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In this work, an image classification based on deep learning for quantitative field determination of aluminum ions (Al.sup.3+) was developed. Carbon quantum dots with yellow fluorescence were synthesized by a one-pot hydrothermal method which could specifically recognize Al.sup.3+ and produce enhanced green fluorescence. Using the convolutional neural network model in deep learning, an image classification was constructed to classify Al.sup.3+ samples at different concentrations. Then, a fitting method for classification information was proposed for the first time which could convert discontinuous, semi-quantitative concentration classification information into continuous, quantitative, and accurate concentration information. Recoveries of 92.0-110.3% in the concentration range of 0.3-320 [mu]M were obtained with a lower limit of detection of 0.3 [mu]M, exhibiting excellent accuracy and sensitivity. It could be completed in 2 min simply without requiring large equipment. Thus, the deep learning-enabled image classification paves a new way for the determination of metal ions., Author(s): Ce Wang [sup.1] , Zhaoliang Wang [sup.2] , Yifei Lu [sup.1] , Tingting Hao [sup.1] , Yufang Hu [sup.1] , Sui Wang [sup.1] , Zhiyong Guo [sup.1] Author Affiliations: [...]

Objective This study is assigned to explore the expression of CD85d in acute myeloid leukemia and its clinical significance in the diagnosis of monocyte associated acute myeloid leukemia. Methods We used flow cytometry by CD45/SSC to analyze the expression of CD85d, CD14, CD64 in 46 monocyte associated acute myeloid leukemia (M-AML) and 60 non monocyte associated acute myeloid leukemia (NM-AML) patients admitted to the Hematology Department of Hainan General Hospital from March 2022 to December 2023. Results Both normal granulocytes and normal monocytes express CD85d, and monocytes express fluorescence with stronger intensity than granulocytes. CD85d is not expressed in normal lymphocytes. The positive expression rate of CD85d in M-AML was 80.4% (37/46), which was significantly higher than that in NM-AML 3.3% (2/60), and the difference was statistically significant (P<0.01). The sensitivity of diagnosing M-AML was ranked from high to low as CD64 (87.0%)>CD85d (80.4%)>CD14 (34.8%). The specificity of diagnosing M-AML was ranked from high to low as with CD14 (100%)>CD85d (96.7%)>CD64 (56.7%). The sensitivity and specificity of CD85d combined with CD64 in diagnosing M-AML were 89.1% and 56.7%. There was no statistically significant difference between the detection rate of abnormal karyotypes (53.8%), positive rate of the fusion genes(26.9%), the CR rate (78.3%) in CD85d+ AML and that in CD85d- AML (66.0%, 50.0%, 70.0%) (P>0.05). Conclusion CD85d can be used as a new high sensitivity and specific surface marker for immature monocytes, which is helpful to improve the detection rate of M-AML and differential diagnosis with NM-AML subtypes. [ABSTRACT FROM AUTHOR]