[ Objective] The paper aimed to search new identification methods of Encephalitozoon cuniculi on tissue sections. [ Method] Using improved Gram staining method and methyl green pyronin staining method, the pathologica...[ Objective] The paper aimed to search new identification methods of Encephalitozoon cuniculi on tissue sections. [ Method] Using improved Gram staining method and methyl green pyronin staining method, the pathological sections of sick rabbits were stained and identified. [ Result] The pathological changes in brain tissue could be clearly observed on sections, but parasites were not examined in pathological brain tissues stained by common staining method. When the pathological section was stained by improved Gram staining method, the pathological changes in brain tissue were not ouly stained very clearly, but blue parasites were also found in brain tissues. The parasites in epithelioid cells were stained into purple ones by methyl green pyronin staining method. [ Conclusion] The im- proved Gram staining method and methyl green pyronin staining method performed good staining effects of E. cuniculi in pathological sections, which were conducive to rapid diagnosis of encephalitozoonosis in rabbit.展开更多
Fluorescence labeling and imaging provide an opportunity to observe the structure of biological tissues,playing a crucial role in the field of histopathology.However,when labeling and imaging biological tissues,there ...Fluorescence labeling and imaging provide an opportunity to observe the structure of biological tissues,playing a crucial role in the field of histopathology.However,when labeling and imaging biological tissues,there are still some challenges,e.g.,time-consuming tissue preparation steps,expensive reagents,and signal bias due to photobleaching.To overcome these limitations,we present a deep-learning-based method for fluorescence translation of tissue sections,which is achieved by conditional generative adversarial network(cGAN).Experimental results from mouse kidney tissues demonstrate that the proposed method can predict the other types of fluorescence images from one raw fluorescence image,and implement the virtual multi-label fluorescent staining by merging the generated different fluorescence images as well.Moreover,this proposed method can also effectively reduce the time-consuming and laborious preparation in imaging processes,and further saves the cost and time.展开更多
基金Supported by National Natural Science Foundation of China(31372407)
文摘[ Objective] The paper aimed to search new identification methods of Encephalitozoon cuniculi on tissue sections. [ Method] Using improved Gram staining method and methyl green pyronin staining method, the pathological sections of sick rabbits were stained and identified. [ Result] The pathological changes in brain tissue could be clearly observed on sections, but parasites were not examined in pathological brain tissues stained by common staining method. When the pathological section was stained by improved Gram staining method, the pathological changes in brain tissue were not ouly stained very clearly, but blue parasites were also found in brain tissues. The parasites in epithelioid cells were stained into purple ones by methyl green pyronin staining method. [ Conclusion] The im- proved Gram staining method and methyl green pyronin staining method performed good staining effects of E. cuniculi in pathological sections, which were conducive to rapid diagnosis of encephalitozoonosis in rabbit.
基金This work was supported in part by the National Natural Science Foundation of China(61871263,12274092,and 12034005)in part by the Explorer Program of Shanghai(21TS1400200)+1 种基金in part by the Natural Science Foundation of Shanghai(21ZR1405200)in part by the Medical Engineering Fund of Fudan University(YG2022-6).
文摘Fluorescence labeling and imaging provide an opportunity to observe the structure of biological tissues,playing a crucial role in the field of histopathology.However,when labeling and imaging biological tissues,there are still some challenges,e.g.,time-consuming tissue preparation steps,expensive reagents,and signal bias due to photobleaching.To overcome these limitations,we present a deep-learning-based method for fluorescence translation of tissue sections,which is achieved by conditional generative adversarial network(cGAN).Experimental results from mouse kidney tissues demonstrate that the proposed method can predict the other types of fluorescence images from one raw fluorescence image,and implement the virtual multi-label fluorescent staining by merging the generated different fluorescence images as well.Moreover,this proposed method can also effectively reduce the time-consuming and laborious preparation in imaging processes,and further saves the cost and time.
