. Ho Alex S. Moreland Thomas Seviour Bo Liedberg Atul N. Parikh Staffan Kjelleberg Jamie Hinks Guillermo C. Bazan School of Chemical and Biomedical Engineering Nanyang Technological University Singapore 639798 Singapore Singapore Centre on Environmental Life Sciences Engineering Nanyang Technological University Singapore 639798 Singapore Interdisciplinary Graduate School Nanyang Technological University Singapore 639798 Singapore Centre for Biomimetic Sensor Science School of Materials Science & Engineering Nanyang Technological University Singapore 639798 Singapore Center for Polymers and Organic Solids Departments of Chemistry & Biochemistry and Materials University of California Santa Barbara CA 93106 USA Department of Chemistry Chemical Engineering Biomedical Engineering and Materials Science & Engineering University of California Davis CA 95616 USA

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Residue detection in the large intestine from colonoscopy video using the support vector machine method

Residue detection in the large intestine from colonoscopy vi...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： M. Cho H. J. Kong J. H. Kim B. Jeon K. S. Hong S. Kim Interdisciplinary Program in Bioengineering Graduate School Seoul National University Seoul Republic of Korea Department of Biomedical Engineering Chungnam National University College of Medicine Daejeon Korea Department of Gastroenterology Seoul National University Boramae Medical Center Seoul Korea Department of Gastroenterology Mediplex Sejong Hospital Incheon Korea Institute of Medical and Biological Engineering Medical Research Center Seoul National University Seoul Republic of Korea

ISBN: (纸本)9781538670804;9788993215168

As colonoscopy is the gold standard screening test for colorectal cancer, studies of applying image processing to colonoscopy video are increasing. When performing colonoscopy or applying image processing to colonoscopy video, the presence of a residue in the large intestine is a negative affecting factor. In this reason, the colonoscopist evaluates and records the colon cleanliness after performing colonoscopy. However, these assessments can be influenced by the subjectivity of the colonoscopist. In order to quantify the frequency of residues in the large intestine, we applied image processing and machine learning techniques to the digital images obtained from colonoscopy. Support vector machine technique was applied to distinguish informative frames from colonoscopy videos, and to classify residue frames and the others. The sensitivity, specificity, and accuracy were evaluated for five patients.

关键词： Colonoscopy Support vector machines Intestines Image processing Endoscopes Cancer Colon

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Influence of shear stress on phenotype and MMP production of smooth muscle cells in a co-culture model

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Journal of Biorheology 2017年第2期31卷 50-56页

作者： Han, Xiaobo Sakamoto, Naoya Tomita, Noriko Meng, Hui Sato, Masaaki Ohta, Makoto Graduate School of Biomedical Engineering Tohoku University 2-1-1 Katahira Aoba-ku Sendai Japan Department of Medical Engineering Kawasaki University of Medical Welfare Japan Department of Intelligent Mechanical Systems Tokyo Metropolitan University Japan Institute of Fluid Science Tohoku University Japan Toshiba Stroke and Vascular Research Center Department of Mechanical and Aerospace Engineering University at Buffalo State University of New York United States

BACKGROUND: High shear stress (SS) could affect vascular remodeling during initiation of cerebral aneurysms. However, the response of smooth muscle cells (SMCs) to high SS, which is related to blood vessel remodeling, are poorly understood. OBJECTIVE: We explored the effect of high SS on SMCs in an EC-SMC coculture model to investigate blood vessel remodeling during aneurysm formation. METHODS: SMCs were threedimensionally cultured in a collagen gel, with ECs cultured on a membrane filter above the SMCs. Then, the co-culture model was exposed to different SS of 0.2, 2, 6, and 10 Pa for 72 h. RESULTS: A physiological SS of 2 Pa maintained the expression of contractile proteins in SMCs, whereas lower or higher SS decreased the expression, suggesting a phenotypic change to a synthetic state. Furthermore, we found that a high SS of 10 Pa increased production of both matrix metalloproteinase (MMP) -2 and -9 from SMCs, which could induce destructive blood vessel remolding. CONCLUSIONS: High SS could change the SMC phenotype to a synthetic state and induce higher MMP production by SMCs in the co-culture model. These results may help to further elucidate blood vessel remodeling mechanisms under high SS conditions. © Japanese Society of Biorheology 2017.

关键词： Shear stress

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Measurement of Respiratory System Function By Airwave Oscillometry (AOS) in a Cohort of Children Undergoing Hematopoietic Stem Cell Transplant (HSCT)

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Transplantation and Cellular Therapy 2024年第2期30卷 S129-S130页

作者： Josephson, Maureen B., MD McDonough, Joseph M., MS Young, Lisa R., MD Freedman, Jason L., MD, MSCE Towe, Christopher, MD Cook, Eleanor, MBBS Gaffin, Jonathan M., MD Koo, Dr. Jane, MD MacMillan, Margaret L., MD, MSc, FRCPC Myers, Kasiani C, MD Lehmann, Leslie E., MD Walkup, Laura L., PhD Wong, Wai, MD Woods, Jason C., PhD Davies, Stella M., MBBS, PhD, MRCP Goldfarb, Samuel B., MD Allen, Julian L., MD Division of Pulmonary and Sleep Medicine Children's Hospital of Philadelphia Philadelphia PA Department of Pediatrics University of Pennsylvania Perelman School of Medicine Philadelphia PA Department of Pediatrics University of Pennsylvania Perelman School of Medicine Pennsylvania PA Cellular Therapy and Transplant Section Division of Oncology Children's Hospital of Philadelphia Philadelphia PA Department of Pediatrics Perelman School of Medicine of the University of Pennsylvania Philadelphia PA Division of Pulmonary Medicine Cincinnati Children's Hospital Medical Center Cincinnati OH Department of Pediatrics University of Cincinnati College of Medicine Cincinnati OH Department of Pediatrics University of Cincinnati Cincinnati OH Division of Bone Marrow Transplantation and Immune Deficiency Cincinnati Children's Hospital Medical Center Cincinnati OH Department of Pediatrics Harvard Medical School Boston MA Pediatrics Blood and Marrow Transplantation & Cellular Therapy Program University of Minnesota Minneapolis MN Center for Stem Cell Transplantation Division of Hematologic Malignancy Department of Medical Oncology Dana-Farber Cancer Institute Harvard Medical School Boston MA Biomedical Engineering University of Cincinnati Cincinnati OH Division of Pediatric Pulmonary and Sleep Medicine University of Minnesota Minneapolis MN Children's Hospital of Philadelphia Pennsylvania PA

IntroductionRespiratory complications are common following HSCT in children. The most sensitive tests to evaluate these complications are unknown. Airwave oscillometry has been proposed to be a sensitive test of disorders of the mechanics of the respiratory system, including large and small airway dysfunction and elevated respiratory system stiffness due to pulmonary parenchymal or chest wall damage. We performed oscillometry (Tremofloã C100, Thorasys, Montreal, CA) at baseline and following HSCT in a cohort of 56 children at 3 of 4 centers to date: Cincinnati Children's Hospital, Children's Hospital of Philadelphia, University of Minnesota Children's Hospital, and Boston Children's Hospital. MethodsChildren between the ages of 4 and 21 years were eligible for study. Following ERS 2020 Guidelines, the following parameters were measured between 5 and 37 hz: R5 (measure of total resistance), R5-R19 (measure of peripheral airway obstruction), X5 (measure of respiratory system stiffness), and AX (measure of stiffness and small airway function). Results were compared to 1. predicted values of Ducharme (2019) and 2. a cohort of 80 CHOP control subjects. A result was considered abnormal if it deviated from the predicted Ducharme mean by > 1.64 standard deviations. ResultsAOS was well tolerated, and technically interpretable in most children. Of 92 separate studies in the first 56 children studied, the overall number (percentage) of abnormal results when compared to Ducharme normal values were as follows: R5: 5(5%); R5-R19: 1 (1%); X5: 7(8%); AX: 12 (13%). When AX data from studies pre-transplant (Figure 1) were compared to studies post-transplant (Figure 2), there were no clear changes in the proportion of abnormal studies by Y2 test. However the power of this analysis may not have been sufficient in this early phase of the study. The proportion of abnormal reactance tests (X5 and AX) (10%) was significantly greater than the proportion of abnormal resistance tests (R5 and

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