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NIR-Responsive Methotrexate-Modified Iron Selenide Nanorods for Synergistic Magnetic Hyperthermic, Photothermal, and Chemodynamic Therapy

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ACS Applied Materials and Interfaces 2024年第20期16卷 25622-25636页

作者： Thirumurugan, Senthilkumar Muthiah, Kayalvizhi Samuvel Lin, Yu-Chien Dhawan, Udesh Liu, Wai-Ching Wang, An-Ni Liu, Xinke Hsiao, Michael Tseng, Ching-Li Chung, Ren-Jei No. 1 Section 3 Zhongxiao East Road Taipei10608 Taiwan Centre for the Cellular Microenvironment Division of Biomedical Engineering James Watt School of Engineering Mazumdar-Shaw Advanced Research Centre University of Glasgow GlasgowG116EW United Kingdom Faculty of Science and Technology Technological and Higher Education Institute of Hong Kong Hong Kong New Territories999077 Hong Kong Scrona AG Grubenstrasse 9 Zürich8045 Switzerland College of Materials Science and Engineering Chinese Engineering and Research Institute of Microelectronics Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Genomics Research Center Academia Sinica Taipei115 Taiwan Department and Graduate Institute of Veterinary Medicine School of Veterinary Medicine National Taiwan University Taipei10617 Taiwan Graduate Institute of Biomedical Materials and Tissue Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan International Ph.D. Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Research Center of Biomedical Device College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan International Ph.D. Program in Cell Therapy and Regenerative Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan Taipei10608 Taiwan

Breast cancer is a malignant tumor with a high mortality rate among women. Therefore, it is necessary to develop novel therapies to effectively treat this disease. In this study, iron selenide nanorods (FeSe2 NRs) were designed for use in magnetic hyperthermic, photothermal, and chemodynamic therapy (MHT/PTT/CDT) for breast cancer. To illustrate their efficacy, FeSe2 NRs were modified with the chemotherapeutic agent methotrexate (MTX). MTX-modified FeSe2 (FeSe2-MTX) exhibited excellent controlled drug release properties. Fe2+ released from FeSe2 NRs induced the release of •OH from H2O2 via a Fenton/Fenton-like reaction, enhancing the efficacy of CDT. Under alternating magnetic field (AMF) stimulation and 808 nm laser irradiation, FeSe2-MTX exerted potent hyperthermic and photothermal effects by suppressing tumor growth in a breast cancer nude mouse model. In addition, FeSe2 NRs can be used for magnetic resonance imaging in vivo by incorporating their superparamagnetic characteristics into a single nanomaterial. Overall, we presented a novel technique for the precise delivery of functional nanosystems to tumors that can enhance the efficacy of breast cancer treatment. © 2024 The Authors. Published by American Chemical Society

关键词： Magnetic resonance imaging

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One-Pot Multi-frame Denoising

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International Journal of Computer Vision 2024年第2期132卷 515-536页

作者： Jin, Lujia Guo, Qing Zhao, Shi Zhu, Lei Chen, Qian Ren, Qiushi Lu, Yanye Institute of Medical Technology Peking University Health Science Center Peking University Beijing100191 China Department of Biomedical Engineering College of Future Technology Peking University Beijing100871 China Institute of Biomedical Engineering Peking University Shenzhen Graduate School Shenzhen518055 China Institute of Biomedical Engineering Shenzhen Bay Laboratory Shenzhen518071 China National Biomedical Imaging Center Peking University Beijing100871 China Chinese Academy of Cultural Heritage Beijing100029 China School of Physics Peking University Beijing100871 China

The efficacy of learning-based denoising techniques is heavily reliant on the quality of clean supervision. Unfortunately, acquiring clean images in many scenarios is a challenging task. Conversely, capturing multiple noisy frames of the same field of view is feasible and natural in real-life scenarios. Thus, it is imperative to explore the potential of noisy data in model training and avoid the limitations imposed by clean labels. In this paper, we propose a novel unsupervised learning strategy called one-pot denoising (OPD), which is the first unsupervised multi-frame denoising method. OPD differs from traditional supervision schemes, such as supervised Noise2Clean, unsupervised Noise2Noise, and self-supervised Noise2Void, as it employs mutual supervision among all the multiple frames. This provides learning with more diverse supervision and allows models to better exploit the correlation among frames. Notably, we reveal that Noise2Noise is a special case of the proposed OPD. We provide two specific implementations, namely OPD-random coupling and OPD-alienation loss, to achieve OPD during model training based on data allocation and loss refine, respectively. Our experiments demonstrate that OPD outperforms other unsupervised denoising methods and is comparable to non-transformer-based supervised N2C methods for several classic noise patterns, including additive white Gaussian noise, signal-dependent Poisson noise, and multiplicative Bernoulli noise. Additionally, OPD shows remarkable performance in more challenging tasks such as mixed-blind denoising, denoising random-valued impulse noise, and text removal. The source code and pre-trained models are available at https://***/LujiaJin/One-Pot_Multi-Frame_Denoising . © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Unsupervised learning

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Classification of auditory ERPs for ADHD detection in children

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Journal of Medical engineering and Technology 2025年第3期49卷 69-78页

作者： Mercado-Aguirre, I. Gutiérrez-Ruiz, K. Contreras-Ortiz, S.H. Biomedical Engineering Program Universidad Tecnológica de Bolívar Cartagena de Indias Colombia Psychology Program Universidad Tecnológica de Bolívar Cartagena de Indias Colombia

Attention deficit hyperactivity disorder (ADHD) is one of the children’s most common neurodevelopmental conditions. ADHD diagnosis is based on evaluating inattention, hyperactivity, and impulsivity symptoms that interfere with or reduce daily functioning. Although electroencephalography (EEG) tests are used for ADHD diagnosis, they are generally considered a complement to clinical evaluation. This paper proposes an approach to classify EEG records of children with ADHD and control cases. We identified and extracted relevant features from EEG signals of 47 children (22 diagnosed with ADHD and 25 controls) and evaluated machine learning techniques for classification. We used the 2-tone oddball paradigm to elicit the subjects’ auditory event-related potentials (ERP), and we recorded EEG signals with a portable headset for approximately five minutes. In the feature extraction stage, we included measures from cognitive evoked potentials, frequency bands power, chaos quantification, and bispectral analysis, in addition to the age of the children and the number of high-pitched tones the children counted during the test. The SVM and Trees algorithms obtained the best performance for 86.36% accuracy and 95.45% sensitivity. These findings demonstrate the potential of portable EEG-based systems to complement standard clinical assessments, offering an objective, time-efficient, and accessible approach to support early ADHD diagnosis. Achieving high accuracy and sensitivity in classification is critical to reducing the risk of misdiagnosis and ensuring timely intervention, ultimately improving patient outcomes. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Electroencephalography

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Injection-Locked Frequency Sixtuplers in 90 nm CMOS by Using the Push-Push Doubler

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IEEE Access 2023年 11卷 130048-130059页

作者： Cheng, Sheng-Jen Shen, Pi-Neng Hong, Chung-Hung Chen, Zheng-Wei Chen, Chung-Ping Jang, Sheng-Lyang Graduate Institute of Electronics Engineering National Taiwan University Taipei10617 Taiwan National Taiwan University of Science and Technology Department of Electronic Engineering Taipei106335 Taiwan China Medical University College of Biomedical Engineering Department of Biomedical Engineering Taichung404333 Taiwan National Taipei University of Technology Department of Electronic Engineering Taipei10608 Taiwan

This paper designs two single-stage LC-tank injection-locked frequency sixtuplers (ILFSs) fabricated in a 90 nm CMOS process and it describes the circuit design, operation principle, and measurement results of the ILFSs. The ILFS circuit with a differential input and single-phase output is made of a first-harmonic injection-locked oscillator (ILO), a frequency tripler, and a push-push doubler. The first ILFS uses an octagonal inductor. For a supply voltage of 0.4 V, the free-running frequency is around 41.52 GHz, DC power consumption is 9.03 mW at the incident power of 0 dBm, and the output locking range at 0 dBm input power is 10.21 %. The second ILFS uses an 8-shaped inductor for low electromagnetic (EM) noise generation. The free-running frequency is around 37.2 GHz, DC power consumption is 7.72 mW at the incident power of 0 dBm, and the output locking range at 0 dBm input power is 17.4 %. The two chips have the same area, 0.7×0.7 mm2. This paper also provides a further analysis and comparison of the RF performance of on-chip inductors designed. Simulation shows the substrate noise coupling and distance noise coupling between victim and aggressor, the 8-shaped inductor exhibits a significant reduction in coupling. By the measurement results, it is evident that the performance of the 8-shaped inductor ILFS is superior to that of the octagonal inductor ILFS. © 2023 The Authors.

关键词： Harmonic analysis

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Back to Basics Series： Medical Ultrasonics

Transactions of Japanese Society for Medical and Biological ...

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Transactions of Japanese Society for Medical and Biological engineering 2022年第2-3期60卷 76-81页

作者： Umemura, Shin-Ichiro Tohoku University Japan Graduate School of Biomedical Engineering Tohoku University Japan

A creative engineer tends to understand the principles and phenomena not only based on mathematical physics but also by intuition. Author has picked up several topics among such principles and phenomena in the area of medical ultrasound and challenges to explain them intuitively in this article. The chosen topics are the lateral resolution, Doppler blood flow measurement, a piezoelectric element, ultrasonic absorption by biological tissues, and intensive/extensive variables. Author hopes that the presented intuitive explanation will help the readers to enhance their creativity as an engineer. © 2022, Japan Soc. of Med. Electronics and Biol. engineering. All rights reserved.

关键词： Ultrasonics

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Brain age estimation:premise,promise,and problems

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Neural Regeneration Research 2025年第8期20卷 2313-2314页

作者： Jarrad Perron Ji Hyun Ko Graduate Program in Biomedical Engineering Price Faculty of EngineeringUniversity of ManitobaWinnipegMBCanada PrairieNeuro Research Centre Kleysen Institute for Advanced MedicineHealth Sciences CentreWinnipegMBCanada Department of Human Anatomy and Cell Science Rady Faculty of Health SciencesUniversity of ManitobaWinnipegMBCanada

Premise:The com bined effects of modern healthcare practices which prolong lifespan and declining birthrates have created unprecedented changes in age demographics worldwide that are especially pronounced in Japan,South Korea,Europe,and North *** old age is the most significant predictor of dementia,global healthcare systems must rise to the challenge of providing care for those with neurodegenerative disorders.

关键词： estimation providing birth

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Convolutional Neural Network-Based Identity Recognition Using ECG atDifferent Water Temperatures During Bathing

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Computers, Materials & Continua 2022年第4期71卷 1807-1819页

作者： Jianbo Xu Wenxi Chen Biomedical Information Engineering Laboratory Graduate School of Computer Science and EngineeringUniversity of AizuAizu-Wakamatsu965-8580Japan

This study proposes a convolutional neural network(CNN)-based identity recognition scheme using electrocardiogram(ECG)at different water temperatures(WTs)during bathing,aiming to explore the impact of ECG length on the recognition *** data was collected using non-contact electrodes at five different WTs during *** young student subjects(seven men and three women)participated in data *** ECG recordings were collected at each preset bathtub WT for each *** recording is 18 min long,with a sampling rate of 200 *** total,150 ECG recordings and 150 WT recordings were *** R peaks were detected based on the processed ECG(baseline wandering eliminated,50-Hz hum removed,ECG smoothing and ECG normalization)and the QRS complex waves were *** segmented waves were then transformed into binary images,which served as the *** each subject,the training,validation,and test data were taken from the first,second,and third ECG recordings,*** number of training and validation images was 84297 and 83734,*** the test stage,the preliminary classification results were obtained using the trained CNN model,and the finer classification results were determined using the majority vote method based on the preliminary *** validation rate was 98.71%.The recognition rates were 95.00%and 98.00%when the number of test heartbeats was 7 and 17,respectively,for each subject.

关键词： Electrocardiogram QRS recognition rate water temperatures convolutional neural network majority vote

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Neural Networks and Explainable Artificial Intelligence for Breast Cancer Prediction and Classification 16

Neural Networks and Explainable Artificial Intelligence for ...

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16th International Conference on ENTERprise Information Systems, CENTERIS 2024 - 12th International Conference on Project MANagement, ProjMAN 2024 - 14th International Conference on Health and Social Care Information Systems and Technologies, HCist 2024

作者： Yahata, Erika Santos, Pablo Deoclecia Dos De Souza Pires, Maria Marlene Suyama, Ricardo Simoes, Priscyla Waleska Graduate Program in Information Engineering Federal University of ABC SP Santo André Brazil Graduate Program in Biomedical Engineering Federal University of ABC São Bernardo Do Campo Brazil Pediatrics Department Federal University of Santa Catarina 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. © 2025 Elsevier B.V.. All rights reserved.

关键词： Prediction models

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Advanced YOLOv7-Based Detection and Enumeration of Teeth and Restorations in Panoramic X-Rays 23

Advanced YOLOv7-Based Detection and Enumeration of Teeth and...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Anas Ali, M.D. Fujita, Daisuke Kobashi, Syoji Jashore University of Science and Technology Department of biomedical engineering Jashore Bangladesh Graduate School of Engineering University of Hyogo Himeji Japan

ISBN: (纸本)9798331528041

Dental radiography plays a crucial role in oral health assessment, aiding dentists in diagnosis and treatment planning. This study introduces an automated method for enumerating teeth, implants, and pontics of dental bridges in panoramic dental X-rays using the YOLOv7 network. The method detects these elements and employs an innovative optimization and dental bridge segmentation algorithm for accurately enumerating teeth and restorations. In this study, we analyzed 1000 panoramic dental X-ray images, including 450 with dental bridges and 64 with implants. For evaluation, 200 images were selected, revealing a mean Average Precision (mAP) of 98.15% for tooth and restoration detection, with overall precision of 0.9774 and overall recall of 0.9803 for enumeration, demonstrating practical utility of the proposed method in clinical dentistry. © 2024 IEEE.

关键词： Restoration

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An Electrochemical HbA1c Immunosensor With a Simple Electrode of Gold Nanoparticle Monolayer

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IEEE Sensors Journal 2023年第15期23卷 16579-16584页

作者： Tseng, Chun-Chiang Lin, Ying-Ting Wang, Gou-Jen National Chung-Hsing University Department of Mechanical Engineering Taichung40227 Taiwan National Chung-Hsing University Program in Tissue Engineering and Regenerative Medicine Taichung40227 Taiwan National Chung-Hsing University Department of Mechanical Engineering The Graduate Institute of Biomedical Engineering Taichung40227 Taiwan

This study developed a novel electrochemical glycosylated hemoglobin (HbA1c) immunosensor. Polyethylene terephthalate (PET) was used as a substrate. After gold (Au) film sputtering on the PET surface, 1,6-hexanedithol with thiol groups on both terminals was used as an intermediary to attach a monolayer of uniformly distributed Au nanoparticles (AuNPs) to the Au thin film to form a nanostructured electrode. HbA1c antibodies were successfully immobilized on the AuNP monolayer via the self-assembled monolayer (SAM) approach. To mimic a real clinical detection environment, a calibration curve for HbA1c detection was performed in a mixture solution containing heparin sodium, red blood cell (RBC) lysis buffer, artificial human serum, and hemoglobin. The experimental results revealed that the proposed HbA1c immunosensor detected HbA1c in a linear range from 2500 to 10 000 ng/mL with a limit of detection of 2253 ng/mL. The developed HbA1c-sensing chip offers the advantages of simple operation, low cost, short detection time, and provides high feasibility for clinical application. © 2001-2012 IEEE.

关键词： Gold nanoparticles

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