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IEEE/IAS International Conference on Industry Applications (INDUSCON)

作者： Lucas Henrique Brito Santos Eduardo José Monteiro Da Silva Aleqssandro Alexandre De Oliveira Farias Jhennifer Freitas Dos Santos Iago Ranieri Miranda Rodrigues Morais Wellington Da Silva Fonseca Marcelo De Oliveira e Silva Faculty of Electrical and Biomedical Engineering Federal University of Pará Belém Brazil Electrical Engineering Graduate Program Federal University of Pará Belém Brazil Mechanical Engineering Graduate Program Federal University of Pará Belém Brazil

Nowadays, industrial processes are creating increasing demands for optimized and efficient sensing systems and require high reliability and a short time interval for information delivery. This paper presents an embedded sensor system that uses the ESP-NOW protocol with a Mesh and Ad Hoc network, in order to provide an auto mesh configuration upon failure at some point in the network. This system was implemented to monitor the variables of an induction motor, after which, before sending the data to the cloud, local processing is performed to reduce the volume of data. Finally, a computer simulation of the WROOM-32 module is performed for a thermal analysis during its operation, where the heating points of the plate are identified and analyzed, in order to validate that the system, even processing a large amount of data, maintains a high reliability during its operation.

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Functional Peptide-Loaded Gelatin Nanoparticles as Eyedrops for Cornea Neovascularization Treatment

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International Journal of Nanomedicine 2023年 18卷 1413-1431页

作者： Chu, Ya-Chun Fang, Hsu-Wei Wu, Yu-Yi Tang, Yu-Jun Hsieh, Erh-Hsuan She, Yizhou Chang, Che-Yi Lin, I-Chan Chen, Yin-Ju Liu, Guei-Sheung Tseng, Ching-Li Graduate Institute of Biomedical Materials and Tissue Engineering College of Biomedical Engineering Taipei Medical University Taipei City Taiwan Department of Chemical Engineering and Biotechnology National Taipei University of Technology Taipei City Taiwan Institute of Biomedical Engineering and Nanomedicine National Health Research Institutes Miaoli County Taiwan School of Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei City Taiwan Department of Ophthalmology Wan Fang Hospital Taipei Medical University Taipei City Taiwan Department of Ophthalmology School of Medicine College of Medicine Taipei Medical University Taipei City Taiwan Department of Radiation Oncology Taipei Medical University Hospital Taipei City Taiwan International Ph.D. Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei City Taiwan Center for Precision Medicine and Translational Cancer Research Taipei Medical University Hospital Taipei City Taiwan Centre for Eye Research Australia Royal Victorian Eye and Ear Hospital East Melbourne VIC Australia Ophthalmology Department of Surgery University of Melbourne East Melbourne VIC Australia Menzies Institute for Medical Research University of Tasmania Hobart TAS Australia Research Center of Biomedical Device College of Biomedical Engineering Taipei Medical University Taipei City Taiwan

Background: Corneal neovascularization (NV) is a process of abnormal vessel growth into the transparent cornea from the limbus and can disturb the light passing through the cornea, resulting in vision loss or even blindness. The use of nanomedicine as an effective therapeutic formulation in ophthalmology has led to higher drug bioavailability and a slow drug release rate. In this research, we designed and explored the feasibility of a new nanomedicine, gp91 ds-tat (gp91) peptide-encapsulated gelatin nanoparticles (GNP-gp91), for inhibiting corneal angiogenesis. Methods: GNP-gp91 were prepared by a two-step desolvation method. The characterization and cytocompatibility of GNP-gp91 were analyzed. The inhibition effect of GNP-gp91 on HUVEC cell migration and tube formation was observed by an inverted microscope. The drug retention test in mouse cornea was observed by in vivo imaging system, fluorescence microscope, and DAPI/TAMRA staining. Finally, the therapeutic efficacy and evaluation of neovascularization-related factors were conducted through the in vivo corneal NV mice model via topical delivery. Results: The prepared GNP-gp91 had a nano-scale diameter (550.6 nm) with positive charge (21.7 mV) slow-release behavior (25%, 240hr). In vitro test revealed that GNP-gp91 enhanced the inhibition of cell migration and tube formation capacity via higher internalization of HUVEC. Topical administration (eyedrops) of the GNP-gp91 significantly prolongs the retention time (46%, 20 min) in the mouse cornea. In chemically burned corneal neovascularization models, corneal vessel area with a significant reduction in GNP-gp91 group (7.89%) was revealed when compared with PBS (33.99%) and gp91 (19.67%) treated groups via every two days dosing. Moreover, GNP-gp91 significantly reduced the concentration of Nox2, VEGF and MMP9 in NV’s cornea. Conclusion: The nanomedicine, GNP-gp91, was successfully synthesized for ophthalmological application. These data suggest that GNP-gp91 contain

关键词： control release corneal neovascularization eye drops gelatin gp91 peptide nanoparticles ocular retention

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3D Image Generation from X-Ray Projections Using Generative Adversarial Networks

3D Image Generation from X-Ray Projections Using Generative ...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Mohammad AlShurbaji Salam Dhou Biomedical Engineering Graduate Program American University of Sharjah Sharjah UAE Department of Computer Science and Engineering American University of Sharjah Sharjah UAE

The human brain can effortlessly imagine a 3D image from only 2D images with a little expertise and imagination, but for machines, this is not a trivial task. Because of this, reconstructing 3D images from 2D ones is a hot topic and has many applications. In this paper, we propose a Generative Adversarial Network (GAN)-based approach that generates CT-like images using pairs of orthogonal X-ray projections taken from different angles. In this work, a variety of orthogonal pairs from different angles, ranging from 0°&90° to 60°&150°, were considered as input to the 3D image generation model. The effectiveness of the proposed method was assessed by measuring the Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR), which resulted in values of 0.641 and 29.21, respectively. Furthermore, the model's ability to capture the respiratory motion in the input projections and reflect it in the generated images was also assessed. This work demonstrated the feasibility of generating CT-like images from X-ray projections captured from different orthogonal angles taking into consideration the respiratory motion exhibited in these projections.

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Optically-generated focused ultrasound for noninvasive brain stimulation with ultrahigh precision

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Light(Science & Applications) 2022年第11期11卷 2817-2831页

作者： Yueming Li Ying Jiang Lu Lan Xiaowei Ge Ran Cheng Yuewei Zhan Guo Chen Linli Shi Runyu Wang Nan Zheng Chen Yang Ji-Xin Cheng Department of Mechanical Engineering Boston UniversityBostonMA 02215USA Graduate Program for Neuroscience Boston UniversityBostonMA 02215USA Department of Electrical and Computer Engineering Boston UniversityBostonMA 02215USA Department of Chemistry Boston UniversitBostonMA 02215USA Department of Biomedical Engineering Boston UniversityBostonMA 02215USA Division of Materials Science and Engineering Boston UniversityBostonMA 02215USA Department of Biological Engineering Massachusetts Institute of TechnologyCambridgeMA 02142USA

High precision neuromodulation is a powerful tool to decipher neurocircuits and treat neurological *** non-invasive neuromodulation methods offer limited precision at the milimeter ***,we report opticallygenerated focused ultrasound(OFUS)for non-invasive brain stimulation with ultrahigh *** is generated by a soft optoacoustic pad(SOAP)fabricated through embedding candle soot nanoparticles in a curved polydimethylsiloxane *** generates a transcranial ultrasound focus at 15 MHz with an ultrahigh lateral resolution of 83μm,which is two orders of magnitude smaller than that of conventional transcranial-focused ultrasound(tFUS).Here,we show effective OFUS neurostimulation in vitro with a single ultrasound *** demonstrate submillimeter transcranial stimulation of the mouse motor cortex in *** acoustic energy of 0.6 mJ/cm?,four orders of magnitude less than that of tFUS,is suffcient for successful OFUS *** offers new capabilities for neuroscience studies and disease treatments by delivering a focus with ultrahigh precision noninvasively.

关键词： SOAP stimulation precision

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Mxene/Polydimethylsiloxane (PDMS) Based Implantable and Flexible Bioelectrodes for Neural Sensing

Mxene/Polydimethylsiloxane (PDMS) Based Implantable and Flex...

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2023 Advances in Science and engineering Technology International Conferences, ASET 2023

作者： Baz Khan, Kashif Rast Al-Othman, Amani Al-Nashash, Hasan Al-Sayah, Mohammad American University of Sharjah Biomedical Engineering Graduate Program Sharjah United Arab Emirates American University of Sharjah Department of Chemical and Biological Engineering Sharjah United Arab Emirates American University of Sharjah Department of Electrical Engineering Sharjah United Arab Emirates American University of Sharjah Chemistry and Environmental Sciences Sharjah United Arab Emirates

ISBN: (纸本)9781665454742

Research applications of neural sensing and muscle stimulation have been advanced by the development of bioelectrodes that are biocompatible, flexible and long lasting. The existing metallic made bioelectrodes exhibit extremely high electrochemical resistances that would interfere with the neural-electrode bridge and suffer from mechanical and immunological mismatch. Novel, flexible, implantable bioelectrodes based on MXenes (Ti3C2) and Polydimethylsiloxane (PDMS) composite are proposed. Mxenes possess the advantage of having metallic ultra-conductive transition metals consisting of large groups of carbides, nitrides, or carbonitrides, while PDMS has inherent biostability, flexibility and biocompatibility. In the current research, PDMS has been used as the supporting matrix for conductive fillers such as PMMA, PEDOT, and Mxenes. A series of characterization techniques were performed such as Electrochemical Impedance Spectroscopy (EIS), Cyclic Voltammetry (CV) and mechanical testing. The average results of triplicate samples exhibited samples' bulk impedance of 0.913 kΩ, an impedance of 1.612 MΩ, at 1 kHz frequency and a conductivity of 1.41x10-4S/cm, charge storage capacity of 0.056 mC/cm2 and Young's modulus of 3.81 MPa. © 2023 IEEE.

关键词： Elastic moduli

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DEVELOPING A DIGITAL TWIN FOR SINGLE-CELL MECHANICAL PHENOTYPING MICROFLUIDIC DEVICES 26

DEVELOPING A DIGITAL TWIN FOR SINGLE-CELL MECHANICAL PHENOTY...

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26th International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2022

作者： Roychowdhury, Sayan Mahmud, Samreen T. Puleri, Daniel F. Lai, Andre Rex, Rachel Li, Brian Sohn, Lydia L. Randles, Amanda Department of Biomedical Engineering Duke University United States UC Berkeley-UC San Francisco Graduate Program in Bioengineering University of California Berkeley United States Department of Mechanical Engineering University of California Berkeley United States

ISBN: (纸本)9781733419048

Microfluidics can be used to characterize individual cells in a population based on their mechanical traits. In this work, we digitally replicate the microfluidic device developed by Li et al. [1] and perform simulations of a representative AP-1060 cell. We show that the numerical model is able to recover whole-cell deformability index, a metric used to quantify the resistance of cells to compressive deformation, within 3% of the experimental average. These results pave the way for future studies which utilize experimental and computational approaches to optimize geometric design parameters, complement signal analysis, and enhance the acquisition of mechanical information. © 2022 MicroTAS 2022 - 26th International Conference on Miniaturized Systems for Chemistry and Life Sciences. All rights reserved.

关键词： Fluid structure interaction

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Enhanced Performance and Data Privacy in Lung Nodule Classification via Federated Deep Learning Approach 24

Enhanced Performance and Data Privacy in Lung Nodule Classif...

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2024 7th International Conference on Healthcare Service Management, ICHSM 2024

作者： Nguyen, Duc-Khanh Li, Ai-Hsien Adam Lai, Yen-Jun Chiu, Yen-Ling Phan, Dinh-Van Chien, Ting-Ying Chan, Chien-Lung Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Statistics and Informatics University of Economics The University of Danang Danang Viet Nam Division of Cardiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan ZDT Group- YZU Joint Research and Development Center for Big Data Taoyuan Taiwan Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400710162

This study explores the feasibility of deep learning for classifying nodule neoplasms, analyzing their performance on two openly available datasets, LUNGx SPIE, and LIDC-IDRI. These datasets offer valuable diversity in image quantity, quality, and malignancy annotation methodologies, posing a real-world challenge for effective model development. To handle the disparities in these diverse datasets, we aim to develop a robust classification model trained using federated learning. This decentralized approach allows model training across datasets without sharing patient data, preserving privacy and overcoming data fragmentation. Our experiments demonstrate that federated learning enables the development of a robust and superior deep learning model compared to individual models trained on limited data. This combination of deep learning, and federated learning holds immense promise for nodule neoplasm classification and can significantly improve patient care and outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Mammographic Breast Positioning Assessment via Deep Learning 1st

Mammographic Breast Positioning Assessment via Deep Learnin...

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1st Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care, Deep-Breath 2024

作者： Tanyel, Toygar Denizoglu, Nurper Seker, Mustafa Ege Alis, Deniz Cerekci, Esma Karaarslan, Ercan Aribal, Erkin Oksuz, Ilkay Biomedical Engineering Graduate Program Istanbul Technical University Istanbul Turkey Department of Radiology Acibadem Healthcare Group Istanbul Turkey Department of Radiology University of Wisconsin-Madison MadisonWI United States Department of Radiology School of Medicine Acibadem University Istanbul Turkey Sisli Hamidiye Etfal Training and Research Hospital Istanbul Turkey Department of Computer Engineering Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9783031777882

Breast cancer remains a leading cause of cancer-related deaths among women worldwide, with mammography screening as the most effective method for the early detection. Ensuring proper positioning in mammography is critical, as poor positioning can lead to diagnostic errors, increased patient stress, and higher costs due to recalls. Despite advancements in deep learning (DL) for breast cancer diagnostics, limited focus has been given to evaluating mammography positioning. This paper introduces a novel DL methodology to quantitatively assess mammogram positioning quality, specifically in mediolateral oblique (MLO) views using attention and coordinate convolution modules. Our method identifies key anatomical landmarks, such as the nipple and pectoralis muscle, and automatically draws a posterior nipple line (PNL), offering robust and inherently explainable alternative to well-known classification and regression-based approaches. We compare the performance of proposed methodology with various regression and classification-based models. The CoordAtt UNet model achieved the highest accuracy of 88.63% ± 2.84 and specificity of 90.25% ± 4.04, along with a noteworthy sensitivity of 86.04% ± 3.41. In landmark detection, the same model also recorded the lowest mean errors in key anatomical points and the smallest angular error of 2.42∘. Our results indicate that models incorporating attention mechanisms and CoordConv module increase the accuracy in classifying breast positioning quality and detecting anatomical landmarks. Furthermore, we make the labels and source codes available to the community to initiate an open research area for mammography, accessible at https://***/tanyelai/deep-breast-positioning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Diseases

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Semilicoisoflavone B induces oral cancer cell apoptosis by targeting claspin and ATR-Chk1 signaling pathways

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Environmental Toxicology 2024年第4期39卷 2417-2428页

作者： Hsieh, Ming-Ju Lin, Chia-Chieh Lo, Yu-Sheng Chuang, Yi-Ching Ho, Hsin-Yu Chen, Mu-Kuan Oral Cancer Research Center Changhua Christian Hospital Changhua 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 Department of Otorhinolaryngology Head and Neck Surgery Changhua Christian Hospital Changhua Taiwan Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan

The prevalence of oral squamous cell carcinoma (OSCC) is increasing worldwide mainly due to poor oral hygiene and unrestricted lifestyle. Advanced-stage OSCC is associated with poor prognosis and a 5-year survival rate of only 30%–50%. The present study was designed to investigate the anticancer effect and mode of action of Glycyrrhiza-derived semilicoisoflavone B (SFB) in 5-fluorourasil (5FU)-resistant human OSCC cell lines. The study findings revealed that SFB significantly reduces OSCC cell viability and colony formation ability by arresting cell cycle at the G2/M and S phases and reducing the expressions of key cell cycle regulators including cyclin A, cyclin B, CDC2, and CDK2. The compound caused a significant induction in the percentage of nuclear condensation and apoptotic cells in OSCC. Regarding pro-apoptotic mode of action, SFB was found to increase Fas-associated death domain and death receptor 5 expressions and reduce decoy receptor 2 expression, indicating involvement of extrinsic pathway. Moreover, SFB was found to increase pro-apoptotic Bim expression and reduce anti-apoptotic Bcl-2 and Bcl-xL expressions, indicating involvement of intrinsic pathway. Moreover, SFB-mediated induction in cleaved caspases 3, 8, and 9 and cleaved poly(ADP-ribose) polymerase confirmed the induction of caspase-mediated apoptotic pathways. Regarding upstream signaling pathway, SFB was found to reduce extracellular signal regulated kinase 1/2 (ERK) phosphorylation to execute its pro-apoptotic activity. The Human Apoptotic Array findings revealed that SFB suppresses claspin expression, which in turn caused reduced phosphorylation of ATR, checkpoint kinase 1 (Chk1), Wee1, and CDC25C, indicating disruption of ATR-Chk1 signaling pathway by SFB. Taken together, these findings indicate that SFB acts as a potent anticancer compound against 5FU-resistant OSCC by modulating mitogen-activated protein kinase (MAPK) and ATR-Chk1 signaling pathways. © 2024 Wiley Periodicals LLC.

关键词： Phosphorylation

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Dimensionality reduction in 3D causal deep learning for neuroimage generation: an evaluation study

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Journal of Medical Imaging 2025年第2期12卷

作者： Ohara, Erik Y. Vigneshwaran, Vibujithan Souza, Raissa Vamosi, Finn G. Wilms, Matthias Forkert, Nils D. University of Calgary Biomedical Engineering Graduate Program CalgaryAB Canada University of Calgary Department of Radiology CalgaryAB Canada University of Calgary Hotchkiss Brain Institute CalgaryAB Canada University of Calgary Alberta Children's Hospital Research Institute CalgaryAB Canada University of Calgary Department of Computer Science CalgaryAB Canada University of Calgary Departments of Pediatrics and Community Health Sciences CalgaryAB Canada

Purpose: Causal deep learning (DL) using normalizing flows allows the generation of true counterfactual images, which is relevant for many medical applications such as explainability of decisions, image harmonization, and in-silico studies. However, such models are computationally expensive when applied directly to high-resolution 3D images and, therefore, require image dimensionality reduction (DR) to efficiently process the data. The goal of this work was to compare how different DR methods affect counterfactual neuroimage generation. Approach: Five DR techniques [2D principal component analysis (PCA), 2.5D PCA, 3D PCA, autoencoder, and Vector Quantised-Variational AutoEncoder] were applied to 23,692 3D brain images to create low-dimensional representations for causal DL model training. Convolutional neural networks were used to quantitatively evaluate age and sex changes on the counterfactual neuroimages. Age alterations were measured using the mean absolute error (MAE), whereas sex changes were assessed via classification accuracy. Results: The 2.5D PCA technique achieved the lowest MAE of 4.16 when changing the age variable of an original image. When sex was changed, the autoencoder embedding led to the highest classification accuracy of 97.84% while also significantly impacting the age variable predictions, increasing the MAE to 5.24 years. Overall, 3D PCA provided the best balance, with an age prediction MAE of 4.57 years while maintaining 94.01% sex classification accuracy when altering the age variable and 94.73% sex classification accuracy and the lowest age prediction MAE (3.84 years) when altering the sex variable. Conclusions: 3D PCA appears to be the best-suited DR method for causal neuroimage analysis. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Principal component analysis

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