Surface molecularly imprinted polymers (SMIPs) for selective adsorption of ampicillin sodium were synthesized using surface molecular imprinting technique with silica gel as a support. The physical and morphological characteristics of the polymers were investigated by scanning electron microscope (SEM), Fourier transform infrared spectroscopy (FT-IR), thermogravimetric analysis (TGA), elemental analysis and nitrogen adsorption–desorption test. The obtained results showed that the SMIPs displayed great adsorption capacity (13.5μg/mg), high recognition ability (the imprinted factor is 3.2) and good binding kinetics for ampicillin sodium. Finally, as solid phase extraction adsorbents, the SMIPs coupled with HPLC method were validated and applied for the enrichment, purification and determination of ampicillin sodium in real milk and blood samples. The averages of spiked accuracy ranged from 92.1%to 107.6%. The relative standard deviations of intra-and inter-day precisions were less than 4.6%. This study provides a new and promising method for enriching, extracting and determining ampicillin sodium in complex biological samples.

Objective:To construct a classification model for endoscopic ultrasonography (EUS) images of gastrointestinal stromal tumors (GISTs) and leiomyomas (LM) based on deep learning technology, and to verify its value for differential diagnosis.Methods:From October 2014 to October 2021, 69 patients of GISTs and 73 of LM who underwent EUS and were pathologically confirmed by surgery or endoscopic resection in the Second Affiliated Hospital of Soochow University were retrospectively studied. One clear EUS image with typical lesion was selected for each case. Using the hold-out method, the images of each disease were divided into the training set and the validation set according to the ratio of the number of images in the training set to the number of images in the validation set, which was 8∶2. Finally, 113 EUS images (55 GISTs and 58 LM) were used to form the training set, and 29 EUS images (14 GISTs and 15 LM) were used to form the validation set. The training set was used to train and optimize the deep learning model, and the validation set was used to verify the classification model. The main observation indicators included the sensitivity, the specificity, the positive predictive value, the negative predictive value and the accuracy of differential diagnosis.Results:The accuracy of the classification model established by Resnet 34 network structure in the differential diagnosis of GISTs and LM tended to be 0.89, better than the classification model established by Resnet 50 network structure (0.81). The sensitivity, the specificity, the positive predictive value, the negative predictive value and the accuracy of the classification model based on Resnet 34 network structure for differentiating EUS images in the validation set were 85.71% (12/14, 95% CI: 67.38%-100.00%), 93.33% (14/15, 95% CI: 80.71%-100.00%), 92.31% (12/13, 95% CI: 77.82%-100.00%), 87.50% (14/16, 95% CI: 71.30%-100.00%) and 89.66% (26/29, 95% CI: 78.57%-100.00%), respectively. Conclusion:It is feasible to use deep learning technology in the differential diagnosis of EUS images of GISTs and LM, which can provide auxiliary diagnostic opinions for clinicians. The deep learning model based on Resnet 34 network structure shows higher accuracy in the differential diagnosis of EUS images of GISTs and LM.