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6th International Conference on Biomedical and Health Informatics, ICBHI 2024

作者： Chen, Yun-Chien Jiang, Ching-Fen Graduate Degree Program of Smart Healthcare and Bioinformatics I-Shou University Kaohsiung City Taiwan

ISBN: (纸本)9783031863226

Respiratory rate is an important vital sign that reflects health status. In hospital, traditional methods of measuring respiratory rate include manual counting and using a respiratory belt, both of which may pose a risk of contact infection. This study conducted clinical trial in medical ward of hospital, presented a non-contact method based on a 24 GHz continuous-wave radar to detect the displacement of the chest wall during breathing. By processing the radar signal, we were able to obtain the respiratory rate. However, during clinical validation, uncertainty in the daily measurement time of vital sign, which serve as the gold standard, can lead to comparison error. To overcome the interference caused by the issue, this study proposed a validation method that calculates the average of radar-detected respiratory rate over specific time interval and uses the standard deviation of these time interval as an indicator of the stability of the radar-detected respiratory rate value. Statistical results show that the radar-detected respiratory rate derived in this study is consistent with the hospital-measured respiratory rate (p = 0.17) and exhibits significant differences across different subjects (p © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Noninvasive medical procedures

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A virtual positioning system for external beam radiotherapy

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Virtual Reality 2023年第3期27卷 2569-2582页

作者： Sung, Wen-Hsu Jiang, Ching-Fen Su, Tai-Sin Sun, Shuh-Ping Department of Physical Therapy and Assistive Technology National Yang Ming Chiao Tung University Taipei Taiwan Graduate Program of Smart Healthcare and Bioinformatics I-Shou University Kaohsiung Taiwan Department of Biomedical Engineering I-Shou University Kaohsiung Taiwan Department of Digital Media Design I-Shou University Kaohsiung Taiwan

In radiation therapy, positioning patients to ensure the accuracy of the setup of a procedure is a routine and labour-intensive process that substantially determines the efficacy of treatment. In this study, we propose a virtual positioning system that can simulate the positioning process with a visible beam path under the broad view of a life-like patient-positioning platform to obviate problems with excessively narrow view. This system integrates image processing, computer graphics, and virtual reality to encompass a 3D treatment target reconstructed from medical images of different modalities in a virtual scene. An innovative evaluation method is further proposed to verify the efficacy of the system using a full-scale 3D solid anthropometric model, and a treatment target showed an accuracy of 99% in calibration and a mean compatibility of 95% with an actual measurement. The proposed virtual positioning system provides a training and education platform for radiation therapy. The methodology through which the system was developed is also disclosed as a promising approach to improve the efficiency and safety of the position verification process for existing systems. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

关键词： Radiotherapy

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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer

Predicting Microsatellite Instability from Histology Images ...

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IEEE Symposium on bioinformatics and Bioengineering (BIBE)

作者： Yun-Chi Chen Tung Chao Wimaya Nitya Phandita Tzu-Yu Sun Huai-Zhi Wang Yi-Hsien Hsieh Le-Yin Hsu Che Lin Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University (NTU) Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan Data Science Degree Program NTU Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients.

关键词： Attention mechanisms Histopathology Computational modeling Precision medicine Small satellites Immunotherapy Colorectal cancer Predictive models Transformers Genetics

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Using ensemble learning and hierarchical strategy to predict the outcomes of ESWL for upper ureteral stone treatment

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Computers in Biology and Medicine 2024年 179卷 108904-108904页

作者： Chen, Chi-Wei Liu, Wayne-Young Huang, Lan-Ying Chu, Yen-Wei Graduate Degree Program of Smart Healthcare & Bioinformatics I-Shou University Kaohsiung City Taiwan Department of Biomedical Engineering I-Shou University Kaohsiung City Taiwan Doctoral Program in Medical Biotechnology National Chung Hsing University Taichung City Taiwan Department of Urology Jen-Ai Hospital Taichung City Taiwan Graduate Institute of Genomics and Bioinformatics National Chung Hsing University Taichung City Taiwan Institute of Molecular Biology National Chung Hsing University Taichung City Taiwan Agricultural Biotechnology Center National Chung Hsing University Taichung City Taiwan Rong Hsing Research Center for Translational Medicine Taichung City Taiwan Ph. D Program in Medical Biotechnology National Chung Hsing University Taichung City Taiwan Taichung402 Taiwan

Urinary tract stones are a common and frequently recurring medical issue. Accurately predicting the success rate after surgery can help avoid ineffective medical procedures and reduce unnecessary healthcare costs. This study collected data from patients with upper ureter stones who underwent extracorporeal shock wave lithotripsy, including cases of successful as well as unsuccessful stone removal after the first and second lithotripsy procedures, and constructed prediction systems for the outcomes of the first and second lithotripsy procedures. Features were extracted from three categories of information: patient characteristics, stone characteristics, and extracorporeal shock wave lithotripsy machine data, and additional features were created using Feature Creation. Finally, the impact of features on the models was analyzed using six methods to calculate feature importance. Our prediction model for the first lithotripsy, selected from among 43 methods and seven ensemble learning techniques, achieves an AUC of 0.91. For the second lithotripsy, the AUC reaches 0.76. The results indicate that the detailed and binary information provided by patients regarding their history of stone experiences contributes differently to the predictive accuracy of the first and second lithotripsy procedures. The prediction tool is available at https://***/ks. © 2024

关键词： Machine learning

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Comparing the prediction performance of machine learning and deep learning methods for 3c-like protease cleavage sites 23

Comparing the prediction performance of machine learning and...

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7th International Conference on Medical and Health Informatics, ICMHI 2023

作者： Huang, Lan-Ying Chen, Chi-Wei Chang, Kai-Po Chu, Yen-Wei Medical Biotechnology National Chung Hsing University Taichung402 Taiwan Department of Pathology School of Medicine China Medical University Taichung402 Taiwan Department of Pathology China Medical University Hospital Taichung402 Taiwan Graduate Degree Program of Smart Healthcare and Bioinformatics I-Shou University Kaohsiung82445 Taiwan Department of Biomedical Engineering I-Shou University Kaohsiung82445 Taiwan Institute of Genomics and Bioinformatics National Chung Hsing University Taichung402 Taiwan Institute of Molecular Biology National Chung Hsing University Taichung402 Taiwan Agricultural Biotechnology Center National Chung Hsing University Taichung402 Taiwan Biotechnology Center National Chung Hsing University Taichung402 Taiwan Translational Medicine National Chung Hsing University Taichung402 Taiwan Rong Hsing Research Center for Translational Medicine National Chung Hsing University Taichung402 Taiwan Taichung402 Taiwan

ISBN: (纸本)9798400700712

The impact of COVID-19 on the human body after infection requires further investigation. The protease carried by the virus may interact with cellular proteins, thereby affecting the regulatory pathways. Machine learning can provide predictive information to aid the research design in such studies. This study examined the effectiveness of different encoding methods for training models using traditional machine learning and deep learning methods. Using the advantages of deep learning methods for high-dimensional data analysis, amino acid properties were extracted and classified. Viral polyprotein data were used as the training set and human protein substrates were collected from the literature as the independent test set. In the one-dimensional convolutional neural network model, the accuracy of the independent test set was the highest, at 0.832. © 2023 ACM.

关键词： Coronavirus

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Transdermal nanolipoplex simultaneously inhibits subcutaneous melanoma growth and suppresses systemically metastatic melanoma by activating host immunity

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Nanomedicine: Nanotechnology, Biology, and Medicine 2023年 47卷 102628-102628页

作者： Chen, Chia-Hung Weng, Tzu-Han Chuang, Cheng-Hsun Huang, Kai-Yao Huang, Sih-Cheng Chen, Pin-Rong Huang, Hsiao-Hsuan Huang, Ling-Ya Shen, Pei-Chun Chuang, Po-Ya Huang, Hsiao-Yen Wu, Yi-Syuan Chang, Hao-Chiun Weng, Shun-Long Liao, Kuang-Wen Department of Medical Research Hsinchu MacKay Memorial Hospital Hsinchu City30071 Taiwan Dependent of Medical Education MacKay Memorial Hospital Taipei10449 Taiwan Institute of Molecular Medicine and Bioengineering National Yang Ming Chiao Tung University Hsinchu City30068 Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu City30068 Taiwan Industrial Development Graduate Program of College of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu City30068 Taiwan Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu City30068 Taiwan Department of Medicine MacKay Medical College New Taipei City25245 Taiwan Department of Obstetrics and Gynecology Hsinchu MacKay Memorial Hospital Hsinchu City30071 Taiwan MacKay Junior College of Medicine Nursing and Management Taipei City11260 Taiwan Drug Development and Value Creation Research Center College of Dental Medicine Kaohsiung Medical University School of Dentistry Graduate Institute of Medicine College of Medicine Center for Cancer Research Kaohsiung Medical University Kaohsiung City80708 Taiwan Center for Intelligent Drug Systems and Smart Bio-devices National Yang Ming Chiao Tung University Hsinchu City30068 Taiwan Department of Biotechnology and Bioindustry Sciences National Cheng Kung University Tainan City70101 Taiwan Ph.D. Degree Program of Biomedical Science and Engineering National Yang Ming Chiao Tung University Hsinchu City30068 Taiwan

Benefit for clinical melanoma treatments, the transdermal neoadjuvant therapy could reduce surgery region and increase immunotherapy efficacy. Using lipoplex (Lipo-PEG-PEI-complex, LPPC) encapsulated doxorubicin (DOX) and carrying CpG oligodeoxynucleotide;the transdermally administered nano-liposomal drug complex (LPPC-DOX-CpG) would have high cytotoxicity and immunostimulatory activity to suppress systemic metastasis of melanoma. LPPC-DOX-CpG dramatically suppressed subcutaneous melanoma growth by inducing tumor cell apoptosis and recruiting immune cells into the tumor area. Animal studies further showed that the colonization and growth of spontaneously metastatic melanoma cells in the liver and lung were suppressed by transdermal LPPC-DOX-CpG. Furthermore, NGS analysis revealed IFN-γ and NF-κB pathways were triggered to recruit and activate the antigen-presenting-cells and effecter cells, which could activate the anti-tumor responses as the major mechanism responsible for the therapeutic effect of LPPC-DOX-CpG. Finally, we have successfully proved transdermal LPPC-DOX-CpG as a promising penetrative carrier to activate systemic anti-tumor immunity against subcutaneous and metastatic tumor. © 2022

关键词： Tumors

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