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3D-printed porous titanium suture anchor: a rabbit lateral femoral condyle model

BMC Musculoskeletal Disorders 2024年第1期25卷 1页

作者： Wu, Lien-Chen Hsieh, Yueh-Ying Hsu, Ting-Shuo Liu, Po-Yi Tsuang, Fon-Yih Kuo, Yi-Jie Chen, Chia-Hsien Van Huynh, T.I.N. Chiang, Chang-Jung Department of Orthopaedics Shuang Ho Hospital Taipei Medical University Department of Orthopaedics School of Medicine College of Medicine Taipei Medical University Graduate Institute of Biomedical Materials and Tissue Engineering College of Biomedical Engineering Taipei Medical University Division of Neurosurgery Department of Surgery National Taiwan University Hospital Spine Tumor Center National Taiwan University Hospital Department of Orthopedic Surgery Wan Fang Hospital Taipei Medical University School of Biomedical Engineering College of Biomedical Engineering Taipei Medical University Department of Interventional Cardiology Thong Nhat Hospital International Ph.D program in medicine college of medicine Taipei medical university

Background: The inclusion of a connecting path in a porous implant can promote nutrient diffusion to cells and enhance bone ingrowth. Consequently, this study aimed to evaluate the biomechanical, radiographic, and histopathological performance of a novel 3D-printed porous suture anchor in a rabbit femur model. Methods: Three test groups were formed based on the type of suture anchor (SA): Commercial SA (CSA, Group A, n = 20), custom solid SA (CSSA, Group B, n = 20), and custom porous SA (CPSA, Group C, n = 20). The SAs were implanted in the lateral femoral condyle of the right leg in each rabbit. The rabbits (New Zealand white rabbits, male, mean body weight of 2.8 ± 0.5 kg, age 8 months) underwent identical treatment and were randomized into experimental and control groups via computer-generated randomization. Five rabbits (10 femoral condyles) were euthanized at 0, 4, 8, and 12 weeks post-implantation for micro-CT, histological analysis, and biomechanical testing. Results: At 12 weeks, the CPSA showed a higher BV/TV (median 0.7301, IQR 0.7276–0.7315) than the CSSA and CSA. The histological analysis showed mineralized osteocytes near the SA. At 4 weeks, new bone was observed around the CPSA and had penetrated its porous structure. By 12 weeks, there was no significant difference in ultimate failure load between the CSA and CPSA. Conclusions: We demonstrated that the innovative 3D-printed porous suture anchor exhibited comparable pullout strength to conventional threaded suture anchors at the 12-week postoperative time-point period. Furthermore, our porous anchor design enhanced new bone formation and facilitated bone growth into the implant structure, resulting in improved biomechanical stability. © The Author(s) 2024.

关键词： 3D-printed porous anchor Bone formation Bone ingrowth Suture anchor

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Heterojunction and co-catalyst engineering of bismuth vanadate by nickel molybdenum oxide and carbon quantum dot decorations for photoelectrochemical water splitting

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International Journal of Hydrogen Energy 2024年 51卷 1099-1108页

作者： Kuo, Tsung-Rong Chiou, Yu-Tai Huang, Hsiao-Wen Kongvarhodom, Chutima Saukani, Muhammad Yougbaré, Sibidou Chen, Hung-Ming Lin, Lu-Yin Graduate Institute of Nanomedicine and Medical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan International PhD Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Department of Chemical Engineering and Biotechnology National Taipei University of Technology Taipei Taiwan Department of Chemical Engineering King Mongkut's University of Technology Thonburi 126 Pracha-u-thit Toong-kru Bangkok10140 Thailand Department of Mechanical Engineering Faculty of Engineering Universitas Islam Kalimantan MAB Jl. Adhyaksa No. 2 Banjarmasin70124 Indonesia /Nanoro 03 B.P 7192 Ouagadougou03 Burkina Faso Gingen technology Co. LTD. Rm. 7 10F. No.189 Sec. 2 Keelung Rd. Xinyi Dist. Taipei11054 Taiwan

Improving the charge diffusion length and water oxidation kinetics of bismuth vanadate (BiVO4) is essential to achieve better photoelectrochemical catalytic ability for water oxidation. Establishing heterojunctions and incorporating co-catalysts with BiVO4 create facile charge transfer paths and generate more water oxidation reactions. In this work, nickel molybdenum oxide (NM) and carbon quantum dot (CQD) are firstly deposited on BiVO4 by hydrothermal and soaking processes to design an efficient co-catalyst/photocatalyst system for water oxidation. The depositing amount of CQD is optimized to balance contributions of CQD and NM as the hole sink and the light-to-electron generator, respectively. The highest photocurrent densities of 2.2 and 3.9 mA/cm2 are achieved for BiVO4/NM/CQD in the electrolyte without and with hole scavenger, respectively. The BiVO4 electrode only shows a photocurrent density of 1.2 mA/cm2 in the electrolyte without hole scavenger. The best photoelectrochemical catalytic ability of BiVO4/NM/CQD is approached by the reduced charge-transfer resistance and charge recombination as well as the enhancement on carrier densities. The excellent long-term stability with the photocurrent retention of 90% under illumination for 24 h is also obtained for BiVO4/NM/CQD. This study opens a blueprint on designing efficient catalysts for water oxidation by incorporating heterojunction and co-catalyst materials simultaneously. © 2023 Hydrogen Energy Publications LLC

关键词： Heterojunctions

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Spatio-Temporal Deep Learning for Final Infarct Prediction Using Acute Stroke CT Perfusion Data 4th

Spatio-Temporal Deep Learning for Final Infarct Prediction U...

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4th International Workshop on Imaging and Treatment Challenges, SWITCH 2024, and 6th International Challenge on Ischemic Stroke Lesion Segmentation Challenge, ISLES 2024, Held in Conjunction with Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Amador, Kimberly Winder, Anthony J. Forkert, Nils D. Biomedical Engineering Graduate Program University of Calgary Calgary Canada Department of Radiology University of Calgary Calgary Canada Hotchkiss Brain Institute University of Calgary Calgary Canada Alberta Children’s Hospital Research Institute University of Calgary Calgary Canada

ISBN: (纸本)9783031811005

Accurate prediction of the tissue outcome is crucial for guiding treatment decisions in acute ischemic stroke (AIS). Spatio-temporal (4D) Computed Tomography Perfusion (CTP) provides detailed insights into cerebral blood flow dynamics, which are essential for predicting final infarct regions. However, its high-dimensional and noisy nature presents challenges for direct prediction. In this study, we evaluate a deep learning model that fully leverages 4D CTP data for predicting tissue outcomes. The model integrates a shared-weight convolutional neural network (CNN) encoder, a Transformer encoder, and a CNN decoder to capture both spatial and temporal dependencies within the data. We evaluated this approach on a multicenter dataset of 143 patients from the ISLES 2024 challenge. The results reveal a Dice score of 0.20, an absolute volume difference of 17 ml, a mean lesion count difference of 19, and a lesion-wise F1-Score of 0.02, underscoring both the potential and challenges of directly utilizing 4D CTP data for final infarct prediction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Efficiently catalyzing photo-oxidation of water by surface engineering of bismuth vanadate with nickel molybdenum oxide coverages

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International Journal of Hydrogen Energy 2024年 51卷 613-622页

作者： Kuo, Tsung-Rong Huang, Hsiao-Wen Kongvarhodom, Chutima Saukani, Muhammad Yougbaré, Sibidou Chen, Hung-Ming Lin, Lu-Yin International PhD Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Graduate Institute of Nanomedicine and Medical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Department of Chemical Engineering and Biotechnology National Taipei University of Technology Taipei Taiwan Department of Chemical Engineering King Mongkut's University of Technology Thonburi 126 Pracha-u-thit Toong-kru Bangkok10140 Thailand Department of Mechanical Engineering Faculty of Engineering Universitas Islam Kalimantan MAB Jl. Adhyaksa No. 2 Banjarmasin70124 Indonesia /Nanoro 03 B.P 7192 Ouagadougou 03 Burkina Faso Gingen Technology Co. LTD. Rm. 7 10F. No.189 Sec. 2 Keelung Rd. Xinyi Dist. Taipei11054 Taiwan

Bismuth vanadate with suitable band edges is one of the efficient photocatalysts for water oxidation. Establishing heterojunction can improve electron diffusion lengths and photocatalytic ability of BiVO4. In this work, nickel molybdenum oxide (Ni–Mo–O) and BiVO4 heterojunction is established by the hydrothermal process with different nickel to molybdenum precursor ratios. The Ni–Mo–O/BiVO4 electrode is applied as photoanodes for water oxidation. The growth mechanism of Ni–Mo–O on BiVO4 surface is proposed. The Ni–Mo–O deposition amount is optimized regarding to light absorbance and charger transportation. The largest photocurrent density of 1.72 mA/cm2 at 1.23 VRHE is obtained for the optimal NiMoO4/BiVO4 electrode (NM12) in the electrolyte without hole scavenger, while the pure BiVO4 electrode only shows a photocurrent density of 0.90 mA/cm2. The NM12 electrode even presented an impressive photocurrent density of 5.39 mA/cm2 at 1.23 VRHE in the electrolyte with the hole scavenger, owing to the abundant active sites and higher light absorbance as well as the favorable synergistic effects from its suitable Ni to Mo ratio. The NM12 electrode also shows excellent long-term stability with the photocurrent retention of 83% after illumination for 6500 s. This work opens a blueprint for establishing a novel heterojunction with the adjustable metal ratio and coverage on BiVO4. © 2023 Hydrogen Energy Publications LLC

关键词： Heterojunctions

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A Physical-Based Artificial Neural Networks Compact Modeling Framework for Emerging FETs

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IEEE Transactions on Electron Devices 2024年第1期71卷 223-230页

作者： Yang, Ya-Shu Li, Yiming Kola, Sekhar Reddy Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory The Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Communications Engineering Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan

We report a compact modeling framework based on the Grove-Frohman (GF) model and artificial neural networks (ANNs) for emerging gate-all-around (GAA) MOSFETs. The framework consists of two ANNs;the first ANN constructed with the drain current model not only can capture the main trend of device I - V characteristics but also can predict its variation even when the amount of training data for the ANN is insufficient or outside the range of applied biases. The second one is then designed to improve the model accuracy by further minimizing the errors between the target and the model outputs. We implement the proposed framework to accurately model emerging GAA nanosheet (NS) MOSFETs and complementary FETs (CFETs) without suffering from divergent issues in circuit simulation. In addition, nonphysical behaviors, such as nonzero current at zero bias, do not occur in the modeling framework. Compared to recently reported machine-learning (ML) models, our approach can achieve a similar level of model accuracy with merely 20% amount of the training data. © 1963-2012 IEEE.

关键词： Neural networks

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Neural Networks and Explainable Artificial Intelligence for Breast Cancer Prediction and Classification

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Procedia Computer Science 2025年 256卷 1159-1166页

作者： Erika Yahata Pablo Deoclecia dos Santos Maria Marlene de Souza Pires Ricardo Suyama Priscyla Waleska Simoes Graduate Program in Information Engineering Federal University of ABC Santo André SP Brazil Graduate Program in Biomedical Engineering Federal University of ABC São Bernardo do Campo SP Brazil Pediatrics Department Federal University of Santa Catarina Florianópolis Santa Catarina Brazil

Solutions based on Artificial Intelligence techniques have been proposed in the healthcare sector, however the lack of understanding of the results has been a cause for concern and one of the main barriers to their effective use. To fill the gap in understanding these models considered black boxes, the Explainable Artificial Intelligence was proposed to explain the relationship between input data and the prediction results made by the Artificial Intelligence models. In this study, we present the use of explainability techniques for a black box machine learning model in breast cancer classification. Breast cancer is the type of cancer that causes the most deaths among women in the world and early diagnosis is essential to increase the chances of survival. The study evaluated the explainability of breast tumor predictions based on the Multilayer Perceptron artificial neural networks algorithm, considering a sample that contained data from 164 female patients undergoing Core Biopsy in the southern region of Brazil. The SHAP, LIME, PDP and ICE methods were used for the global and local explainability of the predictions. The results showed that in the global assessment, the BI-RADS® 5 ultrasound and mammography attributes were considered the most important for predicting the malignant tumor. Nodule size greater than 2 cm, presence of a family history and palpable nodule were also considered important for the prediction. In the local evaluation, it was found that the model correctly classified the tumors considering different characteristics of the patients. Through the results presented, the methods of explicability show evidence that the predictive model for breast cancer can be interpreted and understood, overcoming the barrier of lack understanding of the results of a black box model.

关键词： Explainable Artificial Intelligence Predictive Model Explainability Machine Learning Breast Cancer

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Statistical Device Simulation and Machine Learning of Process Variation Effects of Vertically Stacked Gate-All-Around Si Nanosheet CFETs

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IEEE Transactions on Nanotechnology 2024年 23卷 386-392页

作者： Kola, Sekhar Reddy Li, Yiming Butola, Rajat National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Department of Electronics and Electrical Engineering Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Biomedical Engineering Institute of Pioneer Semiconductor Innovation Institute of Artificial Intelligence Innovation Hsinchu300093 Taiwan

In this study, we report the process variation effect (PVE) including the work function fluctuation (WKF) on the DC/AC characteristic fluctuation of stacked gate-all-around silicon complementary field-effect transistors (CFETs). The PVE affects characteristic fluctuation significantly;in particular, for the variability of off-state current. Owing to the bottom channel of a fin-type, the P-FET suffers from the worst off-state current fluctuation (more than 200% variation) compared to the N-FET. The device variability induced by the WKF is marginal because of amorphous-type metal grains. As input features to an artificial neural network (ANN) model, low and high work function values, as well as parameters of PVE that have prevalent effects on CEFT transfer characteristics are further considered and modeled. The estimated values of R2-score prove that the ANN model properly grasps information from the dataset successfully;thus, it can be used to model emerging CFETs for circuit simulation. © 2002-2012 IEEE.

关键词： Neural networks

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Neuromodulation of the peripheral nervous system:Bioelectronic technology and prospective developments

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BMEMat(biomedical engineering Materials) 2024年第1期2卷 88-114页

作者： Hayoung Song Moohyun Kim Enji Kim Jakyoung Lee Inhea Jeong Kyeonghee Lim Seoung Young Ryu Myoungjae Oh Younhee Kim Jang-Ung Park Department of Materials Science and Engineering Yonsei UniversitySeoulSouth Korea Center for Nanomedicine Institute for Basic Science(IBS)SeoulSouth Korea Department of Neurosurgery Yonsei University College of MedicineSeoulSouth Korea Graduate Program of Nano Biomedical Engineering(NanoBME) Advanced Science InstituteYonsei UniversitySeoulSouth Korea

The peripheral nervous system(PNS)is a fascinatingly complex and crucial component of the human body,responsible for transmitting vital signals throughout the body's intricate network of *** efficient functioning is paramount to our health,with any dysfunction often resulting in serious medical conditions,including motor disorders,neurological diseases,and psychiatric *** strides in science and technology have made neuromodulation of the PNS a promising avenue for addressing these health *** involves modifying nerve activity using a range of techniques,such as electrical,chemical,optical,and mechanical *** plays a critical role in this effort,allowing for precise,controlled,and sustained stimulation of the *** paper provides an overview of the PNS,discusses the current state of neuromodulation devices,and presents emerging trends in the field,including advances in wireless power transfer and materials,that are shaping the future of neuromodulation.

关键词： bioelectronics neuromodulation peripheral nervous system

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VR-Based Implicit Authentication Using Electromyogram and Inertial Measurement Unit 23

VR-Based Implicit Authentication Using Electromyogram and In...

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23rd International Conference on Cyberworlds, CW 2024

作者： Yu, Da-Young Lee, Hyung-Tak Kim, Youngsam Roh, Jong-Hyuk Kim, Soohyung Ku, Jeonghun Hwang, Han-Jeong Korea University Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Dept. Electronics and Information Engineering Sejong Korea Republic of Korea University Dept. Electronics and Information Engineering Sejong Korea Republic of Daejeon Korea Republic of Keimyung University College of Medicine Department of Biomedical Engineering Daegu Korea Republic of

ISBN: (纸本)9798331527174

Biometrics based on electromyogram (EMG) and inertial measurement unit (IMU) signals has received significant attention due to its resistance to forgery and tampering, as well as its relatively higher convenience and security compared to other biometric modalities. However, most EMG and IMU signal-based authentication methods require explicit authentication using unnatural gestures, which is not userfriendly. To address this, we collected EMG and IMU data during virtual reality (VR) rhythm game play, which does not require explicit gestures, and performed implicit authentication. Using a linear discriminant analysis (LDA) classifier, we achieved classification accuracies of 91.71% for human identification and 87.91% for human authentication. The result demonstrated the feasibility of a seamless and implicit authentication in VR environments. © 2024 IEEE.

关键词： Electromyography

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Picrasidine I Regulates Apoptosis in Melanoma Cell Lines by Activating ERK and JNK Pathways and Suppressing AKT Signaling

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Environmental Toxicology 2024年第12期39卷 5309-5320页

作者： Shieu, Mu-Kuei Lin, Chia-Chieh Ho, Hsin-Yu Lo, Yu-Sheng Chuang, Yi-Ching Hsieh, Ming-Ju Department of Dermatology Changhua Christian Hospital Changhua Taiwan Oral Cancer Research Center Changhua Christian Hospital Changhua Taiwan Graduate Institute of Clinical Medicine College of Medicine National Chung Hsing University Taichung Taiwan Doctoral Program in Tissue Engineering and Regenerative Medicine College of Medicine National Chung Hsing University Taichung Taiwan Graduate Institute of Biomedical Sciences China Medical University Taichung Taiwan

World Health Organization data indicate a continuous increase in melanoma incidence, with metastatic melanoma characterized by poor prognosis and drug resistance. The exploration of therapeutics derived from natural products remains an active area of in vitro research. The aim of this study was to determine the antitumor effects of picrasidine I, a natural compound extracted from Picrasma quassioides, against two melanoma cell lines. We selected two metastatic melanoma cell lines, HMY-1 and A2058, for molecular studies, including Western blotting, 4′,6-diamidino-2-phenylindole staining, and flow cytometry. Picrasidine I demonstrated cytotoxic effects against the HMY-1 and A2058 melanoma cell lines. It induced cell cycle arrest in the sub-G1 phase and downregulated cell cycle–related proteins (e.g., cyclin A2, D1, cyclin-dependent kinases 4, and 6). In the intrinsic apoptosis pathway, picrasidine I activated proapoptotic proteins (e.g., Bax, Bak, t-Bid, BimL/S) and suppressed the expression of antiapoptotic proteins (e.g., Bcl-2, Bcl-xL), with an observed increase in the quantity of depolarized cells. In addition, the apoptotic effects of picrasidine I were linked to the activation of the c-Jun N-terminal kinase and extracellular signal-regulated kinase pathways and the inhibition of the protein kinase B signaling pathway. A human apoptosis array indicated claspin inhibition upon picrasidine I treatment, suggesting the potential involvement of picrasidine I in apoptosis and cell cycle regulation. Our findings suggest that picrasidine I has potential as a candidate for treating advanced melanoma, and thus these findings warrant further investigation. The modulation of claspin expression by picrasidine I could be investigated further as a potential biomarker to predict its efficacy in related to advanced stages of melanoma. © 2024 The Author(s). Environmental Toxicology published by Wiley Periodicals LLC.

关键词： Dermatology

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