OBJECTIVE To investigate the distribution and the resistance variance tendency of the pathogenic bacteria isolated from the blood cultureing specimens collected during the period of 2002-2005.METHODS A retrospective analysis was made to the blood cultureing results during the period of 2002-2005 with WHONET 5.1 software.RESULTS Gram-negative rods were the predominant bacteria which caused sepsicemia,the isolated rates of Escherichia coli,Klebsiella pneumoniae and Staphylococcus aureus were the most high during the period of 2002-2004 but the S.epidermidis and S.hominis were also the important pathogenic ones which caused blood stream infection.Vancomycin and the teicoplanin were the most effective to the Gram-positive bacteria,and the imipenem and the cefepime were the most effective to the Gram-negative ones.CONCLUSIONS It′s important to strengthen the blood cultureing for blood stream infection patient.

Objective To study the potency of transdifferentiated renal tubular epithelial cells for acquiring the tissue stem cells during renal fibrosis.Methods The in vitro cellular model of renal tubular epithelial cells(NRK-52E)transdifferentiation under the inflammatory environment of the local renin-angiotensin (AngⅡ)system was established.The expression and change situation of the embryonic kidney developmental gene Pax2 and the tissue stem cell surface marker CD133 were observed.Results Local high concentration of AngⅡcould stimulate the NRK-52E cells to express Pax2 and CD133 molecule,its effect demonstrated the dose-and time-dependent relation.Conclusion The inflammatory damage leads to the transdifferentiated renal tubular epithelial cells po-tency to acquire the tissue stem cell.

Heart failure is a disease that seriously threatens human health and has become a global public health problem. Diagnostic and prognostic analysis of heart failure based on medical imaging and clinical data can reveal the progression of heart failure and reduce the risk of death of patients, which has important research value. The traditional analysis methods based on statistics and machine learning have some problems, such as insufficient model capability, poor accuracy due to prior dependence, and poor model adaptability. In recent years, with the development of artificial intelligence technology, deep learning has been gradually applied to clinical data analysis in the field of heart failure, showing a new perspective. This paper reviews the main progress, application methods and major achievements of deep learning in heart failure diagnosis, heart failure mortality and heart failure readmission, summarizes the existing problems and presents the prospects of related research to promote the clinical application of deep learning in heart failure clinical research.
Humans ; Artificial Intelligence ; Deep Learning ; Heart Failure/diagnosis* ; Machine Learning ; Diagnostic Imaging