IEEE Transactions on NanoBioscience最新文献_第4页

IEEE Transactions on NanoBioscience Information for Authors IEEE纳米生物科学信息汇刊

IF 4.2 4区生物学

IEEE Transactions on NanoBioscience Pub Date : 2026-07-01 Epub Date: 2026-07-02 DOI: 10.1109/TNB.2026.3696735

引用次数: 0

IEEE Transactions on NanoBioscience Publication Information IEEE纳米生物科学学报

IF 4.2 4区生物学

IEEE Transactions on NanoBioscience Pub Date : 2026-07-01 Epub Date: 2026-07-02 DOI: 10.1109/TNB.2026.3696731

引用次数: 0

Ion Transmitter for Molecular Communication 用于分子通信的离子传递器。

IF 4.2 4区生物学

IEEE Transactions on NanoBioscience Pub Date : 2026-07-01 Epub Date: 2026-03-23 DOI: 10.1109/TNB.2026.3676352

Shaojie Zhang;Ozgur B. Akan

引用次数: 0

Dispersion Control in Stochastic Biomolecular Systems Without Peak Shifts 无峰移随机生物分子系统的色散控制。

IF 4.2 4区生物学

IEEE Transactions on NanoBioscience Pub Date : 2026-07-01 Epub Date: 2026-03-04 DOI: 10.1109/TNB.2026.3670609

Taishi Kotsuka;Enoch Yeung

{"title":"Dispersion Control in Stochastic Biomolecular Systems Without Peak Shifts","authors":"Taishi Kotsuka;Enoch Yeung","doi":"10.1109/TNB.2026.3670609","DOIUrl":"10.1109/TNB.2026.3670609","url":null,"abstract":"Intracellular biomolecular circuits often exhibit multimodal stationary distributions due to intrinsic noise, and the dispersion around each peak governs phenotypic robustness and adaptability. However, tuning dispersion by changing reaction parameters typically shifts peak positions or even alters modality. In this paper, we derive conditions that enable peak-shape control without peak shifts using the Chemical Fokker-Planck Equation. First, we formalize sharpness as a peak-local measure via probability ratios and show that the positions of peaks and valleys remain invariant as a control parameter varies. Second, we prove that sharpness varies monotonically as the control parameter increases, while the modality and the positions of extrema remain fixed. We validate these results with Monte Carlo simulations of two univariate networks: the burst-and-trickle gene expression system (unimodal) and the Schlögl system (bimodal), achieving dispersion tuning without peak shifts. Finally, we present preliminary multivariate evidence on the Genetic Toggle Switch, where the marginal distribution of one protein exhibits similar sharpness control. Our results provide structural design rules for engineering stochastic phenotypes while safeguarding the desired modal structure.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":"25 3","pages":"340-348"},"PeriodicalIF":4.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147354870","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

Channel Modeling for Mobile Molecular Communication With Anomalous Diffusion by Deep Neural Network 基于深度神经网络的异常扩散移动分子通信通道建模。

IF 4.2 4区生物学

IEEE Transactions on NanoBioscience Pub Date : 2026-07-01 Epub Date: 2026-01-19 DOI: 10.1109/TNB.2026.3654966

Zhen Cheng;Miaodi Chen;Ming Xia;Qu Li;Kaikai Chi;Xinwei Yao

{"title":"Channel Modeling for Mobile Molecular Communication With Anomalous Diffusion by Deep Neural Network","authors":"Zhen Cheng;Miaodi Chen;Ming Xia;Qu Li;Kaikai Chi;Xinwei Yao","doi":"10.1109/TNB.2026.3654966","DOIUrl":"10.1109/TNB.2026.3654966","url":null,"abstract":"Diffusion-based mobile molecular communication (MMC) systems have shown great potential in nanoscale communication, particularly in the scenarios involving anomalous diffusion. Accurately modeling the anomalous diffusion channel of MMC system with multiple receivers is a challenge. However, prior studies have predominantly addressed conventional analytical approaches to characterize the channel impulse response (CIR) of static molecular communication system under normal diffusion channel. However, the deduction method cannot adapt to time-varying and complex channel conditions. In this paper, we study a three dimensional MMC system with one transmitter and multiple receivers under anomalous diffusion channel. We propose a method based on deep neural network (DNN) to predict the parameters of the CIR of this MMC system. Simulation results demonstrate that the prediction ability of DNN-based model outperforms the recurrent neural networks (RNN) based and the long short-term memory (LSTM) based models in terms of prediction ability under different anomalous diffusion conditions. The DNN-based model can effectively improve the accuracy of predicting the CIR for this MMC system, providing a new approach for channel modeling in MMC systems with anomalous diffusion.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":"25 3","pages":"289-300"},"PeriodicalIF":4.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146003267","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

Recent Advancements and Emerging Trends in Biosensors: A Review on Disease Diagnosis and Personalized Medicine 生物传感器的最新进展和新趋势：疾病诊断和个性化医疗综述。

IF 4.2 4区生物学

IEEE Transactions on NanoBioscience Pub Date : 2026-07-01 Epub Date: 2026-02-16 DOI: 10.1109/TNB.2026.3664930

Vivek Arya;Dharmendra Kumar;Vijay Shanker Chaudhary;Smita Kumari;Anupam Sahu;Sneha Sharma;Ragini Singh

{"title":"Recent Advancements and Emerging Trends in Biosensors: A Review on Disease Diagnosis and Personalized Medicine","authors":"Vivek Arya;Dharmendra Kumar;Vijay Shanker Chaudhary;Smita Kumari;Anupam Sahu;Sneha Sharma;Ragini Singh","doi":"10.1109/TNB.2026.3664930","DOIUrl":"10.1109/TNB.2026.3664930","url":null,"abstract":"Recent research advances and developments in biosensor technology and an overview of its innovative applications for the healthcare industry are reviewed in this article. Biosensors can monitor and detect a variety of biological and chemical characteristics with accuracy and less or minimal invasiveness. It can be classified depending upon their working principle and transducing property, like electrochemical, optical, and piezoelectric processes, and are deployed for various medical applications discussed in this article. This paper provides a brief review of the novel applications of biosensors in healthcare, including illness diagnosis, medication development, and personalized medicine. Difficulties in healthcare biosensor technology as well as the future directions for research and development is highlighted. The fundamental elements of biosensors, their types—such as electrochemical, piezoelectric, thermometric, and optical—as well as current developments in the area, operating theories, and their potential applications, have been covered. This article provides a thorough understanding of the most current advancements in biosensor technology for healthcare and how they may further enhance outcomes and treatment for patients.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":"25 3","pages":"332-339"},"PeriodicalIF":4.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146206650","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

Automation of Fluorescence-Activated Droplet Release by Deep-Learning-Based Droplet Detector 基于深度学习的荧光激活液滴检测器自动释放液滴。

IF 4.2 4区生物学

IEEE Transactions on NanoBioscience Pub Date : 2026-07-01 Epub Date: 2026-01-19 DOI: 10.1109/TNB.2026.3655030

Guangyao Cheng;Yi-Ping Ho

{"title":"Automation of Fluorescence-Activated Droplet Release by Deep-Learning-Based Droplet Detector","authors":"Guangyao Cheng;Yi-Ping Ho","doi":"10.1109/TNB.2026.3655030","DOIUrl":"10.1109/TNB.2026.3655030","url":null,"abstract":"Droplet-based microfluidics enables miniaturized, high-throughput biochemical assays but faces challenges in selective droplet retrieval, particularly after long-term monitoring. While light-induced bubble generation offers a promising, hardware-simplified strategy for releasing individual droplets from passive traps, current implementations suffer from fabrication complexity or slow-release kinetics. To overcome these limitations, we previously developed a light-responsive fluorosurfactant using fluorinated plasmonic nanoparticles (f-PNPs) that enables millisecond-scale vapor bubble formation and efficient droplet release using a 532 nm laser, without requiring integrated photothermal materials. However, automation of this approach was limited by the need for manual droplet identification and release decisions. In this work, we introduce a fully automated Fluorescence-Activated Droplet Release (FADR) system by integrating the light-triggered release mechanism with a deep-learning-based droplet detector. This AI module autonomously identifies and localizes droplets in real-time, triggering selective release based on fluorescence intensity without human intervention. The closed-loop FADR platform offers a scalable and intelligent solution for precise droplet manipulation, enabling robust, high-throughput screening workflows with minimal hardware complexity.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":"25 3","pages":"271-277"},"PeriodicalIF":4.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11358754","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146003246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Synthesis and Characterization of Polyethylenimine-Coated Magnetic Nanoparticles for DNA Binding Toward Pollen-Directed Gene Delivery Applications 聚乙烯亚胺包覆磁性纳米颗粒的合成与表征及其在花粉导向基因传递中的应用。

IF 4.2 4区生物学

IEEE Transactions on NanoBioscience Pub Date : 2026-07-01 Epub Date: 2026-02-23 DOI: 10.1109/TNB.2026.3666861

Khatuna Jannat Esha;Anik Roy;Md Shamsudduha;Taslim Ur Rashid;Mohammad Shamimul Alam;Harinarayan Das;Tahmina Islam;Mahmudur Rahman;Saeed Mahmud Ullah;Ahsan Habib

{"title":"Synthesis and Characterization of Polyethylenimine-Coated Magnetic Nanoparticles for DNA Binding Toward Pollen-Directed Gene Delivery Applications","authors":"Khatuna Jannat Esha;Anik Roy;Md Shamsudduha;Taslim Ur Rashid;Mohammad Shamimul Alam;Harinarayan Das;Tahmina Islam;Mahmudur Rahman;Saeed Mahmud Ullah;Ahsan Habib","doi":"10.1109/TNB.2026.3666861","DOIUrl":"10.1109/TNB.2026.3666861","url":null,"abstract":"Ensuring global food security remains a pressing challenge due to the growing world population and accelerating climate change. Genetic modification provides a pathway to developing crops with improved traits, yet conventional approaches rely heavily on tissue culture regeneration, which is complex, time-consuming, and often impractical for many crop species. Recently, pollen magnetofection has emerged as a tissue culture-independent alternative for plant genetic transformation. In this study, we investigated polyethylenimine (PEI)-coated Fe3O4 (Fe3O4@PEI) magnetic nanoparticles (MNPs) as potential carriers for pollen-mediated gene delivery, focusing on their synthesis, characterization, and DNA-binding capacity. The Fe3O4@PEI MNPs were synthesized via a one-step <italic>in situ coating method and their properties were compared with bare Fe3O4 MNPs synthesized separately. The nanostructures were systematically characterized in terms of size, morphology, crystal structure, surface functionalization, elemental composition, and magnetic behavior, confirming successful <italic>in situ PEI coating without post-synthesis modification. Compared to bare Fe3O4 MNPs, Fe3O4@PEI MNPs displayed reduced agglomeration, a more uniform size distribution, and improved colloidal stability. The nanoparticles had an average particle size of approximately 30 nm and an enhanced DNA-binding performance: up to 6 ng of linear DNA per <inline-formula> <tex-math>$mu $ </tex-math></inline-formula> g of MNPs (DNA/MNP mass ratio of 0.006:1) and complete retention of plasmid DNA across the tested range. These results support the feasibility of Fe3O4@PEI nanocarriers for pollen-directed gene delivery in plants.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":"25 3","pages":"312-320"},"PeriodicalIF":4.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147276173","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

Spiking Neural P Systems for Conversion Between Number Systems 数字系统间转换的脉冲神经P系统。

IF 4.2 4区生物学

IEEE Transactions on NanoBioscience Pub Date : 2026-07-01 Epub Date: 2026-01-01 DOI: 10.1109/TNB.2025.3650349

Hongsen Zhang;Ping Guo;Xiaotong Liu;Chaoliang Li;Maosheng Zhong

引用次数: 0

Recurrent Neural Network Based on DNA Strand Displacement Circuits and Its Application in Location Prediction 基于DNA链位移电路的递归神经网络及其在位置预测中的应用。

IF 4.2 4区生物学

IEEE Transactions on NanoBioscience Pub Date : 2026-07-01 Epub Date: 2026-03-12 DOI: 10.1109/TNB.2026.3673367

Yanfeng Wang;Pengpeng Zhao;Junwei Sun

{"title":"Recurrent Neural Network Based on DNA Strand Displacement Circuits and Its Application in Location Prediction","authors":"Yanfeng Wang;Pengpeng Zhao;Junwei Sun","doi":"10.1109/TNB.2026.3673367","DOIUrl":"10.1109/TNB.2026.3673367","url":null,"abstract":"With the rapid development of science and technology, various intelligent devices continuously generate large amounts of real-time location data. Efficiently utilizing this data for accurate location prediction has become a critical issue in fields such as intelligent transportation and smart logistics. To realize low-power location prediction, this paper constructs a molecular recurrent neural network (RNN) model based on DNA strand displacement (DSD) technology. The RNN model can process sequence data and accurately predict the next position. Firstly, multiple modules are designed based on DSD circuits, including dual-channel weighted summation module, dual-domain data module and <italic>Tanh activation function module. Secondly, a RNN model for processing sequence data is constructed using the above modules. Finally, the constructed RNN model successfully achieves position prediction for multiple inputs and a single output. The robustness and accuracy of the neural network are verified through data experiment. It has been demonstrated that DNA molecules can effectively process complex sequence data. This method holds significant potential in the field of path planning. This paper uses MAE and RMSE to evaluate the experimental data. The results prove that the RNN model constructed in this paper demonstrates strong accuracy and stability.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":"25 3","pages":"349-360"},"PeriodicalIF":4.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147443685","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