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arXiv 2024年

作者： Dong, Zijian Wu, Yilei Chen, Zijiao Zhang, Yichi Jin, Yueming Zhou, Juan Helen Centre for Sleep and Cognition Centre for Translational Magnetic Resonance Research Yong Loo Lin School of Medicine National University of Singapore Singapore Human Potential Translational Research Program Department of Medicine Yong Loo Lin School of Medicine National University of Singapore Singapore NUS Graduate School National University of Singapore Singapore Department of Biomedical Engineering National University of Singapore Singapore

We introduce Scaffold Prompt Tuning (ScaPT), a novel prompt-based framework for adapting large-scale functional magnetic resonance imaging (fMRI) pre-trained models to downstream tasks, with high parameter efficiency and improved performance compared to fine-tuning and baselines for prompt tuning. The full fine-tuning updates all pre-trained parameters, which may distort the learned feature space and lead to overfitting with limited training data which is common in fMRI fields. In contrast, we design a hierarchical prompt structure that transfers the knowledge learned from high-resource tasks to low-resource ones. This structure, equipped with a Deeply-conditioned Input-Prompt (DIP) mapping module, allows for efficient adaptation by updating only 2% of the trainable parameters. The framework enhances semantic interpretability through attention mechanisms between inputs and prompts, and it clusters prompts in the latent space in alignment with prior knowledge. Experiments on public resting state fMRI datasets reveal ScaPT outperforms fine-tuning and multitask-based prompt tuning in neurodegenerative diseases diagnosis/prognosis and personality trait prediction, even with fewer than 20 participants. It highlights ScaPT’s efficiency in adapting pre-trained fMRI models to low-resource tasks. © 2024, CC BY-NC-SA.

关键词： Neurodegenerative diseases

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Spatial Distribution of Reduced Density of Hard Spheres Near a Hard-Sphere Dimer: Results from Three-Dimensional Ornstein–Zernike Equations Coupled with Several Different Closures and from Grand Canonical Monte Carlo Simulation

SSRN

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SSRN 2023年

作者： Matsuo, Mika Nakamura, Yuka Kinoshita, Masahiro Akiyama, Ryo Department of Chemistry Kyushu University Fukuoka819-0395 Japan Interdisciplinary Program of Biomedical Engineering Assistive Technology and Art and Sports Sciences Faculty of Engineering Niigata University Niigata950-2181 Japan Department of Chemistry Graduate School of Science Chiba University Chiba263-8522 Japan Center for the Promotion of Interdisciplinary Education and Research Kyoto University Kyoto606-8501 Japan

We calculated the spatial distribution of the reduced density and pair distribution function (PDF) of solvent hard spheres near a solute using three-dimensional Ornstein–Zernike (OZ) equations coupled with closures in which Percus–Yevick (PY) and hypernetted–chain (HNC) approximations were used or in which the bridge functions (BFs)proposed by Verlet, Duh and Henderson, and Kinoshita were incorporated. The solute was formed by two solvent hard spheres in contact with each other, with the result that the system was not radial-symmetric, necessitating the extension of the Verlet, Duh–Henderson, and Kinoshita BFs to three-variable functions considered in the Cartesian coordinate system. The results were compared with those from grand canonical Monte Carlo (MC) simulations. In terms of the PDF, HNC was superior to PY in the sense that the results from the former are closer to those from MC. Incorporation of the three BFs made the results even closer to those from MC. The three BFs shared almost the sameperformance for a solute immersed in a one-component solvent at the infinite-dilution limit. Analyses on the triplet distribution function (TDF) were also performed. It was found that in terms of the TDF, the incorporation of the BFs does not necessarily lead to improvement. © 2023, The Authors. All rights reserved.

关键词： Distribution functions

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Receptor Ligand-Free Mesoporous Silica Nanoparticles: A Streamlined Strategy for Targeted Drug Delivery across the Blood-Brain Barrier

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ACS Nano 2024年第20期18卷 12716-12736页

作者： Chen, Zih-An Wu, Cheng-Hsun Wu, Si-Han Huang, Chiung-Yin Mou, Chung-Yuan Wei, Kuo-Chen Yen, Yun Chien, I-Ting Runa, Sabiha Chen, Yi-Ping Chen, Peilin Department of Chemistry National Taiwan University Taipei10617 Taiwan Graduate Institute of Nanomedicine and Medical Engineering Taipei Medical University Taipei11031 Taiwan Research Center for Applied Sciences Academia Sinica Taipei11529 Taiwan Nano Targeting & Therapy Biopharma Inc. Taipei10087 Taiwan International Ph.D. Program in Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Neuroscience Research Center Chang Gung Memorial Hospital Taoyuan33305 Taiwan Department of Neurosurgery Chang Gung Memorial Hospital Taoyuan33305 Taiwan School of Medicine Chang Gung University Taoyuan33302 Taiwan Department of Neurosurgery New Taipei Municipal TuCheng Hospital New Taipei City23652 Taiwan Center for Cancer Translational Research Tzu Chi University Hualien970374 Taiwan Cancer Center Taipei Municipal WanFang Hospital Taipei116081 Taiwan SRS Medical Communications LLC ClevelandOH44124 United States

Mesoporous silica nanoparticles (MSNs) represent a promising avenue for targeted brain tumor therapy. However, the blood-brain barrier (BBB) often presents a formidable obstacle to efficient drug delivery. This study introduces a ligand-free PEGylated MSN variant (RMSN25-PEG-TA) with a 25 nm size and a slight positive charge, which exhibits superior BBB penetration. Utilizing two-photon imaging, RMSN25-PEG-TA particles remained in circulation for over 24 h, indicating significant traversal beyond the cerebrovascular realm. Importantly, DOX@RMSN25-PEG-TA, our MSN loaded with doxorubicin (DOX), harnessed the enhanced permeability and retention (EPR) effect to achieve a 6-fold increase in brain accumulation compared to free DOX. In vivo evaluations confirmed the potent inhibition of orthotopic glioma growth by DOX@RMSN25-PEG-TA, extending survival rates in spontaneous brain tumor models by over 28% and offering an improved biosafety profile. Advanced LC-MS/MS investigations unveiled a distinctive protein corona surrounding RMSN25-PEG-TA, suggesting proteins such as apolipoprotein E and albumin could play pivotal roles in enabling its BBB penetration. Our results underscore the potential of ligand-free MSNs in treating brain tumors, which supports the development of future drug-nanoparticle design paradigms. © 2024 The Authors. Published by American Chemical Society.

关键词： Targeted drug delivery

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A nanotherapeutic strategy to target drug-tolerant cells and overcome EGFR tyrosine kinase inhibitor resistance in lung cancer

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Cancer Communications 2023年第4期43卷 503-507页

作者： Tatiana Shaurova Lingyue Yan Yafei Su Laurie James Rich Vui King Vincent-Chong Hannah Calkins Saraswati Pokharel Martin Petkovich Mukund Seshadri Yun Wu Pamela Anne Hershberger Department of Pharmacology and Therapeutics Roswell Park Comprehensive Cancer CenterBuffaloNew YorkUSA Department of Biomedical Engineering University at Buffalothe State University of New YorkBuffaloNew YorkUSA Cell Stress and Biophysical Oncology Graduate Program Roswell Park Comprehensive Cancer CenterBuffaloNew YorkUSA Fujifilm-Visual Sonics Corporation TorontoOntarioCanada Department of Oral Oncology Roswell Park Comprehensive Cancer CenterBuffaloNew YorkUSA Department of Pathology Roswell Park Comprehensive Cancer CenterBuffaloNew YorkUSA Cancer Research Institute School of MedicineQueens UniversityKingstonOntarioCanada

Dear Editor,For patientswith epidermal growth factor receptor(EGFR)mutant non-small cell lung cancer(NSCLC),EGFR tyrosine kinase inhibitors(TKIs)are used as the first-line treatment[1,2].Despite initial therapeutic responses,patients invariably experience disease progression due to acquired drug resistance[3].Resistance arises,in part,because a subset of cancer cells undergoes epithelial-mesenchymal transition(EMT)and remains viable despite exposure to EGFR TKI concentrations that eliminate the bulk population[4].The surviving cells can be re-sensitized to treatment by prolonged culture in the absence of EGFR TKIs,indicating a transient.

关键词： therapeutic viable resistance

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Interpretable ECG Analysis for Myocardial Infarction Detection through Counterfactuals

arXiv

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arXiv 2023年

作者： Tanyel, Toygar Atmaca, Sezgin Gökçe, Kaan Balık, M. Yiğit Güler, Arda Aslanger, Emre Öksüz, İlkay Biomedical Engineering Graduate Program Istanbul Technical University Istanbul Turkey Mehmet Akif Ersoy Thoracic and Cardiovascular Surgery Training and Research Hospital Istanbul Turkey Department of Computer Science Aalto University Espoo Finland Department of Cardiology Başakşehir Pine and Sakura City Hospital Istanbul Turkey Department of Computer Engineering Istanbul Technical University Istanbul Turkey

In the evolving landscape of ECG signal analysis, the challenge of limited transparency in machine learning models remains a significant barrier to their effective integration into clinical practice. This study addresses this issue by investigating the use of counterfactual explanations to improve model interpretability for clinicians, particularly in differentiating healthy subjects from myocardial infarction patients. Utilizing the PTB-XL dataset, we developed a methodology for systematic feature extraction and refinement to prepare for counterfactual analysis. This led to the creation of the Visualizing Counterfactual Clues on Electrocardiograms (VCCE) method, designed to improve the practicality of counterfactual explanations in a clinical setting. The validity of our approach was assessed using custom metrics that reflect the diagnostic relevance of counterfactuals, evaluated with the help of two cardiologists. Our findings suggest that this approach could support future efforts in using ECGs to predict patient outcomes for cardiac conditions, achieving interpretation validity scores of 23.29 ± 1.04 and 20.28 ± 0.99 out of 25 for high and moderate-quality interpretations, respectively. Clinical alignment scores of 0.83 ± 0.12 for high-quality and 0.57 ± 0.10 for moderate-quality interpretations underscore the potential clinical applicability of our method. The methodology and findings of this study contribute to the ongoing discussion on enhancing the interpretability of machine learning models in cardiology, offering a concept that bridges the gap between advanced data analysis techniques and clinical decision-making. The source code for this study is available at https://***/tanyelai/vcce. © 2023, CC BY-NC-SA.

关键词： Contrastive Learning

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Multimodal Spatial Attention Network for Automatic Head and Neck Tumor Segmentation in FDG-PET and CT Images 1

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2nd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021

作者： Cho, Minjeong Choi, Yujin Hwang, Donghwi Yie, Si Young Kim, Hanvit Lee, Jae Sung School of Electrical and Electronics Engineering Chung-Ang University Seoul06974 Korea Republic of Department of Biomedical Engineering Kyunghee University College of Electronics and Information 1732 Deogyeong-daero Giheung-gu Gyeonggi-do Yongin-si Korea Republic of Department of Nuclear Medicine Seoul National University College of Medicine Seoul03080 Korea Republic of Department of Biomedical Sciences Seoul National University College of Medicine Seoul03080 Korea Republic of Interdisciplinary Program in Bioengineering Seoul National University Graduate School Seoul Korea Republic of Integrated Major in Innovative Medical Science Seoul National University Graduate School Seoul Korea Republic of Ulsan Korea Republic of Artificial Intelligence Institute Seoul National University Seoul08826 Korea Republic of Brightonix Imaging Inc. Seoul04782 Korea Republic of

ISBN: (数字)9783030982539

ISBN: (纸本)9783030982522

Quantitative positron emission tomography/computed tomography (PET/CT), owing to the functional metabolic information and anatomical information of the human body that it presents, is useful to achieve accurate tumor delineation. However, manual annotation of a Volume Of Interest (VOI) is a labor-intensive and time-consuming task. In this study, we automatically segmented the Head and Neck (H&N) primary tumor in combined PET/CT images. Herein, we propose a convolutional neural network named Multimodal Spatial Attention Network (MSA-Net), supplemented with a Spatial Attention Module (SAM), which uses a PET image as an input. We evaluated this model on the MICCAI 2021 HEad and neCK TumOR (HECKTOR) segmentation challenge dataset. Our method delivered a competitive cross-validation efficiency, with Dice Similarity Coefficient (DSC) 0.757, precision 0.788, and recall 0.785. When we tested out method on test dataset, we achieved an average DSC and Hausdorff Distance at 95% (HD95) of 0.766 and 3.155 respectively. Our team name is ‘Heck_Uihak’. © 2022, Springer Nature Switzerland AG.

关键词： Tumors

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Enhancing Diagnostic Accuracy for Knee Osteoarthritis Using a Deep Learning Approach

Enhancing Diagnostic Accuracy for Knee Osteoarthritis Using ...

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International Symposium on Applied Science 2024, ISAS 2024

作者： Raza, Aquib Ching, Congo Tak Shing Liu, Peng-Ta Phan, Thien-Luan Tran, Ngoc-Luan Nguyen, Ha Anh T. Rameez, Mohammad Li, Hung-Chung Linh, Dao Nguyen Phuong Van Hieu, Nguyen Tran, Trung Nghia National Taiwan University Taipei City10673 Taiwan Research Center for Information Technology Academia Sinica Taipei City11529 Taiwan Graduate Institute of Biomedical Engineering National Chung Hsing University Taichung402 Taiwan Department of Electrical Engineering National Chi Nan University Nantou County Taiwan Advanced Plant and Food Crop Biotechnology Center National Chung Hsing University Taichung402 Taiwan International Doctoral Program in Agriculture National Chung Hsing University Taichung402 Taiwan Fall Prevention Center Department of Physical Medicine & Rehabilitation Changhua Christian Hospital Changhua500 Taiwan Department of Physics and Electronic Engineering University of Science Vietnam National University - Ho Chi Minh City Ho Chi Minh City70000 Viet Nam Department of Computer Science University of York York HeslingtonYO10 5DD United Kingdom Undergraduate Program of Intellectual Creativity Engineering National Chung Hsing University Taichung402 Taiwan Ho Chi Minh City72409 Viet Nam HCMUT Vietnam National University Ho Chi Minh City Thu Duc Ho Chi Minh City71308 Viet Nam

This research introduces a deep learning framework that combines convolutional neural networks with autoencoders to improve the diagnostic accuracy of knee osteoarthritis. The study utilized a publicly available dataset from the Mendeley Data repository, where knee joint images were preprocessed using Contrast Limited Adaptive Histogram Equalization to enhance contrast and ensure consistent image sizing. Initially, an autoencoder was employed in an unsupervised manner to learn compressed representations of the data. These representations were then used to train a CNN to classify knee images into 'healthy' and 'osteoarthritic' categories. The model was trained using an 80:20 split for training and testing data, incorporating early stopping and data augmentation techniques to prevent overfitting and enhance generalization. Performance metrics such as accuracy, precision, recall, and the area under the Receiver Operating Characteristic curve (ROCAUC) were used to evaluate the model, resulting in a high test accuracy of 98.95%. The high precision and recall rates further confirmed the model's robustness in diagnosing KOA. To enhance interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to identify critical regions in the images that influenced the model's decisions. Additionally, t-Distributed Stochastic Neighbor Embedding (t-SNE) was used to visualize the high-dimensional encoded features in a two-dimensional space, providing deeper insights into how the model differentiates between classes. The promising results of this deep learning approach suggest its potential for broader applications in medical imaging diagnostics. By providing healthcare professionals with a tool that enhances diagnostic accuracy and operational efficiency, this framework lays the foundation for developing scalable, AI-driven diagnostic systems for various complex medical conditions. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Joints (anatomy)

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Machine-learned molecular surface and its application to implicit solvent simulations

arXiv

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arXiv 2021年

作者： Wei, Haixin Zhao, Zekai Luo, Ray Departments of Materials Science and Engineering Molecular Biology and Biochemistry Chemical and Biomolecular Engineering and Biomedical Engineering Graduate Program in Chemical and Materials Physics University of California IrvineCA92697 United States

Implicit solvent models, such as Poisson-Boltzmann models, play important roles in computational studies of biomolecules. A vital step in almost all implicit solvent models is to determine the solvent-solute interface, and the solvent excluded surface (SES) is the most widely used interface definition in these models. However, classical algorithms used for computing SES are geometry-based, thus neither suitable for parallel implementations nor convenient for obtaining surface derivatives. To address the limitations, we explored a machine learning strategy to obtain a level-set formulation for the SES. The training process was conducted in three steps, eventually leading to a model with over 95% agreement with the classical SES. Visualization of tested molecular surfaces shows that the machine-learned SES overlaps with the classical SES on almost all situations. We also implemented the machine-learned SES into the Amber/PBSA program to study its performance on reaction field energy calculation. The analysis shows that the two sets of reaction field energies are highly consistent with 1% deviation on average. Given its level-set formulation, we expect the machine-learned SES to be applied in molecular simulations that require either surface derivatives or high efficiency on parallel computing platforms. © 2021, CC BY.

关键词： Solvents

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Statistical Analysis of Intrinsic High-Frequency Characteristic Fluctuation of Emerging Silicon Gate-All-Around Nanosheet (NS) MOSFETs at Sub-3-nm Nodes

Statistical Analysis of Intrinsic High-Frequency Characteris...

引用

IEEE Electron Devices Technology and Manufacturing Conference (EDTM)

作者： Sekhar Reddy Kola Yiming Li Min-Hui Chuang Kazuhiko Endo Seiji Samukawa Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Institute of Communications Engineering Department of Electronics and Electrical Engineering Institute of Biomedical Engineering Center for mm Wave Smart Radar System and Technologies National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Fluid Science Tohoku University 2-1-1 Katahira Aoba-ku Sendai Japan National Institute of Advanced Industrial Science and Technology Tsukuba Japan

In recent years, the GAA NS Si MOSFET has been explored as a leading technology. However, the intrinsic parameters of GAA NS Si MOSFETs are affected to varying degrees by various fluctuation sources, Statistically independent and identically distributed $(iid)$ assumptions on the aforementioned random variables overestimate the variability of high-frequency characteristics, compared with considering all fluctuation factors simultaneously. Notably, the random nanosized metal grains dominates the variations of voltage gain, cut-off frequency, and 3dB frequency because the random work functions strongly alter the channel surface potential.

关键词： MOSFET Fluctuations Statistical analysis Gallium arsenide Cutoff frequency Metals Logic gates

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A Chemoimmunotherapy Nanogel Enables Efficient Delivery of Interleukin-2 and Induction of Immunogenic Cell Death for Effective Cancer Therapy

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Advanced Functional Materials 2024年第1期34卷

作者： Mu, Hsuan-Yu Ta, Yen-Nhi Ngoc Tham, Max Jing Rui Hsu, Fu-Fei Lin, Yu-Chieh Huang, Hsi-Chien Sung, Yun-Chieh Huang, Chih-I Wu, Ching-Ling Chang, Chao-Hung Yang, Sheng Lee, Tsung-Ying Wan, Dehui Wang, Jane Duda, Dan G. Boucher, Yves Huang, Jen-Huang Ang, Wee Han Chen, Yunching Institute of Biomedical Engineering National Tsing Hua University Hsinchu30013 Taiwan Department of Chemical Engineering National Tsing Hua University Hsinchu30013 Taiwan International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu30013 Taiwan Department of Chemistry National University of Singapore Singapore117544 Singapore NUS Graduate School for Integrative Sciences and Engineering National University of Singapore Singapore119077 Singapore Institute of Biomedical Sciences Academia Sinica Taipei11529 Taiwan Steele Laboratories for Tumor Biology Department of Radiation Oncology Massachusetts General Hospital and Harvard Medical School BostonMA02114 United States Department of Chemistry National Tsing Hua University Hsinchu30013 Taiwan

Interleukin-2 (IL-2) is one of the first FDA-approved immunotherapeutics, but its use is limited by toxicity and low efficacy. In addition, all immunotherapies are limited by the immunosuppressive and desmoplastic microenvironment of "immunologically cold" tumors, such as pancreatic ductal adenocarcinoma (PDAC) or hepatocellular carcinoma (HCC) with advanced liver fibrosis. Here, a new chemoimmunotherapy nanogel (IL2-Pt@Nanogel) for dual delivery of IL-2 and the type II immunogenic cell death inducer Pt-NHC that reduces the immunosuppressive phenotype of tumor-associated macrophages and diminishes regulatory T cell infiltration by inducing the production of type I interferon (IFN) by cancer cells is reported. Combining the angiotensin II receptor blocker losartan with IL2-Pt@Nanogel treatment reduces desmoplasia and reprogrammes the microenvironment of PDAC and HCC toward an immunostimulatory one. These effects result in potent anti-tumor efficacy in models of primary and metastatic PDAC and HCC with underlying liver fibrosis. This study presents a strategy for IL-2-based chemoimmunotherapy with the potential for clinical translation to treat solid tumors. © 2023 Wiley-VCH GmbH.

关键词： Nanogels

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