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Characterization of silk fibroin-based microneedles and in vitro study of stromal cell-derived factor-1-loaded microneedles on adipose stem cell recruitment

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International Journal of Biological Macromolecules 2023年第1期233卷 123537页

作者： Chen, Wu-Xun Cheng, Nai-Chen Chen, Yan-Jun Lee, I-Chi Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Surgery National Taiwan University Hospital and College of Medicine 7 Chung-Shan S Rd Taipei100 Taiwan Graduate Institute of Biomedical Engineering Chang Gung University Taoyuan Taiwan

Silk fibroin (SF) has good biocompatibility, degradability and mechanical properties. In this study, SF-based microneedle (MN) patches were fabricated as stromal cell-derived factor-1 (SDF-1) carriers that may be used for adipose stem cell (ASC) recruitment. Therefore, SF was chosen as the main MN material to achieve sustained drug release. In addition, the variations in SF-based MN crystallinity after water annealing treatment were also determined. The results indicated that SF-based MN patches were successfully fabricated with a 3M™ commercial template and Polydimethylsiloxane mold. Through optical coherence tomography, it was found that all of the SF-based MN patches prepared in this study had sufficient strength to penetrate the skin to a depth of approximately 400 μm. Sustained release of the model drug-dextran from the SF-based MNs was demonstrated. Although SF-based MNs release SDF-1 in a sustained manner, the quantity released can be regulated and improved. Subsequently, dual-layer SDF-1-loaded MNs fabricated with a gelatin tip and SF body were prepared to enhance SDF-1 release for ASC recruitment. SF-based MNs can show good penetration ability and provide good sustained release while dual-layer MNs can regulate the amount of drug released, which could present an alternative for stem cell therapy. © 2023 Elsevier B.V.

关键词： Silicones

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Assessing the Generalizability of a Deep Learning-based Automated Atrial Fibrillation Algorithm

Assessing the Generalizability of a Deep Learning-based Auto...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Ahmadreza Argha Joan Li Joseph Magdy Hamid Alinejad-Rokny Branko G. Celler Ken Butcher Sze-Yuan Ooi Nigel H. Lovell Graduate School of Biomedical Engineering UNSW Sydney Tyree Institute of Health Engineering (IHealthE) Prince of Wales Clinical School UNSW Sydney School of Electrical Engineering and Telecommunication UNSW Sydney

Automated detection of atrial fibrillation (AF) from electrocardiogram (ECG) traces remains a challenging task and is crucial for telemonitoring of patients after stroke. This study aimed to quantify the generalizability of a deep learning (DL)-based automated ECG classification algorithm. We first developed a novel hybrid DL (HDL) model using the PhysioNet/CinC Challenge 2017 (CinC2017) dataset (publicly available) that can classify the ECG recordings as one of four classes: normal sinus rhythm (NSR), AF, other rhythms (OR), and too noisy (TN) recordings. The (pre)trained HDL was then used to classify 636 ECG samples collected by our research team using a handheld ECG device, CONTEC PM10 Portable ECG Monitor, from 102 (age: 68 ± 15 years, 74 male) outpatients of the Eastern Heart Clinic and inpatients in the Cardiology ward of Prince of Wales Hospital, Sydney, Australia. The proposed HDL model achieved average test F 1 -score of 0.892 for NSR, AF, and OR, relative to the reference values, on the CinC2017 dataset. The HDL model also achieved an average F 1 -score of 0.722 (AF: 0.905, NSR: 0.791, OR: 0.471 and TN: 0.342) on the dataset created by our research team. After retraining the HDL model on this dataset using a 5-fold cross validation method, the average F 1 -score increased to 0.961. We finally conclude that the generalizability of the HDL-based algorithm developed for AF detection from short-term single-lead ECG traces is acceptable. However, the accuracy of the pre-trained DL model was significantly improved by retraining the model parameters on the new dataset of ECG traces.

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Computer-Aided Diagnosis of Duchenne Muscular Dystrophy Based on Texture Pattern Recognition on Ultrasound Images Using Unsupervised Clustering Algorithms and Deep Learning

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Ultrasound in Medicine and Biology 2024年第7期50卷 1058-1068页

作者： Liao, Ai-Ho Wang, Chih-Hung Wang, Chong-Yu Liu, Hao-Li Chuang, Ho-Chiao Tseng, Wei-Jye Weng, Wen-Chin Shih, Cheng-Ping Tsui, Po-Hsiang Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Biomedical Engineering National Defense Medical Center Taipei Taiwan Division of Otolaryngology Taipei Veterans General Hospital Taoyuan Branch Taoyuan Taiwan Graduate Institute of Medical Sciences National Defense Medical Center Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Department of Mechanical Engineering National Taipei University of Technology Taipei Taiwan Department of Pediatrics National Taiwan University Hospital and College of Medicine National Taiwan University Taipei Taiwan Department of Pediatric Neurology National Taiwan University Children's Hospital Taipei Taiwan Department of Otolaryngology-Head and Neck Surgery Tri-Service General Hospital National Defense Medical Center Taipei Taiwan Department of Medical Imaging and Radiological Sciences College of Medicine Chang Gung University Taoyuan Taiwan Institute for Radiological Research Chang Gung University and Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan Research Center for Radiation Medicine Chang Gung University Taoyuan Taiwan

Objective: The feasibility of using deep learning in ultrasound imaging to predict the ambulatory status of patients with Duchenne muscular dystrophy (DMD) was previously explored for the first time. The present study further used clustering algorithms for the texture reconstruction of ultrasound images of DMD data sets and analyzed the difference in echo intensity between disease stages. Methods: k-means (Kms) and fuzzy c-means (FCM) clustering algorithms were used to reconstruct the DMD data-set textures. Each image was reconstructed using seven texture-feature categories, six of which were used as the primary analysis items. The task of automatically identifying the ambulatory function and DMD severity was performed by establishing a machine-learning model. Results: The experimental results indicated that the Gaussian Naïve Bayes and k-nearest neighbors classification models achieved an accuracy of 86.78% in ambulatory function classification. The decision-tree model achieved an identification accuracy of 83.80% in severity classification. A deep convolutional neural network model was established as the main structure of the deep-learning model while automatic auxiliary interpretation tasks of ambulatory function and severity were performed, and data augmentation was used to improve the recognition performance of the trained model. Both the visual geometry group (VGG)-16 and VGG-19 models achieved 98.53% accuracy in ambulatory-function classification. The VGG-19 model achieved 92.64% accuracy in severity classification. Conclusion: Regarding the overall results, the Kms and FCM clustering algorithms were used in this study to reconstruct the characteristic texture of the gastrocnemius muscle group in DMD, which was indeed helpful in quantitatively analyzing the deterioration of the gastrocnemius muscle group in patients with DMD at different stages. Subsequent combination of machine-learning and deep-learning technologies can automatically and accurately assist i

关键词： Deterioration

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An Annealing-Inspired Gradient-Descent Based Suboptimal Solver for Combinatorial Problems

An Annealing-Inspired Gradient-Descent Based Suboptimal Solv...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Shu-Ping Chang Cheng-Che Lee Hsin-Jung Lee Chieh-Hsiung Kuan Jason Gemsun Young Chia-Yu Yao Jian-Jiun Ding Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Graduate Institute of Electronics Engineering National Taiwan University Electronic and Optoelectronic System Research Laboratories Industrial Technology Research Institute Department of Electrical Engineering National Taiwan University of Science and Technology Graduate Institute of Communication Engineering National Taiwan University

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

Combinatorial optimization problems, such as IC layout and industrial scheduling, have significant industrial applications but are challenging due to exponential time complexity. In this work, we propose a novel annealing-inspired heuristic algorithm that treats combinatorial problems as function optimization problems using nonlinear programming. The proposed gradient-descent-based solver significantly improves the convergence rate and includes a new regularization constraint to escape local minima by increasing convexity. Applied to the Traveling Salesman Problem (TSP) with various city counts, the proposed algorithm demonstrates polynomial time complexity. It much reduces the complexity from (n−1)!/2 to n 4 and has a marked improvement in computation efficiency. Notably, for a 50-city TSP, the relative error is just around 5%, indicating the accuracy and efficiency of the proposed algorithm in solving high-dimensional instances.

关键词： Visualization Technological innovation Annealing Accuracy Urban areas Traveling salesman problems Polynomials Stability analysis Time complexity Optimization

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Classification of Extracellular Vesicles Based on Surface Glycan Structures by Spongy-like Separation Media

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Analytical Chemistry 2022年第51期94卷 18025-18033页

作者： Kanao, Eisuke Wada, Shuntaro Nishida, Hiroshi Kubo, Takuya Tanigawa, Tetsuya Imami, Koshi Shimoda, Asako Umezaki, Kaori Sasaki, Yoshihiro Akiyoshi, Kazunari Adachi, Jun Otsuka, Koji Ishihama, Yasushi Graduate School of Pharmaceutical Sciences Kyoto University Sakyo-ku Kyoto606-8501 Japan National Institutes of Biomedical Innovation Health and Nutrition Osaka Ibaraki567-0085 Japan Department of Material Chemistry Graduate School of Engineering Kyoto University Katsura Nishikyo-ku Kyoto615-8510 Japan 4-1-8 Honcho Kawaguchi Saitama332-0012 Japan Department of Polymer Chemistry Graduate School of Engineering Kyoto University Katsura Nishikyo-ku Kyoto615-8510 Japan

Extracellular vesicles (EVs) are lipid bilayer vesicles that enclose various biomolecules. EVs hold promise as sensitive biomarkers to detect and monitor various diseases. However, they have heterogeneous molecular compositions. The compositions of EVs from identical donor cells obtained using the same purification methods may differ, which is a significant obstacle for elucidating objective biological functions. Herein, the potential of a novel lectin-based affinity chromatography (LAC) method to classify EVs based on their glycan structures is demonstrated. The proposed method utilizes a spongy-like monolithic polymer (spongy monolith, SPM), which consists of poly(ethylene-co-glycidyl methacrylate) with continuous micropores and allows an efficient in situ protein reaction with epoxy groups. Two distinct lectins with different specificities, Sambucus sieboldiana agglutinin and concanavalin A, are effectively immobilized on SPM without impacting the binding activity. Moreover, high recovery rates of liposomal nanoparticles as a model of EVs are achieved due to the large flow-through pores (>10 μm) of SPM compared to a typical agarose gel. Finally, lectin-immobilized SPMs are employed to classify EVs based on the surface glycan structures and demonstrate different subpopulations by proteome profiling. This is the first approach to clarify the variation of protein contents in EVs by the difference of surface glycans via lectin immobilized media. © 2022 Authors. All rights reserved.

关键词： Affinity chromatography

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Improvement of clinical wound microcirculation diagnosis using an object tracking-based laser speckle contrast imaging system

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APL Bioengineering 2024年第1期8卷 016105页

作者： Hsieh, Meng-Che Chang, Chia-Yu Hsu, Ching-Han Lin, Yan-Ren Hsieh, Pei-You Ching, Congo Tak-Shing Liao, Lun-De Institute of Biomedical Engineering and Nanomedicine National Health Research Institutes 35 Keyan Road Zhunan Town Miaoli County 35053 Taiwan Program in Tissue Engineering and Regenerative Medicine National Chung Hsing University 145 Xingda Road South District Taichung402 Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University No. 101 Section 2 Kuang-Fu Road Hsinchu30013 Taiwan Department of Emergency and Critical Care Medicine Changhua Christian Hospital Changhua Taiwan Graduate Institute of Biomedical Engineering National Chung Hsing University 145 Xingda Road South District Taichung402 Taiwan Department of Electrical Engineering National Chi Nan University 1 University Road Puli Township Nantou County 545301 Taiwan

Wound monitoring is crucial for effective healing, as nonhealing wounds can lead to tissue ulceration and necrosis. Evaluating wound recovery involves observing changes in angiogenesis. Laser speckle contrast imaging (LSCI) is vital for wound assessment due to its rapid imaging, high resolution, wide coverage, and noncontact properties. When using LSCI equipment, regions of interest (ROIs) must be delineated in lesion areas in images for quantitative analysis. However, patients with serious wounds cannot maintain constant postures because the affected areas are often associated with discomfort and pain. This leads to deviations between the drawn ROI and actual wound position when using LSCI for wound assessment, affecting the reliability of relevant assessments. To address these issues, we used the channel and spatial reliability tracker object tracking algorithm to develop an automatic ROI tracking function for LSCI systems. This algorithm is used to track and correct artificial movements in blood flow images, address the ROI position offset caused by the movement of the affected body part, increase the blood flow analysis accuracy, and improve the clinical applicability of LSCI systems. ROI tracking experiments were performed by simulating wounds, and the results showed that the intraclass correlation coefficient (ICC) ranged from 0.134 to 0.976. Furthermore, the object within the ROI affected tracking performance. Clinical assessments across wound types showed ICCs ranging from 0.798 to 0.917 for acute wounds and 0.628-0.849 for chronic wounds. We also discuss factors affecting tracking performance and propose strategies to enhance implementation effectiveness. © 2024 Author(s).

关键词： Image enhancement

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3D Brain Tumor Segmentation in Multimodal MRI Images

3D Brain Tumor Segmentation in Multimodal MRI Images

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Nisar Ahmad Yao-Tien Chen International Ph.D. Program in Innovative Technology of Biomedical Engineering and Medical Devices Ming Chi University of Technology New Taipei City Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

In brain tumor research, the use of machine learning based methods using MRI data to locate brain tumors accurately has significantly increased and new techniques are proposed to facilitate the medical experts. There is still room for improvement as tumors have complex anatomy which requires efforts to explore further and propose new deep learning based solutions. In this research, we propose 3D UNet based multimodal brain tumor segmentation for Non Enhancing Tumor (NET), Peritumoral Edema (PE), and Enhancing Tumor (ET). Data preprocessing and augmentation techniques are employed to optimise the model's performance. The dataset used in this study to train, validate, and test the model performance is the BraTS 2020 dataset. The model achieved improved accuracy and IoU after implementing the mentioned techniques. Results before and after data augmentation demonstrate a significant enhancement in model performance. The model achieved an improved mIoU of 0.77 and accuracies of 98.21% and 99.36% for actual data and augmented data for three brain tumor classes, i.e., NET, PE, and ET. Our model performed multiclass brain tumor segmentation with high accuracies, and we aim to enhance this study to propose a valuable medical assistance model.

关键词： Solid modeling Image segmentation Three-dimensional displays Accuracy Magnetic resonance imaging Brain modeling Data augmentation

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One-Pot Multi-Frame Denoising

arXiv

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arXiv 2023年

作者： Jin, Lujia Zhao, Shi Zhu, Lei Chen, Qian Lu, Yanye Department of Biomedical Engineering Peking University Beijing China Institute of Medical Technology Peking University Beijing China Institute of Biomedical Engineering Peking University Shenzhen Graduate School Shenzhen China School of Physics Peking University Beijing China National Biomedical Imaging Center Peking University Beijing China

The performance of learning-based denoising largely depends on clean supervision. However, it is difficult to obtain clean images in many scenes. On the contrary, the capture of multiple noisy frames for the same field of view is available and often natural in real life. Therefore, it is necessary to avoid the restriction of clean labels and make full use of noisy data for model training. So we propose an unsupervised learning strategy named one-pot denoising (OPD) for multi-frame images. OPD is the first proposed unsupervised multi-frame denoising (MFD) method. Different from the traditional supervision schemes including both supervised Noise2Clean (N2C) and unsupervised Noise2Noise (N2N), OPD executes mutual supervision among all of the multiple frames, which gives learning more diversity of supervision and allows models to mine deeper into the correlation among frames. N2N has also been proved to be actually a simplified case of the proposed OPD. From the perspectives of data allocation and loss function, two specific implementations, random coupling (RC) and alienation loss (AL), are respectively provided to accomplish OPD during model training. In practice, our experiments demonstrate that OPD behaves as the SOTA unsupervised denoising method and is comparable to supervised N2C methods for synthetic Gaussian and Poisson noise, and real-world optical coherence tomography (OCT) speckle noise. © 2023, CC BY-NC-ND.

关键词： Optical tomography

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Tube-shaped Neural Probe with Electrodes Placed Around the Optical Stimulation Area or the Endoscope Observation Area

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IEEJ Transactions on Sensors and Micromachines 2022年第4期142卷 48-55页

作者： Ikeda, Naoto Lu, Wenguang Tsuruoka, Noriko Mushiake, Hajime Osanai, Makoto Member, Tomokazu Oshiro Haga, Yoichi Graduate School of Biomedical Engineering Tohoku University 6-6-12 Azaaoba Aramaki Aoba-ku Sendai980-8579 Japan Department of Physiology Graduate School of Medicine Tohoku University 2-1 Seiryo-machi Aoba-ku Sendai980-8579 Japan Division of Health Sciences Graduate School of Medicine Osaka University 1-7 Yamadaoka Suita565-0871 Japan

In this paper, a tube-shaped neural probe which can be combined with ultra-thin endoscope or optical fiber for optical stimulation was designed and developed. This probe is made of zirconia ceramic tube, and six electrodes were fabricated on the tip of the tube circumferentially using our developed nonplanar photofabrication process. FPC (Flexible Printed Circuit) was used for transmitting the signal from the probe to recording machine, in order not to disturb the action of a rat. The probe can be implanted in the deep brain of a rat and perform electrical recording during optical stimulation or endoscopic observation after recovery of the rat. The impedance of this probe was evaluated using three-electrodes method and in vivo test was performed using a genetically engineered rat for reaction of optical stimulation. As the result, the probe recorded neural activity of the rat at rest and after optical stimulation, and this device could be combined with the endoscope. This device may serve for optical and electrical measurement simultaneously for neuroscience researchers. © 2022 The Institute of Electrical Engineers of Japan.

关键词： Rats

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A new approach to predict the performance of objective response detectors from Monte Carlo simulations

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Research on biomedical engineering 2022年第4期38卷 1113-1119页

作者： Passos, Marcos Aurelio Freire Ferraz de Sá, Antônio Maurício Ferreira Leite Miranda Antunes, Felipe Felix, Leonardo Bonato Axis of Control and Industrial Processes Federal Institute Sul Rio Grandense RS Santana do Livramento Brazil Biomedical Engineering Program Federal University of Rio de Janeiro RJ Rio de Janeiro Brazil Department of Electrical Engineering Federal Institute of Education Science and Technology of Minas Gerais—Ipatinga Campus MG Ipatinga Brazil Department of Electrical Engineering Federal University of Viçosa MG Viçosa Brazil

Purpose: Objective response detection (ORD) techniques assess if evoked potentials are embedded in the background electroencephalogram (EEG). If each technique gives its result, then it is necessary to check which test performs it better. Therefore, a methodology derived from Monte Carlo (MC) simulation was proposed to compare five ORD techniques. Methods: It was based on estimating the probability of detection (PD) of the tests for the different number of data segments (windows) and with a signal-to-noise ratio (SNR) ranging from − 40 to 0 dB. Moreover, an EEG database of subjects under auditory stimulation was used in order to validate the proposed methodology. Results: The simulation results showed that the detectors that used the neighboring frequency-band to the interest frequency, as the local spectral F test and Hotelling’s T2, have a better PD when the number of windows used is less than about 20. Conversely, when they are compared in cases with a greater number of windows, magnitude squared coherence, and modified Rayleigh test tend to have a higher PD value. These predictions were validated with the EEG signals. Conclusion: Therefore, this work predicted the performance of detectors only by means of MC simulation, as a function of the number of windows and tuned to an SNR range consistent with the EEG. © 2022, The Author(s), under exclusive licence to The Brazilian Society of biomedical engineering.

关键词： Signal to noise ratio

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