Pub Date : 2023-08-07DOI: 10.1109/TNB.2023.3302773
Saswati Pal;Sudip Misra;Ranjan K. Mallik
Mesenchymal stem cell (MSC)-derived exosomes are recognized as an unparalleled therapy for tissue damage rendered by COVID-19 infection and subsequent hyper-inflammatory immune response. However, the natural targeting mechanism of exosomes is challenging to detect the damaged tissue over long diffusion distances efficiently. The coordinated movement of exosomes is desired for successful identification of target sites. In this work, we propose a molecular communication model, CoTiR, with a bio-inspired directional migration strategy (DMS) for guided propagation of exosomes to target the damaged tissues. The model includes directional propagation, reception, and regeneration of tissue. The proposed model has the potential to be used in designing efficient communication systems in the nanodomain. We compare the proposed model to the basic random propagation model and show the efficacy of our model regarding the detection of multiple targets and the detection time required. Simulation results indicate that the proposed model requires a shorter period of time for a similar number of exosomes to detect the targets compared to the basic random propagation model. Furthermore, the results reveal a 99.96% decrease in the collagen concentration in the absence of inflammatory cytokine molecules compared to the collagen concentration in the presence of inflammatory cytokine molecules.
{"title":"COTiR: Molecular Communication Model for Synthetic Exosome-Based Tissue Regeneration","authors":"Saswati Pal;Sudip Misra;Ranjan K. Mallik","doi":"10.1109/TNB.2023.3302773","DOIUrl":"10.1109/TNB.2023.3302773","url":null,"abstract":"Mesenchymal stem cell (MSC)-derived exosomes are recognized as an unparalleled therapy for tissue damage rendered by COVID-19 infection and subsequent hyper-inflammatory immune response. However, the natural targeting mechanism of exosomes is challenging to detect the damaged tissue over long diffusion distances efficiently. The coordinated movement of exosomes is desired for successful identification of target sites. In this work, we propose a molecular communication model, CoTiR, with a bio-inspired directional migration strategy (DMS) for guided propagation of exosomes to target the damaged tissues. The model includes directional propagation, reception, and regeneration of tissue. The proposed model has the potential to be used in designing efficient communication systems in the nanodomain. We compare the proposed model to the basic random propagation model and show the efficacy of our model regarding the detection of multiple targets and the detection time required. Simulation results indicate that the proposed model requires a shorter period of time for a similar number of exosomes to detect the targets compared to the basic random propagation model. Furthermore, the results reveal a 99.96% decrease in the collagen concentration in the absence of inflammatory cytokine molecules compared to the collagen concentration in the presence of inflammatory cytokine molecules.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":"23 1","pages":"202-209"},"PeriodicalIF":3.9,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9972527","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}
Pub Date : 2023-07-25DOI: 10.1109/TNB.2023.3298600
M. Okan Araz;Ahmet R. Emirdagi;M. Serkan Kopuzlu;Murat Kuscu
Molecular Communications (MC) is a bio-inspired communication technique that uses molecules to encode and transfer information. Many efforts have been devoted to developing novel modulation techniques for MC based on various distinguishable characteristics of molecules, such as their concentrations or types. In this paper, we investigate a particular modulation scheme called Ratio Shift Keying (RSK), where the information is encoded in the concentration ratio of two different types of molecules. RSK modulation is hypothesized to enable accurate information transfer in dynamic MC scenarios where the time-varying channel characteristics affect both types of molecules equally. To validate this hypothesis, we first conduct an information-theoretical analysis of RSK modulation and derive the capacity of the end-to-end MC channel where the receiver estimates concentration ratio based on ligand-receptor binding statistics in an optimal or suboptimal manner. We then analyze the error performance of RSK modulation in a practical time-varying MC scenario, that is mobile MC, in which both the transmitter and the receiver undergo diffusion-based propagation. Our numerical and analytical results, obtained for varying levels of similarity between the ligand types used for ratio-encoding, and varying number of receptors, show that RSK can significantly outperform the most commonly considered MC modulation technique, concentration shift keying (CSK), in dynamic MC scenarios.
分子通信(MC)是一种利用分子编码和传输信息的生物启发通信技术。许多人致力于根据分子的各种可区分特性(如浓度或类型)为 MC 开发新型调制技术。在本文中,我们研究了一种名为 "比移键控(RSK)"的特殊调制方案,在这种方案中,信息以两种不同类型分子的浓度比进行编码。根据假设,RSK 调制能在动态 MC 场景中实现准确的信息传输,在这种场景中,时变信道特性对两种分子的影响相同。为了验证这一假设,我们首先对 RSK 调制进行了信息理论分析,并得出了端到端 MC 信道的容量,在这种信道中,接收器根据配体-受体结合统计数据以最优或次优方式估计浓度比。然后,我们分析了 RSK 调制在实际时变 MC 场景(即移动 MC)中的误差性能,在这种场景中,发射器和接收器都经历了基于扩散的传播。我们对用于比率编码的配体类型之间不同程度的相似性和不同数量的受体进行的数值和分析结果表明,在动态 MC 场景中,RSK 的性能明显优于最常用的 MC 调制技术--浓度偏移键控(CSK)。
{"title":"Ratio Shift Keying Modulation for Time-Varying Molecular Communication Channels","authors":"M. Okan Araz;Ahmet R. Emirdagi;M. Serkan Kopuzlu;Murat Kuscu","doi":"10.1109/TNB.2023.3298600","DOIUrl":"10.1109/TNB.2023.3298600","url":null,"abstract":"Molecular Communications (MC) is a bio-inspired communication technique that uses molecules to encode and transfer information. Many efforts have been devoted to developing novel modulation techniques for MC based on various distinguishable characteristics of molecules, such as their concentrations or types. In this paper, we investigate a particular modulation scheme called Ratio Shift Keying (RSK), where the information is encoded in the concentration ratio of two different types of molecules. RSK modulation is hypothesized to enable accurate information transfer in dynamic MC scenarios where the time-varying channel characteristics affect both types of molecules equally. To validate this hypothesis, we first conduct an information-theoretical analysis of RSK modulation and derive the capacity of the end-to-end MC channel where the receiver estimates concentration ratio based on ligand-receptor binding statistics in an optimal or suboptimal manner. We then analyze the error performance of RSK modulation in a practical time-varying MC scenario, that is mobile MC, in which both the transmitter and the receiver undergo diffusion-based propagation. Our numerical and analytical results, obtained for varying levels of similarity between the ligand types used for ratio-encoding, and varying number of receptors, show that RSK can significantly outperform the most commonly considered MC modulation technique, concentration shift keying (CSK), in dynamic MC scenarios.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":"23 1","pages":"176-189"},"PeriodicalIF":3.9,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9924621","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}
Pub Date : 2023-07-24DOI: 10.1109/TNB.2023.3298444
Subhash Chandra Pal;Dimitrios Toumpanakis;Johan Wikström;Chirag Kamal Ahuja;Robin Strand;Ashis Kumar Dhara
Segmentation of major brain vessels is very important for the diagnosis of cerebrovascular disorders and subsequent surgical planning. Vessel segmentation is an important preprocessing step for a wide range of algorithms for the automatic diagnosis or treatment of several vascular pathologies and as such, it is valuable to have a well-performing vascular segmentation pipeline. In this article, we propose an end-to-end multiscale residual dual attention deep neural network for resilient major brain vessel segmentation. In the proposed network, the encoder and decoder blocks of the U-Net are replaced with the multi-level atrous residual blocks to enhance the learning capability by increasing the receptive field to extract the various semantic coarse- and fine-grained features. Dual attention block is incorporated in the bottleneck to perform effective multiscale information fusion to obtain detailed structure of blood vessels. The methods were evaluated on the publicly available TubeTK data set. The proposed method outperforms the state-of-the-art techniques with dice of 0.79 on the whole-brain prediction. The statistical and visual assessments indicate that proposed network is robust to outliers and maintains higher consistency in vessel continuity than the traditional U-Net and its variations.
{"title":"Multi-Level Residual Dual Attention Network for Major Cerebral Arteries Segmentation in MRA Toward Diagnosis of Cerebrovascular Disorders","authors":"Subhash Chandra Pal;Dimitrios Toumpanakis;Johan Wikström;Chirag Kamal Ahuja;Robin Strand;Ashis Kumar Dhara","doi":"10.1109/TNB.2023.3298444","DOIUrl":"10.1109/TNB.2023.3298444","url":null,"abstract":"Segmentation of major brain vessels is very important for the diagnosis of cerebrovascular disorders and subsequent surgical planning. Vessel segmentation is an important preprocessing step for a wide range of algorithms for the automatic diagnosis or treatment of several vascular pathologies and as such, it is valuable to have a well-performing vascular segmentation pipeline. In this article, we propose an end-to-end multiscale residual dual attention deep neural network for resilient major brain vessel segmentation. In the proposed network, the encoder and decoder blocks of the U-Net are replaced with the multi-level atrous residual blocks to enhance the learning capability by increasing the receptive field to extract the various semantic coarse- and fine-grained features. Dual attention block is incorporated in the bottleneck to perform effective multiscale information fusion to obtain detailed structure of blood vessels. The methods were evaluated on the publicly available TubeTK data set. The proposed method outperforms the state-of-the-art techniques with dice of 0.79 on the whole-brain prediction. The statistical and visual assessments indicate that proposed network is robust to outliers and maintains higher consistency in vessel continuity than the traditional U-Net and its variations.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":"23 1","pages":"167-175"},"PeriodicalIF":3.9,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10242076","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}
Pub Date : 2023-07-11DOI: 10.1109/TNB.2023.3294330
J. Divya;S. Selvendran;A. Sivanantha Raja;Vamsi Borra
A dual-channel D-shaped photonic crystal fiber (PCF) based plasmonic sensor is proposed in this paper for the simultaneous detection of two different analytes using the surface plasmon resonance (SPR) technique. The sensor employs a 50 nm-thick layer of chemically stable gold on both cleaved surfaces of the PCF to induce the SPR effect. This configuration offers superior sensitivity and rapid response, making it highly effective for sensing applications. Numerical investigations are conducted using the finite element method (FEM). After optimizing the structural parameters, the sensor exhibits a maximum wavelength sensitivity of 10000 nm/RIU and an amplitude sensitivity of −216 RIU $^{-{1}}$