Objective To understand the phenotype and enzyme genotype of pan‐drug resistant carbapenemase‐producing Acine‐tobacter baumannii to provide the evidence for clinical rational use of antibiotics and monitoring hospital infection .Methods A total of 117 clinically isolated strains of Acinetobacter baumannii were collected and performed the routine microbiological detection . Multi‐drug resistant Acinetobacter baumannii was screened by K‐B disk diffusion method .The phenotype of carbapenemase‐produ‐cing strains was detected by using the Carba NP colorimetry and modified Hodge test .The drug resistant genotype of multi‐drug re‐sistant Acinetobacter baumannii was verified by PCR .Results Among clinically isolated 117 strains of Acinetobacter baumannii ,64 strains were multi‐drug resistant Acinetobacter baumannii ,in which 33 strains were carbapenemase positive .OXA‐23 drug‐resistant genotype of carbapenemase was detected by PCR ,while IMP ,VIM and NDM‐1 drug resistant genes were not detected .Conclusion The CarbaNP method can rapidly detect carbapenemase‐producing strains with the advantages of strong sensitivity and simple oper‐ation ,which conduces to improve the detection rate of carbapenemase‐producing strains and monitor the nosocomial infection .

Objective:To propose a model using the maximum intensity projection (MIP) of lung field computed tomography (CT) images and deep convolution neural network (CNN) and explore its value in identifying chronic obstructive pulmonary disease (COPD).Methods:A total of 201 subjects were selected from the Second Hospital of Dalian Medical University from January 2010 to May 2021. All subjects were included according to the inclusion criteria and were divided into COPD group (101 cases) and healthy controls group (100 cases). Each patient underwent a high-resolution CT scan of the chest and pulmonary function test. First, the lung field was extracted from CT images and the intrapulmonary MIP images were acquired. Second, with these MIP images as input, the model for identifying COPD was constructed based on a modified residual network (ResNet). Finally, the influence of the number of residual blocks on the performance of the models was investigated. Accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to evaluate the identification efficiency.Results:The accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) of ResNet26 was 76.1%, 76.2%, 76.0%, 76.2%, and 76.0%, respectively; and the AUC of the test was 0.855 (95% CI: 0.799-0.901). The accuracy, sensitivity, specificity, PPV, NPV of ResNet50 was 77.6%, 76.2%, 79.0%, 78.6%, and 76.7%, respectively; and the AUC of the test was 0.854 (95% CI: 0.797-0.900). The accuracy, sensitivity, specificity, PPV, NPV of ResNet26d was 82.1%, 83.2%, 81.0%, 81.6%, and 82.7%, respectively; and the AUC of the test was 0.885 (95% CI: 0.830-0.926). Conclusions:The COPD identification model via MIP images from CT images within the lung and deep CNN is successfully constructed and achieves accurate COPD identification. And it can provide an effective tool for COPD screening.

Objective     To explore the association between the preoperative systemic immune-inflammation index (SII) and prognosis in non-small cell lung cancer (NSCLC) patients. Methods     A comprehensive literature survey was performed on PubMed, Web of Science, EMbase, The Cochrane Library, Wanfang, and CNKI databases to search the related studies from inception to December 2021. The hazard ratio (HR) and 95% confidence interval (CI) were combined to evaluate the correlation of the preoperative SII with overall survival (OS), disease-free survival (DFS), and recurrence-free survival (RFS) in NSCLC patients. Results     A total of 11 studies involving 9 180 patients were eventually included. The combined analysis showed that high SII levels were significantly associated with worse OS (HR=1.61, 95%CI 1.36-1.90, P<0.001), DFS (HR=1.50, 95%CI 1.34-1.68, P<0.001), and RFS (HR=1.17, 95%CI 1.04-1.33, P<0.001). Subgroup analyses also further verified the above results. Conclusion     Preoperative SII is a powerful prognostic biomarker for predicting outcome in patients with operable NSCLC and contribute to prognosis evaluation and treatment strategy formulation. However, more well-designed and prospective studies are warranted to verify our findings.

Objective Effect of hawthorn and melanoidins on the in-vitro growth of Bifidobacterium and E.coli. Methods According to methods of the Chinese pharmacopoeia (2015),the charred hawthorn was prepared. The melanoidins in charred hawthorn were separated and purified by the macroporous resin extraction process. Ultraviolet spectrophotometry was used to detect melanoidins. The gas chromatography was used to detect the effects of hawthorn, charred hawthorn and melanoidins on the content of the acetic acid in Bifidobacterium and E.coli during growth, stable and decay period. Results In the early stage, the effects of hawthorn and charred hawthorn on bacteria were greater than melanoidins. In the middle and late stage, melanoidins inhibited the growth and metabolism of E.coli by changing the generation of acetic acid, and contributed to that of Bifidobacterium and also promoted the generation of acetic acid and regulate the intestinal flora. Conclusion Hawthorn, charred hawthorn and melanoidins all promote digestion by promoting the growth and metabolism of intestinal flora. Among them, charred hawthorn has a better effect on intestinal flora.