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ZnSe/NiO heterostructure-based chemiresistive-type sensors for low-concentration NO_(2) detection

Rare Metals 2021年第6期40卷 1632-1641页

作者： Wei Liu Ding Gu Jian-Wei Zhang Xiao-Gan Li Marina N.Rumyantseva Alexander M.Gaskov School of Microelectronics Key Laboratory of Liaoning for Integrated Circuits TechnologyDalian University of TechnologyDalian 116024China Liaoning Key Laboratory of Integrated Circuit and Biomedical Electronic System Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalian 116024China Laboratory of Chemistry and Physics of Semiconductor and Sensor Materials Chemistry DepartmentMoscow State UniversityMoscow 199991Russia

Novel ZnSe/NiO heterostructure nanocomposites were successfully prepared by one-step hydrothermal *** ZnSe/NiO-based sensor exhibits a response of~96.47% to 8×10^(-6) NO_(2) at 140℃,which is significantly higher than those of intrinsic ZnSe-based(no response)and NiO-based(~19.65%)*** theoretical detection limit(LOD)of the sensor is calculated to be 8.91×10^(-9),indicating that the sensor can be applied to detect the ultralow concentrations of NO_(2).The effect of NiO content on the gas-sensing performance of the nanocomposites was investigated in *** optimal NiO content in the nanocomposite is determined to be15.16%to achieve the highest *** as-fabricated sensor also presents an excellent selectivity to several possible interferents such as methanol,ethanol,acetone,benzene,ammonia and *** enhanced sensing performance can be attributed to the formation of p-p heterostructures between ZnSe and NiO,which induces the charge transfer across the interfaces and yields more active sites.

关键词： ZnSe/NiO heterostructure NO_(2)detection Gas sensors Charge transfer

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Exploiting marine phospholipid nanoliposomes for enhanced apremilast delivery and therapeutic efficacy in inflammatory bowel disease

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Colloids and Surfaces A: Physicochemical and Engineering Aspects 2025年 719卷

作者： Shabana, Samah Hamouda, Hamed I. Yin, Heng Hamouda, Samia Ahmed, Busati El-Sayed, Mohamed H. Chi, Zhe Liu, Chenguang School of Biomedical Engineering Faculty of Medicine Liaoning Key Lab of Integrated Circuit and Biomedical Electronic System Dalian University of Technology Dalian116024 China Department of Biochemistry and Molecular Biology College of Marine Life Sciences Ocean University of China Qingdao266003 China Egyptian Ministry of Health and Population Cairo11516 Egypt Dalian Engineering Research Center for Carbohydrate Agricultural Preparations Dalian Technology Innovation Center for Green Agriculture Liaoning Provincial Key Laboratory of Carbohydrates Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian116023 China Nasr City Cairo11727 Egypt College of Food Science and Engineering Ocean University of China Qingdao266003 China Anesthesiology and ICU Department Faculty of Medicine for Girls Al-Azhar University Cairo Egypt Department of Biology College of Sciences and Arts-Rafha Northern Border University Arar Saudi Arabia

Marine phospholipid (MPL) nanoliposomes represent a new frontier in drug delivery systems, offering unique bio-functional and physicochemical properties. This study developed orally administered MPL nanoliposomes to enhance apremilast delivery and bioavailability in inflamed bowel disease (IBD). Drug release studies showed effective gastric protection (21.2 ± 0.4 % release at pH 1.2 after 2 h) and substantial release (96.4 ± 0.3 %) at pH 6.8 over 12 h. The optimized formulation (4 % drug w/w) showed remarkable stability over 6 weeks of storage at 4 °C. Apr/MPL nanoliposomes up to 300 µg/mL showed great cytocompatibility to RAW 264.7 macrophages and Caco2 intestinal cells. Furthermore, the formulation exhibited superior anti-inflammatory effects, significantly reducing nitric oxide (NO) production and downregulating pro-inflammatory cytokines (TNF-α, IL-1β, IL-6) in lipopolysaccharide (LPS)-stimulated RAW 264.7 macrophages. In vivo, Apr/MPL nanoliposomes improved recovery in a dextran sulfate sodium (DSS)-induced ulcerative colitis model, outperforming free apremilast. These findings underscore MPL nanoliposomes as a promising pharmacological tool for managing inflammation and a versatile drug delivery system for IBD. © 2025

关键词： Macrophages

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Marine phospholipid nanoliposomes mediate oral colonic delivery of 5-aminosalicylic acid and mitigate the severity of DSS-induced colitis in mouse models

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Journal of Drug Delivery Science and Technology 2025年 108卷

作者： Shabana, Samah Hamouda, Hamed I. Noureldin, Suzan Chi, Zhe Liu, Chenguang School of Biomedical Engineering Faculty of Medicine Liaoning Key Lab of Integrated Circuit and Biomedical Electronic System Dalian University of Technology Dalian 116024 China Department of Biochemistry and Molecular Biology College of Marine Life Sciences Ocean University of China Qingdao 266003 China Egyptian Ministry of Health and Population Cairo 11516 Egypt Dalian Engineering Research Center for Carbohydrate Agricultural Preparations Liaoning Provincial Key Laboratory of Carbohydrates Dalian Institute of Chemical Physics Chinese Academy of Sciences CAS Dalian 116023 China Processes Development Department Egyptian Petroleum Research Institute (EPRI) Cairo Nasr City 11727 Egypt Qingdao Key Laboratory of Food Biotechnology College of Food Science and Engineering Ocean University of China Qingdao China Animal Science Department Jiangsu University of Science and Technology Jiangsu China

5-Aminosalicylic acid (5-ASA) is the preferred medication for treating mild to moderate inflammatory bowel disease (IBD). However, side effects and sometimes insufficient efficacy pose challenges. This study introduces chitosan-coated orally targeted marine phospholipid nanoliposomes grafted with Vit. E. TPGS for delivering 5-aminosalicylic acid to the inflamed mucosa in IBD treatment. MTL nanoliposomes were prepared by the thin film hydration method, exhibiting sizes of 165.8 ± 2.19 nm pre-coating and 259.5 ± 1.77 nm size post-coating. The nanoliposomes showed controlled drug release at intestinal pH, with minimal release in the simulated gastric medium. They demonstrated excellent stability over a 45-day storage period. In vitro, cytocompatibility studies with L929 cells and ex vivo studies with RBCs showed excellent biocompatibility. MTL nanoliposomes showed selective deposition in inflamed RAW 264.7 macrophages and time-dependent uptake in Caco2 intestinal cells. In vitro, anti-inflammatory experiments significantly reduced inflammatory biomarkers like TNF-α, IL1-β, IL-6, and ROS. In vivo, DSS-induced colitis studies demonstrated improved colitis indices, colonic tissue change restoration, and myeloperoxidase activity inhibition. These findings underscore the potential of MTL nanoliposomes as a promising targeted delivery system for 5-aminosalicylic acid in treating IBD. © 2025

关键词： 5-Aminosalicylic acid Chitosan Inflammatory bowel disease Marine phospholipids Vit. E. TPGS

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Application and Implementation of Convolutional Neural Network Accelerator Based on FPGA in Environmental Sound Classification 8

Application and Implementation of Convolutional Neural Netwo...

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8th International Conference on Computer and Communication systems, ICCCS 2023

作者： Zhang, Jianwei Yan, Haonan School of Biomedical Engineering School of Artificial Intelligence Dalian University of Technology Liaoning Key Lab of Integrated Circuit and Biomedical Electronic System Dalian China School of Biomedical Engineering Liaoning Key Lab of Integrated Circuit and Biomedical Electronic System Dalian University of Technology Dalian China

ISBN: (纸本)9781665456128

With the development of artificial intelligence, convolutional neural network (CNN) has achieved great success in various learning tasks, and has been applied to environmental sound classification tasks, achieving a high classification accuracy. With the development of convolutional neural network in the application of environmental sound classification, the design of hardware accelerator for convolutional neural network has become a hot topic. Compared with CPU, GPU and ASIC, FPGA has the unique advantages of low power consumption and high energy efficiency in accelerating deep learning, and has become the preferred hardware platform for neural network hardware implementation. In this paper, a convolution neural network accelerator based on FPGA is proposed, which can realize convolution, tanh nonlinear activation, average pooling and other operations. The hardware accelerator can realize the inference process of environmental sound classification, and achieve high energy efficiency and low resource consumption while having a high classification accuracy. The network was tested on Xilinx Virtex VC-707 FPGA board. The experimental results show that its energy efficiency is 547.60 and 377.36 times higher than that of CPU and GPU respectively. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Gray and White Matters Segmentation in Brain CT Images Using Multi-task Learning from Paired CT and MR Images

Gray and White Matters Segmentation in Brain CT Images Using...

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International Workshop on New Approaches for Multidimensional Signal Processing, NAMSP 2023

作者： Han, Taohai Wang, Hongkai School of Biomedical Engineering Faculty of Medicine Dalian University of Technology Liaoning Dalian China Liaoning Key Laboratory of Integrated Circuit and Biomedical Electronic System Dalian University of Technology Liaoning Dalian China

ISBN: (纸本)9789819727438

Computer tomography (CT) has been routinely used for decades in clinical neuroimaging. Compared to Magnetic resonance imaging (MR), CT is more readily available and more cost-effective, but the soft-tissue contrast is much lower. In brain CT images, the unclear soft-tissue boundaries and the high noise level hamper accurate segmentation of the gray and white matter, leaving obstacles for the subsequent geometrical quantification of brain structures. To address this challenge, this paper specifically acquires same-patient CT and MR image pairs and proposes a multi-task learning model for simultaneous tissue segmentation and modality transfer. We aim to use the modality transfer task to learn corresponding MR and CT features which assists the segmentation task to achieve more accurate results than single modality learning. Moreover, we add a Shannon entropy loss function to the training loss to further combat the high noise influence and reduce the fragmentation problem of the segmentation results. Experimental results proved that our multi-task framework achieves more accurate segmentation than the single segmentation task, and the Shannon entropy loss results in much fewer broken brain regions than the state-of-the-art (SOTA) U-net method. Our study provides a useful tool for clinical neural CT image analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Brain

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Distributed data-privacy preserving federated learning method for sleep stage classification

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biomedical Signal Processing and Control 2025年 109卷

作者： Hongjin Li Xiaohui Zhao Hang Liu Ziyi Wang Yukai Cai Chuanshuai Yang Fengyu Cong School of Biomedical Engineering Faculty of Medicine Dalian University of Technology Dalian China Liaoning Key Laboratory of Integrated Circuit and Biomedical Electronic System Dalian University of Technology Dalian China Department of Respiratory and Critical Care Medicine The Affiliated Central Hospital of Dalian University of Technology Dalian China Faculty of Information Technology University of Jyväskylä Jyväskylä Finland

Artificial intelligence and big data have great promise for promoting sleep medicine assistance. However, obstacles such as data privacy restrictions occur when enough data are collected from different data centers to develop classification models with better performance. Federated learning (FL), a collaborative machine learning approach, aims to address problems by acquiring models rather than directly accessing data from multiple clients. In this work, we utilize federated learning to increase model performance and preserve data privacy when deploying automatic sleep staging methods with insufficient data. And we construct a CNN-Transformer model and design an anti-class ratio (ACR) weighted loss function to complete the classification task with class imbalanced data. We evaluate our model and ACR using Sleep-EDF database and achieve better overall and N1 classification performance. To clearly show the efficiency of FL, we set three different data volume experiments with three independent heterogeneous public sleep databases. The experimental results are compared with two benchmark methods, traditional local learning and data-centralized learning. The results suggest that federated learning method outperforms the local learning approach in classification performance and achieves comparable results with data-centralized learning. As a result, we argue that federated learning is a promising alternative for multiple clients developing sleep staging algorithms especially with insufficient data because it adds benefits in terms of model performance and data privacy protection.

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An inexact alternating proximal gradient algorithm for nonnegative CP tensor decomposition

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Science China(Technological Sciences) 2021年第9期64卷 1893-1906页

作者： WANG DeQing CONG FengYu School of Biomedical Engineering Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalian 116024China Faculty of Information Technology University of JyväskyläJyväskylä40100Finland School of Artificial Intelligence Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalian 116024China Key Laboratory of Integrated Circuit and Biomedical Electronic System Liaoning ProvinceDalian University of TechnologyDalian 116024China

Nonnegative tensor decomposition has become increasingly important for multiway data analysis in recent years. The alternating proximal gradient(APG) is a popular optimization method for nonnegative tensor decomposition in the block coordinate descent framework. In this study, we propose an inexact version of the APG algorithm for nonnegative CANDECOMP/PARAFAC decomposition, wherein each factor matrix is updated by only finite inner iterations. We also propose a parameter warm-start method that can avoid the frequent parameter resetting of conventional APG methods and improve convergence *** experimental tests, we find that when the number of inner iterations is limited to around 10 to 20, the convergence speed is accelerated significantly without losing its low relative error. We evaluate our method on both synthetic and real-world *** results demonstrate that the proposed inexact APG algorithm exhibits outstanding performance on both convergence speed and computational precision compared with existing popular algorithms.

关键词： tensor decomposition nonnegative CANDECOMP/PARAFAC block coordinate descent alternating proximal gradient inexact scheme

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Facial Photo-Guided Head Anatomy Modeling Based on Deep Learning and 2D/3D Shape Prior Model Registration 2nd

Facial Photo-Guided Head Anatomy Modeling Based on Deep Lear...

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2nd World Conference on Intelligent and 3D Technologies, WCI3DT 2023

作者： Wang, Meng Wang, Hongkai Faculty of Medicine School of Biomedical Engineering Dalian University of Technology Dalian116024 China Liaoning Key Laboratory of Integrated Circuit and Biomedical Electronic System Dalian University of Technology Dalian116024 China

ISBN: (纸本)9789819970100

Three-dimensional (3D) models of individualized head anatomy are often used in the preoperative planning of plastic surgery. Most current methods use Computed Tomography (CT) to acquire the head anatomy, but CT scan exposes the patients to X-ray radiation. Alternatively, artificial intelligence-based estimation of 3D head anatomy from the facial photo is a safer and more efficient choice, especially for scenarios that do not require high modeling accuracy of internal structures (e.g., doctor–patient communication and surgery procedure demonstration). We develop a method for constructing a personalized 3D model of complete head anatomy (including skin and internal structures) from the front and side view photos. We first detect key facial landmarks from the photos with a convolution neural network (CNN), then perform 2D/3D registration to morph a statistical shape model (SSM) of complete head anatomy to match the dual-view photos. Our method uses deep learning to achieve accurate facial landmark detection and employ the anatomy shape prior to yielding reasonable internal structure estimation. We evaluated the method based on 12 subjects (including 7 males and 5 females). The facial surface reconstruction error was assessed using the 3D surface scan of the subjects as the ground truth. Our method’s root mean square error (RMSE) was 3.60 ± 0.49 mm, which was 1.06 ± 0.39 mm lower than the state-of-the-art (SOTA) CNN-based face reconstruction method. Our method also predicts all the internal head structures (bones, muscles, vessels, nerve fibers, fat, glands, and brain structures), which are not provided by the SOTA method. To the best of our knowledge, this is the first study modeling complete head anatomy from facial photos toward clinical applications. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Surgery

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A High Energy Efficiency and Low Resource Consumption FPGA Accelerator for Convolutional Neural Network 7

A High Energy Efficiency and Low Resource Consumption FPGA A...

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7th International Conference on Computer and Communications, ICCC 2021

作者： Zhang, Jianwei Cai, Houjia Li, Jinghu Dalian University of Technology Liaoning Key Lab of Integrated Circuit and Biomedical Electronic System School of Biomedical Engineering Dalian China Fujian Agriculture and Forestry University College of Computer and Information Sciences Fuzhou China

ISBN: (纸本)9781665409506

With the rapid development of convolutional neural networks (CNN), the design of hardware accelerators for CNN calculations has become a major focus of current research. However, power consumption and resource consumption are two main limits for the application of CNN hardware accelerators. In response to these problems, a FPGA-based implementation of a CNN hardware accelerator with high energy efficiency and low resource consumption is proposed. The implementation includes zero padding, convolution, batch normalization, ReLU6, and MaxPooling. The computing performance of the network is improved by using pipeline design and layer fusion. The LeNet-5 network is to test the implementation on the Xilinx Virtex VC-707 FPGA board. The experimental result shows that when inferring the MNIST test set, the energy efficiency is 843.91 times and 228.35 times that of the CPU and GPU, respectively. © 2021 IEEE.

关键词： Field programmable gate arrays (FPGA)

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A TD-Learning Based Bionic Cerebellar Model Controller For Humanoid Robots

A TD-Learning Based Bionic Cerebellar Model Controller For H...

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2022 International Symposium on Biomimetic Intelligence and Robotics, ISBIR 2022

作者： Li, Honghui Liu, Rong Wang, Yongxuan Liu, Yin Chen, Yaru Wang, Jiaxing Gu, Jason Liaoning Key Lab of Integrated Circuit and Biomedical Electronic System School of Biomedical Engineering Dalian University of Technology Liaoning Dalian116024 China School of Information Engineering Dalian University Liaoning Dalian116024 China Department of Electrica Dalhousie University Halifax Canada

The cerebellum is a crucial component of the human body that plays a vital role in human walking. To design a robot gait controller by referring the working mechanism of the cerebellum is one of the hotspots in the bionic control field. This paper designs a bionic cerebellar motion control model to control the slope gait of a humanoid robot. The cerebellum model refers to the connection method between neurons in a human cerebellum, and expresses from a bionic perspective how the neurons in the cerebellum process external information and generate control commands during walking. Inspired by how human walking is learned, this model employs reinforcement learning in the learning process of the bionic cerebellar model. A corresponding simulation environment is also designed to train and test the cerebellar control model's effectiveness when regulating a robot's slope walking stability. The simulation experimental results demonstrate that the cerebellum model can achieve stable control of the walking motion of the humanoid robot after training, verifying its effectiveness, and laying a foundation for further realization of human-like artificial intelligence. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Proceedings of International Symposium on Biomimetic Intelligence and Robotics.

关键词： Anthropomorphic robots

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