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Environmental toxicity assessment of chromium (III) oxide nanoparticles using a phytotoxic, cytotoxic, and genotoxic approach

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Emerging Contaminants 2024年第2期10卷 76-82页

作者： Uhram Song Kwon Soon Pyo Ho Hyun Song Sun ryung Lee Jieun Kim Department of Biology Jeju National UniversityJeju63243South Korea Department of Interdisciplinary Program in Biomedical Science Graduate SchoolSoonchunhyang UniversityAsanChungcheongnam-doSouth Korea Department of Laboratory Medicine Soonchunhyang University Seoul HospitalSeoul04401South Korea

Chromium(III)oxide nanoparticles(NPs)have a wide range of industrial applications,particularly in the production of lithium-ion batteries;however,the toxicity of these NPs has not been studied *** phytotoxicity and genotoxicity of chromium(III)oxide NPs have not been assessed,and only one study reported their cytotoxicity,underscoring the need to perform comprehensive toxicity studies on these ***,we showed that chromium(III)oxide NPs had no effect on germination rates,and only decreased root elongation in *** as determined by root elongation and biomass ***(III)oxide NPs had no phytotoxic effects under environmental realistic conditions,whereas they had cytotoxic effects against HK-2 and Raw 264.7 cells in ***,the cytotoxicity of chromium(III)oxide NPs did not occur under environmentally realistic *** genotoxicity of chromium(III)oxide NPs was not significant in HK-2 cells,suggesting that the potential environmental cytotoxicity of chromium(III)oxide NPs is *** study proposes comprehensive phytotoxicity,cytotoxicity,and genotoxicity testing tools that would be easy,safe,and effective for evaluating the toxicity of *** integrated testing system could be an accurate and stable method for evaluating the toxicity of other nanomaterials.

关键词： Nanoparticle Plant growth Cell viability Chromium(III)oxide Nephrotoxicity

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View-Invariant Object Representation in Anterior and Posterior Inferotemporal Cortex: A Machine Learning Approach

SSRN

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SSRN 2025年

作者： Okamura, Jun-Ya Fukano, Daisuke Wang, Gang Information Science and Biomedical Engineering Program Graduate School of Science and Engineering Kagoshima University Kagoshima890-0065 Japan

Inferotemporal (IT) cortex is the final visual area in the ventral stream where object information is processed. Previous electrophysiological studies showed viewing angle tolerance of 30-60° of single IT cells to the objects experienced in discrimination at each of several viewing angles, and to the objects experienced in learning association of different views. IT is divided into anterior (cytoarchitectonic area TE) and posterior (TEO) parts. It was reported that single cells in area TE showed the viewing angle tolerance while those in area TEO did not. In the present study population activities were compared between cell populations in area TE and those in area TEO using machine learning algorithm. An object set consisted of four similar objects created by deforming a prototype object, and four views each separated by 30°. A population vector was created by aligning responses of the cells to each object image. A classifier was trained by support vector machine (SVM) to create a hyperplane that separated one object from the other three objects at the same viewing angles, and then tested by response vectors to the object images at different viewing angles. In area TE, dynamics of the performance evaluated by d’ showed viewing angle tolerance of 30-60° to the objects with prior experience in learning association of different views. In area TEO, populations of the cells did not show the viewing angle tolerance. Significant increase of the d’ values in area TE in the late time period for the objects with prior experience in learning association of different views may suggest view-invariance is more represented in late time period than early time period. These results suggest that viewpoint invariance is expressed more strongly in the TE region, providing new insights into the neural basis of object recognition © 2025, The Authors. All rights reserved.

关键词： Support vector machines

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3D Image Generation from X-ray Projections Using Generative Adversarial Networks 23

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

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23rd IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2023

作者： AlShurbaji, Mohammad Dhou, Salam Biomedical Engineering Graduate Program American University of Sharjah Sharjah United Arab Emirates Department of Computer Science and Engineering American University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798350393118

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. © 2023 IEEE.

关键词： Generative adversarial 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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Pathogen inhibition and indication by gelatin nonwoven mats with incorporation of polyphenol derivatives

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RSC Advances 2023年第45期13卷 31602-31615页

作者： Bai, By Meng-Yi Wang, Ting-Teng Chen, Xin-An Wu, Chia-Chun Graduate Institute of Biomedical Engineering and Biomedical Engineering Program Graduate Institute of Applied Science and Technology National Taiwan University of Science and Technology TR-917 AAEON Building No. 43 Keelung Rd. Sec. 4 Da'an Dist. Taiwan Taipei City10607 Taiwan Adjunct Appointment to the National Defense Medical Center Taiwan Taipei11490 Taiwan Institute of Prevention Medicine National Defense Medical Center Taiwan Taipei11490 Taiwan

There is a need for non-pharmaceutical intervention methods that can prevent and indicate the risk of airborne disease spread. In this study, we developed a nonwoven mat based on the polyphenol gallic acid, which can inhibit pathogens growth and also indicate pathogen levels in the surrounding environment. Using nuclear magnetic resonance, Fourier-transform infrared spectroscopy, and high-performance liquid chromatography, we characterized this novel gelatin-based nonwoven mat and investigated the mechanism governing its ability to indicate pathogen levels. We demonstrated that the incorporation of gallic acid serves a vital role in indicating the presence of bacteria, causing the nonwoven mat to change in color from white to brown. We have proposed a plausible mechanism for this color change behavior based on a reaction of gallic acid with components excreted by bacteria, including glutamate, valine, and leucine. The concentrations of these components reflect the bacterial counts, enabling a real-time indication of pathogen levels in the surrounding air. In summary, the nonwoven mat presented herein can serve as an excellent antibacterial agent and as an indicator of nearby bacteria for fabricating personal protection equipment like filtration mask. © 2023 The Royal Society of Chemistry.

关键词： Bacteria

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A motion-level-aware denoising framework for x-ray fluoroscopic images

A motion-level-aware denoising framework for x-ray fluorosco...

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Medical Imaging 2023: Physics of Medical Imaging

作者： Jo, Geonhui Jang, Minhee Jeon, Sun-Young Kim, Wonjin Choi, Jang-Hwan Department of Computer Science and Engineering Ewha Womans University Seoul03760 Korea Republic of Division of Mechanical and Biomedical Engineering Graduate Program in System Health Science and Engineering Ewha Womans University Seoul03760 Korea Republic of

ISBN: (纸本)9781510660311

Fluoroscopy in a low-dose tube output is used to reduce the damage associated with radiation exposure. However, lowering the radiation dose inevitably increases random noise in x-ray images, resulting in poor diagnostic image quality, which requires noise reduction for accurate diagnosis. Also, in the case of non-static objects, the image is blurred due to motion. The most-used denoiser with a recursive filter (RF) preserves details well when applied to temporal data, but it is vulnerable to motion blur. Existing convolutional neural network (CNN)-based algorithms with single-frame input cannot use the temporary context, and others with multi-frame input are good for motion detection but poor for detail preservation. Therefore, we propose a motion-level-aware denoising framework to combine the results of RF- and CNN-based algorithms depending on the pixel-wise magnitude of motion to complement each other. The data we use are fluoroscopy images taken in continuous time, and we aim at many-to-one so that one frame is denoised by considering sequential frames. Also, since both RF- and CNN-based algorithms used in our architecture are many-to-one methods, they can consider spatiotemporal information. In the multi-frame input, the difference in intensity of each pixel between frames is calculated to obtain a moving map. Depending on the factor value from the moving map, the final image is obtained by reflecting the outputs of the RF- and CNN-based algorithms. If the factor value is high, the pixel intensity of the final image is like the CNN-based output, which is good for motion detection, and vice versa, it more reflects the intensity of RF output, which is excellent in perceptual quality. Therefore, it prevents motion blur and does not over-smooth microdetails, such as bones and muscles. The results show that combining the two outputs together records higher peak signal-to-noise ratio (PSNR) and has better perceptual quality for diagnosis than using only one method. F

关键词： Deep learning

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Overcoming the Blood-Brain Tumor Barrier with Docetaxel-Loaded Mesoporous Silica Nanoparticles for Treatment of Temozolomide-Resistant Glioblastoma

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ACS Applied Materials and Interfaces 2024年第17期16卷 21722-21735页

作者： Hsu, Tsung-I Chen, Yi-Ping Zhang, Rong-Lin Chen, Zih-An Wu, Cheng-Hsun Chang, Wen-Chang Mou, Chung-Yuan Chan, Hardy Wai-Hong Wu, Si-Han Ph.D. Program in Medical Neuroscience College of Medical Science and Technology Taipei Medical University and National Health Research Institutes Taipei110 Taiwan International Master Program in Medical Neuroscience College of Medical Science and Technology Taipei Medical University Taipei110 Taiwan Graduate Institute of Nanomedicine and Medical Engineering Taipei Medical University Taipei110 Taiwan International Ph.D. Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei110 Taiwan Nano Targeting & Therapy Biopharma Inc. Taipei110 Taiwan Graduate Institute of Medical Sciences College of Medicine Taipei Medical University Taipei110 Taiwan Department of Chemistry National Taiwan University Taipei106 Taiwan

While temozolomide (TMZ) has been a cornerstone in the treatment of newly diagnosed glioblastoma (GBM), a significant challenge has been the emergence of resistance to TMZ, which compromises its clinical benefits. Additionally, the nonspecificity of TMZ can lead to detrimental side effects. Although TMZ is capable of penetrating the blood-brain barrier (BBB), our research addresses the need for targeted therapy to circumvent resistance mechanisms and reduce off-target effects. This study introduces the use of PEGylated mesoporous silica nanoparticles (MSN) with octyl group modifications (C8-MSN) as a nanocarrier system for the delivery of docetaxel (DTX), providing a novel approach for treating TMZ-resistant GBM. Our findings reveal that C8-MSN is biocompatible in vitro, and DTX@C8-MSN shows no hemolytic activity at therapeutic concentrations, maintaining efficacy against GBM cells. Crucially, in vivo imaging demonstrates preferential accumulation of C8-MSN within the tumor region, suggesting enhanced permeability across the blood-brain tumor barrier (BBTB). When administered to orthotopic glioma mouse models, DTX@C8-MSN notably prolongs survival by over 50%, significantly reduces tumor volume, and decreases side effects compared to free DTX, indicating a targeted and effective approach to treatment. The apoptotic pathways activated by DTX@C8-MSN, evidenced by the increased levels of cleaved caspase-3 and PARP, point to a potent therapeutic mechanism. Collectively, the results advocate DTX@C8-MSN as a promising candidate for targeted therapy in TMZ-resistant GBM, optimizing drug delivery and bioavailability to overcome current therapeutic limitations. © 2024 The Authors. Published by American Chemical Society.

关键词： Tumors

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SRECG: ECG Signal Super-Resolution Framework for Portable/Wearable Devices in Cardiac Arrhythmias Classification

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IEEE Transactions on Consumer Electronics 2023年第3期69卷 250-260页

作者： Chen, Tsai-Min Tsai, Yuan-Hong Tseng, Huan-Hsin Liu, Kai-Chun Chen, Jhih-Yu Huang, Chih-Han Li, Guo-Yuan Shen, Chun-Yen Tsao, Yu National Taiwan University and Academia Sinica Graduate Program of Data Science Taipei106319 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei115 Taiwan Taiwan Artificial Intelligence Academy Foundation Program of Taiwan AI Academy New Taipei22065 Taiwan Artificial Intelligence Foundation Technology Development Center New Taipei24158 Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei10617 Taiwan Institute of Biomedical Sciences Academia Sinica Taipei115 Taiwan National Taiwan University Department of Mathematics Taipei10617 Taiwan

A combination of cloud-based deep learning (DL) algorithms with portable/wearable (P/W) devices has been developed as a smart heath care system to support automatic cardiac arrhythmias (CAs) classification using electrocardiography (ECG). However, long-term and continuous ECG monitoring is challenging because of limitations of batteries and transmission bandwidth of P/W devices while incorporated with consumer electronics (CE). A feasible approach to address this challenge is to decrease sampling rates. However, low sampling rates lead to low-resolution signals that hinder the CAs classification performance. In this study, we propose a DL-based ECG signal super-resolution framework (called SRECG) to enhance low-resolution ECG signals by jointly considering the accuracies when applied to the DL-based high-resolution multiclass classifier (HMC) of CAs. In our experiments, we downsampled the ECG signals from the CPSC2018 dataset and evaluated their HMC accuracies with and without the SRECG. Experimental results show that SRECG can well improve the HMC accuracies as compared to traditional interpolation methods. Moreover, approximately half of the CAs classification accuracies of HMC were maintained within the enhanced ECG signals by SRECG. The promising results confirm that SRECG can be suitably used to enhance low-resolution ECG signals from P/W devices with CE to improve their cloud-based HMC performances. © 1975-2011 IEEE.

关键词： Electrocardiography

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Measurement of peripheral nerve magnetostimulation thresholds of a head solenoid coil between 200 Hz and 88.1 kHz

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IEEE Journal of Translational Engineering in Health and Medicine 2025年 13卷 275-285页

作者： Barksdale, Alex C. Ferris, Natalie G. Mattingly, Eli Sliwiak, Monika Guerin, Bastien Wald, Lawrence L. Davids, Mathias Klein, Valerie MIT Department of Electrical Engineering and Computer Science CambridgeMA United States Martinos Center for Biomedical Imaging CharlestownMA United States Harvard University Harvard Graduate Program in Biophysics CambridgeMA United States Harvard-MIT Division of Health Sciences and Technology BostonMA United States Harvard Medical School BostonMA United States

Objective: Magnetic fields switching at kilohertz frequencies induce electric fields in the body, which can cause peripheral nerve stimulation (PNS). Although magnetostimulation has been extensively studied below 10 kHz, the behavior of PNS at higher frequencies remains poorly understood. This study aims to investigate PNS thresholds at frequencies up to 88.1 kHz and to explore deviations from the widely accepted hyperbolic strength-duration curve (SDC). Methods and procedures: PNS thresholds were measured in the head of 8 human volunteers using a solenoidal coil at 16 distinct frequencies, ranging from 200 Hz to 88.1 kHz. A hyperbolic SDC was used as a reference to compare the frequency-dependent behavior of PNS thresholds. Results: Contrary to the predictions of the hyperbolic SDC, PNS thresholds did not decrease monotonically with frequency. Instead, thresholds reached a minimum near 25 kHz, after which they increased by an average of 39% from 25 kHz to 88.1 kHz across subjects. This pattern indicates a significant deviation from previously observed behavior at lower frequencies. Conclusion: Our results suggest that PNS thresholds exhibit a non-monotonic frequency dependence at higher frequencies, diverging from the traditional hyperbolic SDC. These findings offer critical data for refining neurodynamic models and provide insights for setting PNS safety limits in applications like MRI gradient coils and magnetic particle imaging (MPI). Further investigation is needed to understand the biological mechanisms driving these deviations beyond 25 kHz. Clinical impact: These findings call for further basic research into biological mechanisms underlying high frequency PNS threshold trends, and supports refinement of safety guidelines for MRI and MPI systems for clinical implementation. © 2013 IEEE.

关键词： Neuroimaging

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Towards a Healthy AI Tradition: Lessons from Biology and biomedical science

arXiv

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arXiv 2024年

作者： Kasif, Simon Department of Biomedical Engineering Bioinformatics Graduate Program Department of Computer Science Boston University United States Department of Computer Science Tel Aviv University

AI is a magnificent field that directly and profoundly touches on numerous disciplines ranging from philosophy, computer science, engineering, mathematics, decision and data science and economics, to cognitive science, neuroscience and more. The number of applications and impact of AI is second to none and the potential of AI to broadly impact future science developments is particularly thrilling. While attempts to understand knowledge, reasoning, cognition and learning go back centuries, AI remains a relatively new field. In part due to the fact it has so many wide-ranging overlaps with other disparate fields it appears to have trouble developing a robust identity and culture. Here we suggest that contrasting the fast-moving AI culture to biological and biomedical sciences is both insightful and useful way to inaugurate a healthy tradition needed to envision and manage our ascent to AGI and beyond (independent of the AI Platforms used). After all, the human brain is a biological organ produced by evolution and human intelligence is a remarkable bi-product of nature and nurture and their complex interaction. In this perspective, we focus on traditions and culture, namely the commonly observed practices of evaluating, recognizing applauding, critiquing, debating and managing all progress including useful advances and discovery of challenging limitations. We are not discussing specific scientific exchanges between AI and Biology that include interdisciplinary cross fertilization of scientific methods, technology, ideas and applications that have been amply demonstrated and will continue to be transformative in the future. In a previous perspective, we suggested that biomedical laboratories or centers can usefully embrace logistic traditions in AI labs that will allow them to be highly collaborative, improve the reproducibility of research, reduce risk aversion and produce faster mentorship pathways for PhDs and fellows. This perspective focuses on the benefits of AI a

关键词： Biological organs

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