Objective:To investigate the clinical effect and mechanisms of ceftriaxone combined with ranitidine on the acute pancreatitis.Methods:92 cases of patients with acute pancreatitis were selected and randomly divided into the control group (n=46) and experimental group (n=46),the control group was treated with ceftriaxone,and the experimental group was treated with ranitidine based on the control group,the serum levels of intedeukin-6 (IL-6),c-reactive protein(CRP),platelet activating factor (PAF),superoxide dismutase (SOD),propylene glycol (MDA),gastric secrete element,stomach,heart rate (HR),mean arterial pressure (MAP),and the relief time of clinical manifestation and the clinical efficacy were observed and compared between the two groups.Results:After treatment,the serum levels ofIL-6,CRP,PAF,MDA,gastric secrete element and HR of experimental group were significantly lower than those of the control group (P＜0.05).The serum levels of SOD,stomach motion element and MAP of experimental group were higher than those of the control group (P＜0.05).The relief time of clinical manifestation and total efficiency of experimental group were better than those of the control group (P＜0.05).Conclusions:Ceftriaxone combined with ranitidine could effectively enhance the clinical efficacy of acute pancreatitis,which might be related to the anti-oxidation and anti-inflammation.

21 8 patients with acute pulpitis were randomly divided into 2 groups.1 09 cases were treated by Depulpin inactivation agent (group DI)for emergency management,another 1 09 cases were treated by pulp drainage with phenol camphor cotton ball(group CP).The effective analgesia rate of DI and CP group was 95.4% and 69.7% respectively(P<0.001 ).

Accurate identification of compound-protein interactions (CPIs) in silico may deepen our understanding of the underlying mechanisms of drug action and thus remarkably facilitate drug discovery and development. Conventional similarity-or docking-based computational methods for predicting CPIs rarely exploit latent features from currently available large-scale unlabeled com-pound and protein data and often limit their usage to relatively small-scale datasets. In the present study, we propose DeepCPI, a novel general and scalable computational framework that combines effective feature embedding (a technique of representation learning) with powerful deep learning methods to accurately predict CPIs at a large scale. DeepCPI automatically learns the implicit yet expressive low-dimensional features of compounds and proteins from a massive amount of unla-beled data. Evaluations of the measured CPIs in large-scale databases, such as ChEMBL and Bind-ingDB, as well as of the known drug-target interactions from DrugBank, demonstrated the superior predictive performance of DeepCPI. Furthermore, several interactions among small-molecule compounds and three G protein-coupled receptor targets (glucagon-like peptide-1 recep-tor, glucagon receptor, and vasoactive intestinal peptide receptor) predicted using DeepCPI were experimentally validated. The present study suggests that DeepCPI is a useful and powerful tool for drug discovery and repositioning. The source code of DeepCPI can be downloaded from https://github.com/FangpingWan/DeepCPI.

Objective To investigate the expression of SLC35A2 and PFDN2 in breast cancer and their relationship with clinical indicators and prognosis.Methods TCGA database and TIMER 2.0 database were used to analyze the differences of SLC35A2 and PFDN2 expression in breast cancer tissues and paracancerous tissues;K-M database was used to create the survival curves of patients in the high and low expression groups of the two.qRT-PCR and immunohistochemistry were used to detect the expression of SLC35A2 and PFDN2 in the cancerous and paracancerous tissues,and the expression differences,the relationship between their expression levels and the clinical observation indexes were statistically analyzed,and the independent prognostic factors of breast cancer were screened out;K-M survival analysis was used to compare the prognostic differences between the groups and create the survival curves.Results The expression levels of SLC35A2 and PFDN2 in breast cancer tissues were significantly higher than those in paracancerous tissues according to the results of biopsy,qRT-PCR and immuno-histochemistry,and the expression levels of SLC35A2 were significantly correlated with lymph node metastasis,while the expression of PFDN2 was significantly correlated with the diameter of the tumor and the metastasis of lymph nodes,and the expression of SLC35A2 and PFDN2 was an independent prognostic factor for breast cancer.patients had the worst prognosis.Conclusion The expression of SLC35A2 and PFDN2 in breast cancer tissues was closely related to clinical indicators and prognosis of breast cancer,and could be used as a potential target for breast cancer treatment.