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2024 7th International Conference on Healthcare Service Management, ICHSM 2024

作者： Nguyen, Duc-Khanh Li, Ai-Hsien Adam Lai, Yen-Jun Chiu, Yen-Ling Phan, Dinh-Van Chien, Ting-Ying Chan, Chien-Lung Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Statistics and Informatics University of Economics The University of Danang Danang Viet Nam Division of Cardiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan ZDT Group- YZU Joint Research and Development Center for Big Data Taoyuan Taiwan Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400710162

This study explores the feasibility of deep learning for classifying nodule neoplasms, analyzing their performance on two openly available datasets, LUNGx SPIE, and LIDC-IDRI. These datasets offer valuable diversity in image quantity, quality, and malignancy annotation methodologies, posing a real-world challenge for effective model development. To handle the disparities in these diverse datasets, we aim to develop a robust classification model trained using federated learning. This decentralized approach allows model training across datasets without sharing patient data, preserving privacy and overcoming data fragmentation. Our experiments demonstrate that federated learning enables the development of a robust and superior deep learning model compared to individual models trained on limited data. This combination of deep learning, and federated learning holds immense promise for nodule neoplasm classification and can significantly improve patient care and outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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ATP_mCNN: Predicting ATP binding sites through pretrained language models and multi-window neural networks

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Computers in Biology and Medicine 2025年 185卷 109541-109541页

作者： Le, Van-The Malik, Muhammad-Shahid Lin, Yi-Jing Liu, Yu-Chen Chang, Yan-Yun Ou, Yu-Yen Department of Computer Science and Engineering Yuan Ze University Chung-Li32003 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Chung-Li32003 Taiwan Department of Computer Sciences Karakoram International University Gilgit-Baltistan 15100 Pakistan

Adenosine triphosphate plays a vital role in providing energy and enabling key cellular processes through interactions with binding proteins. The increasing amount of protein sequence data necessitates computational methods for identifying binding sites. However, experimental identification of adenosine triphosphate-binding residues remains challenging. To address the challenge, we developed a multi-window convolutional neural network architecture taking pre-trained protein language model embeddings as input features. In particular, multiple parallel convolutional layers scan for motifs localized to different window sizes. Max pooling extracts salient features concatenated across windows into a final multi-scale representation for residue-level classification. On benchmark datasets, our model achieves an area under the ROC curve of 0.95, significantly improving on prior sequence-based models and outperforming convolutional neural network baselines. This demonstrates the utility of pre-trained language models and multi-window convolutional neural networks for advanced sequence-based prediction of adenosine triphosphate-binding residues. Our approach provides a promising new direction for elucidating binding mechanisms and interactions from primary structure. © 2024 Elsevier Ltd

关键词： Convolutional neural networks

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The effect of stereochemical constraints on the radius of gyration of folded proteins

arXiv

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arXiv 2025年

作者： Logan, Jack A. Sumner, Jacob Grigas, Alex T. Shattuck, Mark D. O'Hern, Corey S. Department of Mechanical Engineering Yale University New HavenCT06520 United States Graduate Program in Computational Biology & Biomedical Informatics Yale University New HavenCT06520 United States Integrated Graduate Program in Physical and Engineering Biology Yale University New HavenCT06520 United States Benjamin Levich Institute and Physics Department The City College of New York New YorkNY10031 United States Department of Applied Physics Yale University New HavenCT06520 United States Department of Physics Yale University New HavenCT06520 United States

Proteins are composed of linear chains of amino acids that fold into complex three-dimensional structures. A general feature of folded proteins is that they are compact with a radius of gyration Rg(N) ∼ Nν that obeys power-law scaling with the number of amino acids N in each protein and ν ∼ 1/3. In this study, we investigate the internal scaling of the radius of gyration Rg(n) versus the chemical separation n between amino acids for all subchains of length n. We show that for globular proteins Rg(n) ∼ nν1,2 with a larger exponent ν1 > 1/3 for small n and a smaller exponent ν2 g(N) ∝ Nν. To describe this scaling behavior for Rg(n), we carry out folding simulations for a series of coarse-grained models for proteins beginning with the freely-jointed and freely-rotating chain models composed of spherical monomers and varying degrees of stereochemical constraints. We show that a minimal model, which coarse-grains amino acids into a single spherical backbone atom and one variable-sized spherical side-chain atom, and enforces bend- and dihedral-angle constraints, can recapitulate Rg(n) for x-ray crystal structures of globular proteins. In addition, this model predicts the correct average packing fraction and size of the hydrophobic core, which are two key physical features that can be used to distinguish between computational 'decoys' and correctly folded proteins in protein design applications. © 2025, CC BY.

关键词： Spheres

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Genomic data processing with GenomeFlow

arXiv

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arXiv 2025年

作者： Park, Junseok Maury, Eduardo A. Oh, Changhoon Shin, Donghoon Denisko, Danielle Lee, Eunjung Alice Division of Genetics and Genomics Boston Children’s Hospital 3 Blackfan Circle BostonMA United States Department of Pediatrics Harvard Medical School 25 Shattuck St BostonMA United States Bioinformatics Integrative Genomics Program Harvard/MIT MD-PHD Program Harvard Medical School BostonMA United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard CambridgeMA United States Graduate School of Information Yonsei University 50 Yonsei-ro Seodaemun-gu Seoul Korea Republic of Department of Human Centered Design and Engineering University of Washington SeattleWA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States

Advances in genome sequencing technologies generate massive amounts of sequence data that are increasingly analyzed and shared through public repositories. On-demand infrastructure services on cloud computing platforms enable the processing of such large-scale genomic sequence data in distributed processing environments with a significant reduction in analysis time. However, parallel processing on cloud computing platforms presents many challenges to researchers, even skillful bioinformaticians. In particular, it is difficult to design a computing architecture optimized to reduce the cost of computing and disk storage as genomic data analysis pipelines often employ many heterogeneous tools with different resource requirements. To address these issues, we developed GenomeFlow, a tool for automated development of computing architecture and resource optimization on Google Cloud Platform, which allows users to process a large number of samples at minimal cost. We outline multiple use cases of GenomeFlow demonstrating its utility to significantly reduce computing time and cost associated with analyzing genomic and transcriptomic data from hundreds to tens of thousands of samples from several consortia. Here, we describe a step-by-step protocol on how to use GenomeFlow for a common genomic data processing task. We introduce this example protocol geared toward a bioinformatician with little experience in cloud computing. © 2025, CC BY.

关键词： Cloud platforms

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Applying an Agile Science Roadmap to Integrate and Evaluate Ethical Frameworks Throughout the Lifecycle and Use of Artificial Intelligence Tools in the Intensive Care Unit

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Critical Care Nursing Clinics of North America 2025年第2期37卷 347-363页

作者： Lindroth, Heidi Sahajwani, Juhi Hudson, Mark Heier, Laura Gonzalez, Andrew A. Bhattacharyya, Anirban Zheng, Zhi Boustani, Malaz Herasevich, Vitaly McGowan, Michelle Barry, Barbara Division of Nursing Research Department of Nursing Mayo Clinic Rochester MN United States Center for Aging Research Regenstrief Institute School of Medicine Indiana University Indianapolis IN United States Center for Health Innovation and Implementation Science School of Medicine Indiana University Indianapolis IN United States Department of Critical Care Medicine Mayo Clinic Jacksonville FL United States School of Psychology and Counselling The Open University Milton Keynes United Kingdom Department of Graduate Nursing Viterbo University La Crosse WI United States Department of Neurosurgery Mayo Clinic Rochester MN United States Center for Health Services Research Regenstrief Institute School of Medicine Indiana University Indianapolis IN United States Surgical Outcomes and Quality Improvement Center Department of Surgery School of Medicine Indiana University Indianapolis IN United States Department of Electrical Engineering University of Notre Dame Notre Dame IN United States Department of Anesthesiology and Perioperative Medicine Mayo Clinic Rochester MN United States Biomedical Ethics Departments of Quantitative Health Sciences and Artificial Intelligence and Informatics Biomedical Ethics Research Program Mayo Clinic Rochester MN United States Kern Center Mayo Clinic Rochester MN United States

This article summarizes existing ethical frameworks for healthcare artificial intelligence (AI) and ambient sensing technology, such as computer vision, and examines their application to improve patient outcomes in the intensive care unit (ICU). Integrating ethical considerations such as privacy, fairness, and autonomy into the lifecycle of an AI tool is necessary to fully harvest AI's potential to deliver safe, high-quality, personalized, and low-cost healthcare services that provide positive experiences for patients, families, and clinicians while improving health outcomes. Drawing from agile science, the article proposes a practical roadmap for clinician and researcher use to identify, integrate, and monitor ethical considerations throughout the lifecycle of an AI tool with the intention to improve patient care. A use case illustrates the application of the agile science-informed roadmap that details the development of a passive digital marker for delirium severity.

关键词： Agile science AI tools Artificial intelligence Computer vision Delirium severity Ethical frameworks for health care AI ICU Passive digital marker

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Early-Life Risks of Central Precocious Puberty

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Endocrine Practice 2025年

作者： Yin, Wei-Jou Hsu, Jhih-Wei Chen, Chun-Chang Su, Emily Chia-Yu Wang, San-Yuan Chen, Yan-Jen Chen, Yang-Ching College of Medicine Taipei Medical University Taipei Taiwan Department of Family Medicine School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan Master Program in Clinical Genomics and Proteomics Department of Pharmaceutical Sciences Taipei Medical University Taipei Taiwan Department of Family Medicine Wan Fang Hospital Taipei Medical University Taipei Taiwan Nutrition Research Center Taipei Medical University Hospital Taipei Taiwan

Objectives: To investigate the factors in early life that may contribute to central precocious puberty (CPP). Methods: The study utilized data from the Taiwan Puberty Longitudinal Study, including 2241 children under pubertal assessment and a questionnaire of risk factors. We analyzed associations using the Fitting Generalized Linear Models in R (R Core Team, 2023), with R studio (Posit, 2023) version 4.3.1. Results: Among the 2241 children examined, 745 had CPP. Overall, higher gestational weight gain (GWG) increased the risk of CPP (odds ratio [OR]: 1.03, 95% CI: 1.01-1.05);while higher GWG served as a protective factor in females (OR: 0.97, 95% CI: 0.95-0.99). Maternal gestational diabetes mellitus (GDM) also increased the CPP risk, particularly in males (OR: 2.66, 95% CI: 1.00-7.25). Longer exclusive breastfeeding was linked to lower CPP risk overall (OR: 0.96, 95% CI: 0.93-1.00) but was not significant when analyzed by gender. Conclusions: Higher maternal GWG was associated with an increased risk of CPP overall, while GWG became negatively associated with CPP, only in females. Maternal GDM was linked to a higher risk of CPP, particularly in males. The duration of exclusive breastfeeding was inversely correlated with CPP risk, but this effect was not significant when analyzed by gender. © 2025 AACE

关键词： central precocious puberty early-life risk factors pediatrics

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Analysis of combined ultrasound therapy and medium frequency current in abdominal adiposity

Analysis of combined ultrasound therapy and medium frequency...

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13th Mediterranean Conference on Medical and Biological Engineering and Computing 2013, MEDICON 2013

作者： Gripp, Paula Alessandra Garcia Stadnik, A.M.W. Bona, A.A. Neves, E.B. UTFPR/Graduate Program in Biomedical Engineering Curitiba Brazil UTFPR/Department of Electrical Engineering and Industrial Informatics Curitiba Brazil

ISBN: (纸本)9783319008455

Brazil is the second country in the world in aesthetic surgical procedures, with 905,000 procedures, being behind only the U.S., according to information corresponding to the year 2011 from the International Society of Aesthetic Plastic Surgery (ISAPS) and the Brazilian Society of Plastic Surgery (BSPS). Given this scenario, we chose to study the adipose cells, which are a organ with intense metabolic and endocrine activity. Accumulated in excess adipocytes causes the disadvantages metabolic and endocrine in the organism, leading to obesity in general and located. This research referred to obesity marked by deposition in the abdominal area, that is denominated central or androide obesity, in the case of a localized adiposity. The objective of the research was to analyze the effects of therapeutic ultrasound and medium frequency current in this particular type of adiposity. The therapeutic protocol consisted of the utilization therapy of therapeutic ultrasound 3 MHz and medium frequency current (1 kHz, with Bursts of 2 ms), performed in ten sessions of 40-minute each. The sample consisted of 16 women, of whom 14 have finalized treatment. The assessment instruments used were: percentage of total body fat by bioimpedance, skinfold measurement in the region where localized adiposity was. We observed the reduction in measures measured in abdominal circumference and percentage decrease in total body fat measured on a digital scale bipolar bioimpedance, as well as the decrease measured by skinfold plicometer in all patients submitted to treatment. The results of submission to the treatment of Combined Therapy can be considered satisfactory, due the record in reducing the parameters analyzed, despite being necessary to a more thorough study on the subject and scientific substantiation in a larger sample. Based on this study has observed a dearth of studies on the subject so that the same password be a non-invasive alternative in the treatment of localized adiposity and derm

关键词： Ultrasonics

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Tuning the Size and Stiffness of Inflatable Particles

arXiv

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arXiv 2025年

作者： Pashine, Nidhi Wang, Dong Baines, Robert Goyal, Medha Shattuck, Mark D. O’Hern, Corey S. Kramer-Bottiglio, Rebecca Department of Mechanical Engineering Yale University New HavenCT06520 United States Department of Physics BioInspired Institute Syracuse University SyracuseNY13244 United States Department of Physics Benjamin Levich Institute City College of New York New York CityNY10031 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States Graduate Program in Computational Biology & Biomedical Informatics Yale University New HavenCT06520 United States

We describe size-varying cylindrical particles made from silicone elastomers that can serve as building blocks for robotic granular materials. The particle size variation, which is achieved by inflation, gives rise to changes in stiffness under compression. We design and fabricate inflatable particles that can become stiffer or softer during inflation, depending on key parameters of the particle geometry, such as the ratio of the fillet radius to the wall thickness, r/t. We also conduct numerical simulations of the inflatable particles and show that they only soften during inflation when localization of large strains occurs in the regime r/t → 0. This work introduces novel particle systems with tunable size and stiffness that can be implemented in numerous soft robotic applications. © 2025, CC BY.

关键词： Stiffness

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Comparison of Fetal Heart Rate Detection Using Envelope Extraction and Shannon Energy

Comparison of Fetal Heart Rate Detection Using Envelope Extr...

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2019 International Seminar on Application for Technology of Information and Communication, iSemantic 2019

作者： Setiawan, Agung W. Sujatmiko, Wahyu Biomedical Engineering Research Group School of Electrical Engineering and Informatics ITB Bandung Indonesia Graduate Program in Electrical Engineering School of Electrical Engineering and Informatics ITB Bandung Indonesia

ISBN: (纸本)9781728138329

Heart rate monitoring is widely used as a general procedure to examine fetal health condition. Fetal heart rate can be used to identified possibilities of fetal anomaly and warn the obstetrician to provide immediate intervention before any further abnormalities. The purpose of this research is to compare fetal heart rate detection result using envelope extraction and Shannon energy envelope method. There are 115 fetal phonocardiography and maternal phonocardiography that is used in this research. The dataset was obtained from Hafez Hospital, Shiraz University of Medical Science. Envelope extraction method can be used to detect fetal heart rate signal with 89.2% accuracy for all fetal phonocardiography signal while Shannon energy envelope method can be used to detect fetal heart rate signal with 88.3% accuracy for all fetal phonocardiography signal. © 2019 IEEE.

关键词： Phonocardiography

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Coevolution of Myoelectric Hand Control under the Tactile Interaction among Fingers and Objects

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Cyborg and Bionic Systems 2022年第1期2022卷 285-302页

作者： Yuki Kuroda Yusuke Yamanoi Shunta Togo Yinlai Jiang Hiroshi Yokoi Joint Doctoral Program for Sustainability Research Graduate School of Informatics and Engineering The University of Electro-CommunicationsTokyoJapan Department of Mechan ical and Intelligent System Engineering Graduate School of Informatics and Engineering The University of Electro Communications TokyoJapan Center for Neuroscience and Biomedical Engineering The University of Electro Communications TokyoJapan Bejing Innovation Center for Inelligent Robots and Systems BeijingChina

The usability of a prosthetic hand difers significantly from that of a real ***,the complexity of manipulation increases as the number of degrees of freedom to be controlled increases,making manipulation with biological signals extremely *** overcome this problem,users need to select a grasping posture that is adaptive to the object and a stable grasping method that prevents the object from *** previous studies,these have been left to the operating skills of the user,which is extremely difficult to *** this study,we demonstrate how stable and adaptive grasping can be achieved according to the object regardless of the user's operation *** required grasping technique is achieved by determining the correlation bet ween the motor output and each sensor through the interaction between the prosthetic hand and the surrounding stimuli,such as myoelectricity,sense of touch,and grasping *** agents of the 16 DOF robot hand were trained with the myoelectric signals of six participants,including one child with a congenital forearm ***,each agent could open and close the hand in response to the myoeletric stimuli and could accomplish the object pickup *** the tasks,the agents successfully identified grasping patterns suitable for practical and stable positioning of the *** addition,the agents were able to pick up the object in a similar posture regardless of the partiipant,suggesting that the hand was optimized by evolutionary computation to a posture that prevents the object from being dropped.

关键词： robot Objects overcome

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