Objective:To explore the mechanism of Amomi Fructus in ameliorating ethanol-induced gastric ulcer (GU) in mice using metabolomics, network pharmacology and molecular docking techniques.Methods:The mice were divided into the blank group, model group, aqueous extract of Amomi Fructus group, volatile oil of Amomi Fructus group, combined aqueous extract and volatile oil of Amomi Fructus group and omeprazole group according to the random number table method, with 10 mice in each group. The blank and model groups were gavaged with sodium carboxymethyl cellulose, the Amomi Fructusaqueous extract group was gavaged with 0.152 5 g/kg of Amomi Fructus aqueous extract, the Amomi Fructus volatile oil group was gavaged with 26 μl/kg of Amomi Fructus volatile oil, the Amomi Fructus aqueous extract and volatile oil combined group was gavaged with 0.152 5 g/kg+26 μl/kg of Amomi Fructus aqueous extract and volatile oil synergistic solution, and the omeprazole group was gavaged with 5.2 mg/kg of omeprazole, 1 time/day, which was administered continuously for 7 d. The gastric ulcer model was established by using ethanol 2 h after the last administration, and the pathological changes of gastric histology were observed by using HE staining; the main differential metabolites were detected by UPLC-Q-TOF-MS/MS non-targeted metabolomics technique, and the metabolic pathway enrichment analysis was carried out; the potential targets and key pathways of the anti-GU action of Amomi Fructus were predicted by network pharmacology; the "metabolite-response-enzyme-gene" network was established by combining the serum metabolomics and network pharmacology; and the key targets were verified by molecular docking technology.Results:HE staining showed that the gastric mucosa of mice in the model group was severely damaged, with cellular tissue damage and inflammatory cell infiltration, whereas the drug administration group showed some protective effects; the results of non-targeted metabolomics showed that 2 metabolites were up-regulated and 17 metabolites were down-regulated in sera of mice in the co-administration group of aqueous extract and volatile oil of Amomi Fructus compared with the control group, and the 19 metabolites were strongly correlated and well clustered, involving nicotinic acid and nicotinamide metabolism, citric acid cycle, glyoxylate and dicarboxylic acid metabolism, phenylalanine metabolism, alanine, aspartate and glutamate metabolism and other metabolic pathways; the results of network pharmacology showed that Amomi Fructus improved GU by affecting target proteins, such as STAT3, AKT1, SRC, and TLR4, which were closely linked to the signaling pathways of cancer pathway, human cytomegalovirus infection, and lipids and atherosclerosis; the joint analysis of network pharmacology and the combined analysis of network pharmacology and metabolomics identified the glycerophospholipid metabolic pathway as the main metabolic pathway in which Amomi Fructus may exert gastroprotective effects; the molecular docking results showed that the main active component of quercetin had a better binding ability to the key targets.Conclusion:Amomi Fructus exerts a protective effect on ethanol induced GU model by regulating the glycerophospholipid metabolism pathway, providing theoretical basis for further research on Amomi Fructus.

Objective:To propose a method of cervical cytology screening based on deep convolutional neural network and compare it with the diagnosis of cytologists.Method:The deep segmentation network was used to extract 618 333 regions of interest (ROI) from 5, 516 cytological pathological images. Combined with the experience of physicians, the deep classification network with the ability to analyze ROI was trained. The classification results were used to construct features, and the decision model was used to complete the classification of cytopathological images.Results:The sensitivity and specificity were 89.72%, 58.48%, 33.95% and 95.94% respectively. Among the smears derived from four different preparation methods, this algorithm had the best effect on natural fallout with a sensitivity of 91.10%, specificity of 69.32%, positive predictive rate of 41.41%, and negative predictive rate of 97.03%.Conclusion:Deep convolutional neural network image recognition technology can be applied to cervical cytology screening.

Objective To compare the efficacy of proximal femoral anti-rotation nail and artificial femoral head replacement with bone cement in the treatment of intertrochanteric fracture of femur in elderly patients.Methods70 elderly patients with intertrochanteric fracture of femur were selected as the research subjects,and they were randomly divided into the observation group and the control group according to the digital table,35 cases in each group.The observation group was treated with proximal femoral anti-rotation nail,the control group was treated with artificial femoral head replacement with bone cement.The clinical efficacy of the two groups was observed and compared.Results The operation time,intraoperative bleeding volume of the observation group were (52.63±13.72)min,(115.26±35.17)mL,respectively,which were significantly better than those of the control group[(86.69±21.15)min,(328.72±47.62)mL,t=3.98,9.86,all P<0.05].The time of standing and walking after operation in the observation group were (12.36±3.61)d,(17.51±3.60)d,which were longer than those of the control group[(9.42±3.18)d,(15.18±3.55)d],but the differences were not statistically significant(t=1.36,1.08,all P>0.05).There was no significant difference in the Harris hip score 30 months after operation between the two groups(t=0.18,P>0.05),while the Harris hip score of the observation group after 6 and 12 months were higher than those of the control group,the differences were statistically significant(t=9.04,3.75,all P<0.05).ConclusionCompared with artificial femoral head replacement with bone cement,proximal femoral anti-rotation nail for elderly patients with intertrochanteric fracture of femur has more significant effect,it is worthy of clinical promotion.