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https://www.selleckchem.com/products/sotrastaurin-aeb071.html 32%, and the area under the curve was 0.982. Conclusions The accuracy of deep learning neural network model in the 4-category classification of non-inflammatory aortic lesionsis confirmed based on digital photomicrographs. This method can effectively improve the diagnostic efficiency of pathologists.Objective To study the application of cell transfer technology to solve the problem of the limited number of fine needle aspiration cytology (FNAC) smears for various immunocytochemistry (ICC) staining and other auxiliary tests, and to enhance accurate cytological diagnosis. Methods Thirty-four cases of FNAC smears from January 2020 to April 2020 in the Department of Pathology of Beijing Hospital were collected for investigation of the cell transfer technique. The materials in the most cell smear were divided and transferred to several glass slides. After de-staining, the recipient slides were stained with EnVision ICC. The technique was validated by comparing the consistency of the ICC of transferred cell smears and the corresponding immunohistochemical (IHC) staining on biopsies. Results There were a total of 180 cell transfer slides from 34 cases, of which 174 had the same cell morphology, size and structure as the original smears, with the success rate of cell transfer of 96.7% (174/180). Totally 174 ICC stains were performed on the successfully transferred cell smears, of which 153 smears had available corresponding IHC staining of histologic specimens. Of these, 148 showed concordance between ICC staining and the IHC staining. Cells were successfully transferred in 96.7 % (148/153) of the cell sheets, keeping the same morphology and structure as compared to their original smears. The diagnosis of all 34 FNAC cases was the same to that of their corresponding pathology on biopsies with 100 % concordance. Conclusions The cell transfer technique is a simple and effective way to make full use of diagnostic ce

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