{"title":"通过视觉感知:通过生物纳米物联网进行血液黏度的体内检测和外部传输","authors":"Yue Sun;Kunlun Wu;Dong Du;Yifan Chen","doi":"10.1109/JSEN.2025.3579155","DOIUrl":null,"url":null,"abstract":"This study introduces a novel “sensing-by-seeing” methodology for real-time in vivo detection and external transmission of whole blood viscosity (WBV) through the Internet of Bio-Nano Things (IoBNT). The motivation arises from the lack of real-time, noninvasive techniques to monitor dynamic tumor-induced physiological changes. Leveraging the computational nanobiosensing (CONA) framework, we model the tumor targeting process as a natural computation problem, where tumor-triggered biological gradient fields (BGFs) serve as the objective function to be optimized. Externally controlled nanoswarms (NSs) act as search agents, sensing BGF variations, and transmitting data to an external monitoring device. WBV, a key indicator of the tumor microenvironment (TME), modulates NS dynamics and reflects pathological progression. We employ the full-width-at-half-maximum (FWHM) of NS spatial distributions as a measurable parameter for WBV estimation, integrating convection-diffusion modeling with statistical mechanics. To address measurement noise, we propose a rotating magnetic field strategy to stabilize NS behavior. In silico simulations validate the spatiotemporal dynamics of WBV in capillary-scale TME models, achieving a relative detection error of 10%. Our method tracks sinusoidal, square, and triangular WBV waveforms with robust performance under thermal and flow variations. A strong correlation (<inline-formula> <tex-math>${R}^{{2}} = {0.9617}$ </tex-math></inline-formula>) with Brookfield viscometer measurements confirms empirical consistency. The results demonstrate the feasibility of closed-loop NS-based sensing, advancing IoBNT applications in tumor detection and continuous health monitoring.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"29940-29952"},"PeriodicalIF":4.3000,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sensing by Seeing: In Vivo Detection and External Transmission of Blood Viscosity Through the Internet of Bio-Nano Things\",\"authors\":\"Yue Sun;Kunlun Wu;Dong Du;Yifan Chen\",\"doi\":\"10.1109/JSEN.2025.3579155\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study introduces a novel “sensing-by-seeing” methodology for real-time in vivo detection and external transmission of whole blood viscosity (WBV) through the Internet of Bio-Nano Things (IoBNT). The motivation arises from the lack of real-time, noninvasive techniques to monitor dynamic tumor-induced physiological changes. Leveraging the computational nanobiosensing (CONA) framework, we model the tumor targeting process as a natural computation problem, where tumor-triggered biological gradient fields (BGFs) serve as the objective function to be optimized. Externally controlled nanoswarms (NSs) act as search agents, sensing BGF variations, and transmitting data to an external monitoring device. WBV, a key indicator of the tumor microenvironment (TME), modulates NS dynamics and reflects pathological progression. We employ the full-width-at-half-maximum (FWHM) of NS spatial distributions as a measurable parameter for WBV estimation, integrating convection-diffusion modeling with statistical mechanics. To address measurement noise, we propose a rotating magnetic field strategy to stabilize NS behavior. In silico simulations validate the spatiotemporal dynamics of WBV in capillary-scale TME models, achieving a relative detection error of 10%. Our method tracks sinusoidal, square, and triangular WBV waveforms with robust performance under thermal and flow variations. A strong correlation (<inline-formula> <tex-math>${R}^{{2}} = {0.9617}$ </tex-math></inline-formula>) with Brookfield viscometer measurements confirms empirical consistency. The results demonstrate the feasibility of closed-loop NS-based sensing, advancing IoBNT applications in tumor detection and continuous health monitoring.\",\"PeriodicalId\":447,\"journal\":{\"name\":\"IEEE Sensors Journal\",\"volume\":\"25 15\",\"pages\":\"29940-29952\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2025-06-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Sensors Journal\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11040134/\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Sensors Journal","FirstCategoryId":"103","ListUrlMain":"https://ieeexplore.ieee.org/document/11040134/","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Sensing by Seeing: In Vivo Detection and External Transmission of Blood Viscosity Through the Internet of Bio-Nano Things
This study introduces a novel “sensing-by-seeing” methodology for real-time in vivo detection and external transmission of whole blood viscosity (WBV) through the Internet of Bio-Nano Things (IoBNT). The motivation arises from the lack of real-time, noninvasive techniques to monitor dynamic tumor-induced physiological changes. Leveraging the computational nanobiosensing (CONA) framework, we model the tumor targeting process as a natural computation problem, where tumor-triggered biological gradient fields (BGFs) serve as the objective function to be optimized. Externally controlled nanoswarms (NSs) act as search agents, sensing BGF variations, and transmitting data to an external monitoring device. WBV, a key indicator of the tumor microenvironment (TME), modulates NS dynamics and reflects pathological progression. We employ the full-width-at-half-maximum (FWHM) of NS spatial distributions as a measurable parameter for WBV estimation, integrating convection-diffusion modeling with statistical mechanics. To address measurement noise, we propose a rotating magnetic field strategy to stabilize NS behavior. In silico simulations validate the spatiotemporal dynamics of WBV in capillary-scale TME models, achieving a relative detection error of 10%. Our method tracks sinusoidal, square, and triangular WBV waveforms with robust performance under thermal and flow variations. A strong correlation (${R}^{{2}} = {0.9617}$ ) with Brookfield viscometer measurements confirms empirical consistency. The results demonstrate the feasibility of closed-loop NS-based sensing, advancing IoBNT applications in tumor detection and continuous health monitoring.
期刊介绍:
The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following:
-Sensor Phenomenology, Modelling, and Evaluation
-Sensor Materials, Processing, and Fabrication
-Chemical and Gas Sensors
-Microfluidics and Biosensors
-Optical Sensors
-Physical Sensors: Temperature, Mechanical, Magnetic, and others
-Acoustic and Ultrasonic Sensors
-Sensor Packaging
-Sensor Networks
-Sensor Applications
-Sensor Systems: Signals, Processing, and Interfaces
-Actuators and Sensor Power Systems
-Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting
-Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data)
-Sensors in Industrial Practice