IEEE Transactions on NanoBioscience最新文献_第2页

A2HTL: An Automated Hybrid Transformer Based Learning for Predicting Survival of Esophageal Cancer Using CT Images. A2HTL：利用CT图像预测食管癌存活率的基于混合变压器的自动学习方法

IF 3.7 4区生物学

IEEE Transactions on NanoBioscience Pub Date : 2024-08-12 DOI: 10.1109/TNB.2024.3441533

Hailin Yue, Jin Liu, Lina Zhao, Hulin Kuang, Jianhong Cheng, Junjian Li, Mengshen He, Jie Gong, Jianxin Wang

{"title":"A2HTL: An Automated Hybrid Transformer Based Learning for Predicting Survival of Esophageal Cancer Using CT Images.","authors":"Hailin Yue, Jin Liu, Lina Zhao, Hulin Kuang, Jianhong Cheng, Junjian Li, Mengshen He, Jie Gong, Jianxin Wang","doi":"10.1109/TNB.2024.3441533","DOIUrl":"10.1109/TNB.2024.3441533","url":null,"abstract":"Esophageal cancer is a common malignant tumor, precisely predicting survival of esophageal cancer is crucial for personalized treatment. However, current region of interest (ROI) based methodologies not only necessitate prior medical knowledge for tumor delineation, but may also cause the model to be overly sensitive to ROI. To address these challenges, we develop an automated Hybrid Transformer based learning that integrates a Hybrid Transformer size-aware U-Net with a ranked survival prediction network to enable automatic survival prediction for esophageal cancer. Specifically, we first incorporate the Transformer with shifted windowing multi-head self-attention mechanism (SW-MSA) into the base of the UNet encoder to capture the long-range dependency in CT images. Furthermore, to alleviate the imbalance between the ROI and the background in CT images, we devise a size-aware coefficient for the segmentation loss. Finally, we also design a ranked pair sorting loss to more comprehensively capture the ranked information inherent in CT images. We evaluate our proposed method on a dataset comprising 759 samples with esophageal cancer. Experimental results demonstrate the superior performance of our proposed method in survival prediction, even without ROI ground truth.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141971021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Influence of Red Blood Cells on Channel Characterization in Cylindrical Vasculature. 红细胞对圆柱形血管中通道特性的影响

IF 3.7 4区生物学

IEEE Transactions on NanoBioscience Pub Date : 2024-08-07 DOI: 10.1109/TNB.2024.3436022

Kathan S Joshi, Dhaval K Patel, Shivam Thakker, Miguel Lopez-Benitez, Janne J Lehtomaki

引用次数: 0

Deep Learning for the Accurate Prediction of Triggered Drug Delivery. 深度学习用于触发式给药的精确预测

IF 3.7 4区生物学

IEEE Transactions on NanoBioscience Pub Date : 2024-07-17 DOI: 10.1109/TNB.2024.3426291

Ghaleb A Husseini, Rana Sabouni, Vladimir Puzyrev, Mehdi Ghommem

{"title":"Deep Learning for the Accurate Prediction of Triggered Drug Delivery.","authors":"Ghaleb A Husseini, Rana Sabouni, Vladimir Puzyrev, Mehdi Ghommem","doi":"10.1109/TNB.2024.3426291","DOIUrl":"https://doi.org/10.1109/TNB.2024.3426291","url":null,"abstract":"The need to mitigate the adverse effects of chemotherapy has driven the exploration of innovative drug delivery approaches. One emerging trend in cancer treatment is the utilization of Drug Delivery Systems (DDSs), facilitated by nanotechnology. Nanoparticles, ranging from 1 nm to 1000 nm, act as carriers for chemotherapeutic agents, enabling precise drug delivery. The triggered release of these agents is vital for advancing this novel drug delivery system. Our research investigated this multifaceted delivery capability using liposomes and metal organic frameworks as nanocarriers and utilizing all three targeting techniques: passive, active, and triggered. Liposomes are modified using targeting ligands to render them more targeted toward certain cancers. Moieties are conjugated to the surfaces of these nanocarriers to allow for their binding to receptors overexpressed on cancer cells, thus increasing the accumulation of the agent at the tumor site. A novel class of nanocarriers, namely metal organic frameworks, has emerged, showing promise in cancer treatment. Triggering techniques (both intrinsic and extrinsic) can be used to release therapeutic agents from nanoparticles, thus enhancing the efficacy of drug delivery. In this study, we develop a predictive model combining experimental measurements with deep learning techniques. The model accurately predicts drug release from liposomes and MOFs under various conditions, including low- and high-frequency ultrasound (extrinsic triggering), microwave exposure (extrinsic triggering), ultraviolet light exposure (extrinsic triggering), and different pH levels (intrinsic triggering). The deep learning-based predictions significantly outperform linear predictions, proving the utility of advanced computational methods in drug delivery. Our findings demonstrate the potential of these nanocarriers and highlight the efficacy of deep learning models in predicting drug release behavior, paving the way for enhanced cancer treatment strategies.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141633393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-Risk Sequence Prediction Model in DNA Storage: The LQSF Method. DNA 储存中的高风险序列预测模型：LQSF 方法

IF 3.7 4区生物学

IEEE Transactions on NanoBioscience Pub Date : 2024-07-08 DOI: 10.1109/TNB.2024.3424576

Yitong Ma, Shuai Chen, Xu Qi, Zuhong Lu, Kun Bi

{"title":"High-Risk Sequence Prediction Model in DNA Storage: The LQSF Method.","authors":"Yitong Ma, Shuai Chen, Xu Qi, Zuhong Lu, Kun Bi","doi":"10.1109/TNB.2024.3424576","DOIUrl":"https://doi.org/10.1109/TNB.2024.3424576","url":null,"abstract":"Traditional DNA storage technologies rely on passive filtering methods for error correction during synthesis and sequencing, which result in redundancy and inadequate error correction. Addressing this, the Low Quality Sequence Filter (LQSF) was introduced, an innovative method employing deep learning models to predict high-risk sequences. The LQSF approach leverages a classification model trained on error-prone sequences, enabling efficient pre-sequencing filtration of low-quality sequences and reducing time and resources in subsequent stages. Analysis has demonstrated a clear distinction between high and low-quality sequences, confirming the efficacy of the LQSF method. Extensive training and testing were conducted across various neural networks and test sets. The results showed all models achieving an AUC value above 0.91 on ROC curves and over 0.95 on PR curves across different datasets. Notably, models such as Alexnet, VGG16, and VGG19 achieved a perfect AUC of 1.0 on the Original dataset, highlighting their precision in classification. Further validation using Illumina sequencing data substantiated a strong correlation between model scores and sequence error-proneness, emphasizing the model's applicability. The LQSF method marks a significant advancement in DNA storage technology, introducing active sequence filtering at the encoding stage. This pioneering approach holds substantial promise for future DNA storage research and applications.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141558613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

3D Printed Interdigitated Electrodes for Cardiac Biomarker Detection. 用于心脏生物标记检测的三维打印交织电极

IF 3.7 4区生物学

IEEE Transactions on NanoBioscience Pub Date : 2024-07-04 DOI: 10.1109/TNB.2024.3423020

Parvathy Nair, Khairunnisa Amreen, R N Ponnalagu, Sanket Goel

{"title":"3D Printed Interdigitated Electrodes for Cardiac Biomarker Detection.","authors":"Parvathy Nair, Khairunnisa Amreen, R N Ponnalagu, Sanket Goel","doi":"10.1109/TNB.2024.3423020","DOIUrl":"https://doi.org/10.1109/TNB.2024.3423020","url":null,"abstract":"The identification of biomarkers has significant benefits for early disease diagnosis and treatment. Hence, there is an increasing demand for low-cost, disposable point-of-care diagnostic devices for rapid and specific biomarker detection, with good sensitivity and range. Interdigitated electrodes (IDEs) are among the most widely used transducers, especially in chemical and biological sensors, because of their high sensitivity, low cost, and straightforward manufacturing procedure. In this work, a simple 3D printed IDE structure has been developed for cardiac troponin I detection to indicate the risk of acute myocardial infarction (AMI). IDEs have been fabricated using 3D printing technique and the electrically conductive composite polylactic acid (PLA) filament being utilized for the fabrication of electrodes. The demonstrated cardiac troponin I sensor has a calculated quantification limit and detection limit of 0.233 ng ml-1 and 76.97 pg ml-1, respectively which are clinically significant ranges for AMI identification. Electrochemical analytical techniques, such as electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV), were carried out for the detection of analyte concentration. Furthermore, using this fabrication methodology IDEs can be fabricated for under US$ 0.4 which can be utilized to detect several other biomarkers.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141534287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Thermal Study of Terahertz Induced Protein Interactions. 太赫兹诱导蛋白质相互作用的热学研究

IF 3.7 4区生物学

IEEE Transactions on NanoBioscience Pub Date : 2024-07-02 DOI: 10.1109/TNB.2024.3422280

Hadeel Elayan, Samar Elmaadawy, Andrew W Eckford, Raviraj Adve, Josep Jornet

{"title":"A Thermal Study of Terahertz Induced Protein Interactions.","authors":"Hadeel Elayan, Samar Elmaadawy, Andrew W Eckford, Raviraj Adve, Josep Jornet","doi":"10.1109/TNB.2024.3422280","DOIUrl":"https://doi.org/10.1109/TNB.2024.3422280","url":null,"abstract":"Proteins can be regarded as thermal nanosensors in an intra-body network. Upon being stimulated by Terahertz (THz) frequencies that match their vibrational modes, protein molecules experience resonant absorption and dissipate their energy as heat, undergoing a thermal process. This paper aims to analyze the effect of THz signaling on the protein heat dissipation mechanism. We therefore deploy a mathematical framework based on the heat diffusion model to characterize how proteins absorb THz-electromagnetic (EM) energy from the stimulating EM fields and subsequently release this energy as heat to their immediate surroundings. We also conduct a parametric study to explain the impact of the signal power, pulse duration, and inter-particle distance on the protein thermal analysis. In addition, we demonstrate the relationship between the change in temperature and the opening probability of thermally-gated ion channels. Our results indicate that a controlled temperature change can be achieved in an intra-body environment by exciting protein particles at their resonant frequencies. We further verify our results numerically using COMSOL Multiphysics® and introduce an experimental framework that assesses the effects of THz radiation on protein particles. We conclude that under controlled heating, protein molecules can serve as hotspots that impact thermally-gated ion channels. Through the presented work, we infer that the heating process can be engineered on different time and length scales by controlling the THz-EM signal input.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141491740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Transactions on NanoBioscience Information for Authors 电气和电子工程师学会《纳米生物科学学报》为作者提供的信息

IF 3.7 4区生物学

IEEE Transactions on NanoBioscience Pub Date : 2024-07-01 DOI: 10.1109/TNB.2024.3415195

引用次数: 0

IEEE Transactions on NanoBioscience Publication Information 电气和电子工程师学会《纳米生物科学论文集》出版信息

IF 3.7 4区生物学

IEEE Transactions on NanoBioscience Pub Date : 2024-07-01 DOI: 10.1109/TNB.2024.3415191

引用次数: 0

Design and Probing of Prism-Based SPR Nano-Biosensor for Human Sperm Detection. 用于人类精子检测的棱镜式 SPR 纳米生物传感器的设计与探测

IF 3.7 4区生物学

IEEE Transactions on NanoBioscience Pub Date : 2024-06-26 DOI: 10.1109/TNB.2024.3419571

Yesudasu Vasimalla, Baljinder Kaur, Suman Maloji, Santosh Kumar

{"title":"Design and Probing of Prism-Based SPR Nano-Biosensor for Human Sperm Detection.","authors":"Yesudasu Vasimalla, Baljinder Kaur, Suman Maloji, Santosh Kumar","doi":"10.1109/TNB.2024.3419571","DOIUrl":"https://doi.org/10.1109/TNB.2024.3419571","url":null,"abstract":"Human sperm functioning is crucial for maintaining natural reproduction, but its sterility is enhanced by variations in environmental conditions. Because of these agitating properties, powerful computer-aided devices are required, but their precision is inadequate, particularly when it comes to samples with low sperm concentrations. Therefore, for the first time, this article introduces the sulfide material-based structure for the detection of human sperm samples using the prism-based surface plasmon resonance sensor (SPR) Nano-biosensor. The proposed structure is designed on the basis of a prism-based Kretschmann configuration and includes silver, silicon, a sulfide layer, black phosphorus, and a sensing medium. This work takes advantage of the excitement of surface plasmons and evanescent waves in the metal dielectric region. For the detection process, seven sperm samples are taken, with their concentration, mobility, and refractive index measured by the refractometer. The proposed structure provides a maximum sensitivity of 409.17°/RIU, QF of 97.45RIU-1 and a DA of 1.37. The results provide a substantial improvement in comparison to the reported work in the literature.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141456449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning-enhanced predictive modeling for arbitrary deterministic lateral displacement design and test. 用于任意确定性侧向位移设计和测试的机器学习增强型预测建模。

IF 3.7 4区生物学

IEEE Transactions on NanoBioscience Pub Date : 2024-06-17 DOI: 10.1109/TNB.2024.3415365

Yidan Zhang, Junchao Wang, Jinkai Chen, Guodong Su, Wen-Sheng Zhao, Jun Liu

{"title":"Machine learning-enhanced predictive modeling for arbitrary deterministic lateral displacement design and test.","authors":"Yidan Zhang, Junchao Wang, Jinkai Chen, Guodong Su, Wen-Sheng Zhao, Jun Liu","doi":"10.1109/TNB.2024.3415365","DOIUrl":"10.1109/TNB.2024.3415365","url":null,"abstract":"The separation of biological particles like cells and macromolecules from liquid samples is vital in clinical medicine, supporting liquid biopsies and diagnostics. Deterministic Lateral Displacement (DLD) is prominent for sorting particles in microfluidics by size. However, the design, fabrication, and testing of DLDs are complex and time-consuming. Researchers typically rely on finite element analysis to predict particle trajectories, which are crucial in evaluating the performance of DLD. Traditional particle trajectory predictions through finite element analysis often inaccurately reflect experimental results due to manufacturing and experimental variabilities. To address this issue, we introduced a machine learning-enhanced approach, combining past experimental data and advanced modeling techniques. Our method, using a dataset of 132 experiments from 40 DLD chips and integrating finite element simulation with a microfluidic-optimized particle simulation algorithm (MOPSA) and a Random Forest model, improves trajectory prediction and critical size determination without physical tests. This enhanced accuracy in simulation across various DLD chips speeds up development. Our model, validated against three DLD chip designs, showed a high correlation between predicted and experimental particle trajectories, streamlining chip development for clinical applications.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141418735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0