This paper demonstrates the use of deep learning,specifically the U-Net model,to recognize the menisci of droplets in an electrowetting-on-dielectric(EWOD)digital microfluidic(DMF)device.Accurate recognition of drople...This paper demonstrates the use of deep learning,specifically the U-Net model,to recognize the menisci of droplets in an electrowetting-on-dielectric(EWOD)digital microfluidic(DMF)device.Accurate recognition of droplet menisci would enable precise control over the movement of droplets to improve the performance and reliability of an EWOD DMF system.Furthermore,important information such as fluid properties,droplet characteristics,spatial position,dynamic behavior,and reaction kinetics of droplets during DMF manipulation can be understood by recognizing the menisci.Through a convolutional neural network utilizing the U-Net architecture,precise identification of droplet menisci is achieved.A diverse dataset is prepared and used to train and test the model.As a showcase,details of training and the optimization of hyperparameters are described.Experimental validation demonstrated that the trained model achieves a 98% accuracy rate and a 0.92 Dice score,which confirms the model’s high performance.After the successful recognition of droplet menisci,postprocessing techniques are applied to extract essential information such as the droplet and bubble size and volume.This study shows that the trained U-Net model is capable of discerning droplet menisci even in the presence of background image interference and low-quality images.The model can detect not only simple droplets,but also compound droplets of two immiscible liquids,droplets containing gas bubbles,and multiple droplets of varying sizes.Finally,the model is shown to detect satellite droplets as small as 2% of the size of the primary droplet,which are byproducts of droplet splitting.展开更多
We developed magnetically driven bionic drug-loaded nanorobots(MDNs)to accurately target tumors and deliver chemotherapy agents using a customized three-dimensional(3D)magnetic manipulation platform(MMP)system to prec...We developed magnetically driven bionic drug-loaded nanorobots(MDNs)to accurately target tumors and deliver chemotherapy agents using a customized three-dimensional(3D)magnetic manipulation platform(MMP)system to precisely control their movement mode.MDNs were based on polyethylene glycol-modified homogeneous ultrasmall iron oxide nanoparticles(7.02±0.18 nm).Doxorubicin(12%±2%[w/w])was encapsulated in MDNs by an imide bond.MDNs could imitate the movement mode of a school of wild herrings(e.g.,re-dispersion/arrangement/vortex/directional movement)to adapt to the changing and complex physiological environment through the 3D MMP system.MDNs overcame blood flow resistance and biological barriers using optimized magnetic driving properties according to in vivo imaging(magnetic resonance imaging and fluorescence)and histopathology.The performance of fabricated MDNs was verified through cells and tumor-bearing mouse models.The MDNs showed high efficiency of drug delivery and targeting at the tumor site(>10-fold),lower toxicity than free doxorubicin(5 mg/kg body weight),activated immune response in the tumor site,and significantly lengthened survival for mice.The synergistic interaction between MDNs and the 3D MMP system underscores the immense potential of this drug delivery system,indicating a potential revolution in the field of tumor chemotherapy.展开更多
文摘This paper demonstrates the use of deep learning,specifically the U-Net model,to recognize the menisci of droplets in an electrowetting-on-dielectric(EWOD)digital microfluidic(DMF)device.Accurate recognition of droplet menisci would enable precise control over the movement of droplets to improve the performance and reliability of an EWOD DMF system.Furthermore,important information such as fluid properties,droplet characteristics,spatial position,dynamic behavior,and reaction kinetics of droplets during DMF manipulation can be understood by recognizing the menisci.Through a convolutional neural network utilizing the U-Net architecture,precise identification of droplet menisci is achieved.A diverse dataset is prepared and used to train and test the model.As a showcase,details of training and the optimization of hyperparameters are described.Experimental validation demonstrated that the trained model achieves a 98% accuracy rate and a 0.92 Dice score,which confirms the model’s high performance.After the successful recognition of droplet menisci,postprocessing techniques are applied to extract essential information such as the droplet and bubble size and volume.This study shows that the trained U-Net model is capable of discerning droplet menisci even in the presence of background image interference and low-quality images.The model can detect not only simple droplets,but also compound droplets of two immiscible liquids,droplets containing gas bubbles,and multiple droplets of varying sizes.Finally,the model is shown to detect satellite droplets as small as 2% of the size of the primary droplet,which are byproducts of droplet splitting.
基金supported by the National Key R&D Program of China(2022YFA1207300,2021YFA1201204,and 2022YFF1502000)the directional institutional-ized scientific research platform relying on the Beijing Synchrotron Radiation Facility(BSRF)of the Chinese Academy of Sclences,the Science and Technology Plan Program of Beijing(Z221100007122006)the Beijing Municipal Fund for Distinguished Young Scholars(grant JQ22022).
文摘We developed magnetically driven bionic drug-loaded nanorobots(MDNs)to accurately target tumors and deliver chemotherapy agents using a customized three-dimensional(3D)magnetic manipulation platform(MMP)system to precisely control their movement mode.MDNs were based on polyethylene glycol-modified homogeneous ultrasmall iron oxide nanoparticles(7.02±0.18 nm).Doxorubicin(12%±2%[w/w])was encapsulated in MDNs by an imide bond.MDNs could imitate the movement mode of a school of wild herrings(e.g.,re-dispersion/arrangement/vortex/directional movement)to adapt to the changing and complex physiological environment through the 3D MMP system.MDNs overcame blood flow resistance and biological barriers using optimized magnetic driving properties according to in vivo imaging(magnetic resonance imaging and fluorescence)and histopathology.The performance of fabricated MDNs was verified through cells and tumor-bearing mouse models.The MDNs showed high efficiency of drug delivery and targeting at the tumor site(>10-fold),lower toxicity than free doxorubicin(5 mg/kg body weight),activated immune response in the tumor site,and significantly lengthened survival for mice.The synergistic interaction between MDNs and the 3D MMP system underscores the immense potential of this drug delivery system,indicating a potential revolution in the field of tumor chemotherapy.
