OBJECTIVE To explore a novel pH-sensitive fluorescent probe for in vivo tumor imaging.METHODS Zn5 were obtained in 140℃ after mixed with Me OH,water,Zn(NO_3)2·6 H_2O,H4L and trimethylamine.The fluorescence spect...OBJECTIVE To explore a novel pH-sensitive fluorescent probe for in vivo tumor imaging.METHODS Zn5 were obtained in 140℃ after mixed with Me OH,water,Zn(NO_3)2·6 H_2O,H4L and trimethylamine.The fluorescence spectra of Zn5 with the same concentration in different pH aqueous solutions were detected.And the stability of Zn5 was investigated by time dependent fluorescence emission spectra of Zn5 in BSA aqueous solution and 5.0% serum solution.Then,the cytotoxicity of Zn5 was detected by MTT assays.To clarify whether a similar fluorescence response occurs in biological organisms,He La cells were pretreated with probe Zn5(0.5 μmol·L^(-1)) and fluorescence imaging were collected for targeting lysosomes in living cells because of lysosomes′ acidic microenvironment.The A375 tumor-bearing mice were used to assess the imaging ability of Zn5 in vivo.Mouse tumor xenografts were established by injection of A375 cells with 2×10~6 cells per flank.Probe(1 μg·g^(-1)) was administered to mice by injection.Images were obtained using IVIS Spectrum CT Imaging System.RESULTS There is a 11-fold intensity increasing as the pH values changing from 8 to 2.The almost unchanged emission intensities suggest Zn5 is stable in both BSA and serum.Zn5 has negligible cytotoxicity for He La,293 T and CHO-K1 cells.Zn5 can selectively display lysosomes in living cells.Both the 2D and 3D images in vivo distinguish the tumor from other tissues with good fluorescence contrast.CONCLUSION The high chemical stability,emission in the Vis/NIR range,pH sensitivity,a pKa located in the tumor pH range,and low toxicity make Zn5 is suitable for application as a pH-sensitive fluorescent probe for bio-imaging.展开更多
Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface ...Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface vehicle,the swarm robot system is more efficient than the operation of a single object as the former can reduce cost and save time.It is necessary to detect adjacent surface obstacles robustly to operate a cluster of unmanned surface vehicles.For this purpose,a LiDAR(light detection and ranging)sensor is used as it can simultaneously obtain 3D information for all directions,relatively robustly and accurately,irrespective of the surrounding environmental conditions.Although the GPS(global-positioning-system)error range exists,obtaining measurements of the surface-vessel position can still ensure stability during platoon maneuvering.In this study,a three-layer convolutional neural network is applied to classify types of surface vehicles.The aim of this approach is to redefine the sparse 3D point cloud data as 2D image data with a connotative meaning and subsequently utilize this transformed data for object classification purposes.Hence,we have proposed a descriptor that converts the 3D point cloud data into 2D image data.To use this descriptor effectively,it is necessary to perform a clustering operation that separates the point clouds for each object.We developed voxel-based clustering for the point cloud clustering.Furthermore,using the descriptor,3D point cloud data can be converted into a 2D feature image,and the converted 2D image is provided as an input value to the network.We intend to verify the validity of the proposed 3D point cloud feature descriptor by using experimental data in the simulator.Furthermore,we explore the feasibility of real-time object classification within this framework.展开更多
基金supported by Distinguished Young Scholars(21525101)the NSFC(91422302,and 21371037)
文摘OBJECTIVE To explore a novel pH-sensitive fluorescent probe for in vivo tumor imaging.METHODS Zn5 were obtained in 140℃ after mixed with Me OH,water,Zn(NO_3)2·6 H_2O,H4L and trimethylamine.The fluorescence spectra of Zn5 with the same concentration in different pH aqueous solutions were detected.And the stability of Zn5 was investigated by time dependent fluorescence emission spectra of Zn5 in BSA aqueous solution and 5.0% serum solution.Then,the cytotoxicity of Zn5 was detected by MTT assays.To clarify whether a similar fluorescence response occurs in biological organisms,He La cells were pretreated with probe Zn5(0.5 μmol·L^(-1)) and fluorescence imaging were collected for targeting lysosomes in living cells because of lysosomes′ acidic microenvironment.The A375 tumor-bearing mice were used to assess the imaging ability of Zn5 in vivo.Mouse tumor xenografts were established by injection of A375 cells with 2×10~6 cells per flank.Probe(1 μg·g^(-1)) was administered to mice by injection.Images were obtained using IVIS Spectrum CT Imaging System.RESULTS There is a 11-fold intensity increasing as the pH values changing from 8 to 2.The almost unchanged emission intensities suggest Zn5 is stable in both BSA and serum.Zn5 has negligible cytotoxicity for He La,293 T and CHO-K1 cells.Zn5 can selectively display lysosomes in living cells.Both the 2D and 3D images in vivo distinguish the tumor from other tissues with good fluorescence contrast.CONCLUSION The high chemical stability,emission in the Vis/NIR range,pH sensitivity,a pKa located in the tumor pH range,and low toxicity make Zn5 is suitable for application as a pH-sensitive fluorescent probe for bio-imaging.
基金supported by the Future Challenge Program through the Agency for Defense Development funded by the Defense Acquisition Program Administration (No.UC200015RD)。
文摘Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface vehicle,the swarm robot system is more efficient than the operation of a single object as the former can reduce cost and save time.It is necessary to detect adjacent surface obstacles robustly to operate a cluster of unmanned surface vehicles.For this purpose,a LiDAR(light detection and ranging)sensor is used as it can simultaneously obtain 3D information for all directions,relatively robustly and accurately,irrespective of the surrounding environmental conditions.Although the GPS(global-positioning-system)error range exists,obtaining measurements of the surface-vessel position can still ensure stability during platoon maneuvering.In this study,a three-layer convolutional neural network is applied to classify types of surface vehicles.The aim of this approach is to redefine the sparse 3D point cloud data as 2D image data with a connotative meaning and subsequently utilize this transformed data for object classification purposes.Hence,we have proposed a descriptor that converts the 3D point cloud data into 2D image data.To use this descriptor effectively,it is necessary to perform a clustering operation that separates the point clouds for each object.We developed voxel-based clustering for the point cloud clustering.Furthermore,using the descriptor,3D point cloud data can be converted into a 2D feature image,and the converted 2D image is provided as an input value to the network.We intend to verify the validity of the proposed 3D point cloud feature descriptor by using experimental data in the simulator.Furthermore,we explore the feasibility of real-time object classification within this framework.
