Histopathological Diagnosis System for Gastritis Using Deep Learning Algorithm / 中国医学科学杂志(英文版)

Objective To develope a deep learning algorithm for pathological classification of chronic gastritis and assess its performance using whole-slide images (WSIs). Methods We retrospectively collected 1,250 gastric biopsy specimens (1,128 gastritis, 122 normal mucosa) from PLA General Hospital. The deep learning algorithm based on DeepLab v3 (ResNet-50) architecture was trained and validated using 1,008 WSIs and 100 WSIs, respectively. The diagnostic performance of the algorithm was tested on an independent test set of 142 WSIs, with the pathologists' consensus diagnosis as the gold standard. Results The receiver operating characteristic (ROC) curves were generated for chronic superficial gastritis (CSuG), chronic active gastritis (CAcG), and chronic atrophic gastritis (CAtG) in the test set, respectively.The areas under the ROC curves (AUCs) of the algorithm for CSuG, CAcG, and CAtG were 0.882, 0.905 and 0.910, respectively. The sensitivity and specificity of the deep learning algorithm for the classification of CSuG, CAcG, and CAtG were 0.790 and 1.000 (accuracy 0.880), 0.985 and 0.829 (accuracy 0.901), 0.952 and 0.992 (accuracy 0.986), respectively. The overall predicted accuracy for three different types of gastritis was 0.867. By flagging the suspicious regions identified by the algorithm in WSI, a more transparent and interpretable diagnosis can be generated. Conclusion The deep learning algorithm achieved high accuracy for chronic gastritis classification using WSIs. By pre-highlighting the different gastritis regions, it might be used as an auxiliary diagnostic tool to improve the work efficiency of pathologists.

PTD-hEGF fusion protein--gene expression and function analysis / 生物工程学报

To produce membrane-permeable human epidermal growth factor (hEGF), a pPTD-hEGF prokaryocyte expression vector was constructed and transformed into E. coli BL 21 (DE3). The PTD-hEGF fusion protein was induced by 0.3 mmol/L of IPTG expressed in the form of inclusion body with an yield of 40% of the total protein in the cells, and then purified by Ni2+ -NTA affinity chromatography. The SDS-PAGE analysis showed a single fusion protein band with a molecular weight of 16 kD. The amino acid sequence was checked by MALDI-TOF-MS analysis. The genetic engineering PTD-hEGF fusion protein obviously promoted the proliferation and growth of the HEK-293 cells in vitro.