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Objective To explore the application of combination of time-resolved fluorescence immunoassay (TRFIA) and fluorescence quantitative polymerase chain reaction (FQ - PCR) method in detection of HBV markers. Methods From January 2008 to May 2014 in Yunnan province, 2476 cases of patients with suspected chronic hepatitis B in the Second People's Hospital of Yunnan Province were selected in this study. TRFIA was used to determine the HBV serological markers (HBV - m) and line FQ - PcR method was used to detect the HBV. DNA content, then the positive detection rates of the two methods were compared. Results In 2476 cases, positive HBv DNA detected with FQ - PcR was found in 1656, the total positive rate was 66.9%, positive HBsAg, HBeAg and HBcAb detected with TRFIA was found in 861, positive HBv DNA detected with FQ - PcR was found in 816, accounting for 94.8%; Positive HBsAg, HBeAb HBcAb detected with TRFIA was found in 1023 cases, positive HBv DNA detected with FQ - PcR was found in 674, accounting for 65.9%; Positive both HBsAg and HBeAg was found in 27, positive HBv DNA detected with FQ - PcR was found in 26 (96.3%). Positive HBsAg and HBcAb was found in 148, positive HBv DNA detected with FQ - PcR was found in 117, accounting for 79.1%. Positive both HBsAb and HBeAb was found in 55, positive HBv DNA detected with FQ - PcR was found in 2 (3.64%). HBsAb, HBeAb HBcAb were positive in 123, HBV - DNA was positive by FQ - PcR in 11, 8.94%; HBeAb wa positive 22, HBV - DNA was positive by FQ - PcR in 2, 9.09%; HBeAb HBcAb were positive in 43, HBV - DNA was positive by FQ - PcR in 3, accounting for 6.98%; HBcAb was positive in 37, HBV - DNA was positive by FQ - PcR in 2 (5.41%); HBsAb was positive in 89, HBV - DNA was positive by FQ - PcR in 2 (2.22%); All negative was found in 48, HBV - DNA was positive by FQ - PcR in 1, accounting for 2.08%. Conclusions TRFIA method can only give a preliminary estimation in screening hepatitis B virus (HBV) infection, cannot accurately detect the low serum HBV - DNA titer, so TRFIA is not enough for diagnosis of HBV infection. FQ -PCR method can accurately judge the replication of the hepatitis b virus in the body, infectious strength, is a complement to TRFIA method. While FQ - PPCR method in detection is easily affected by some artificial or non-artificial factors. Therefore, combining the two detection methods can improve the accuracy, is advantageous to diagnosis and judgment of HBV infection. [ABSTRACT FROM AUTHOR]

Objective To investigate the association of PGC-1 α gene G1y482Ser single nucleotide polymorphism with type 2 diabetes mellitus (T2DM) in Kunming, Yunnan province. Methods Four hundred and thirty patients with type 2 diabetes mellitus and 431 normal volunteers (NGT) were recruited in the study. Their blood specimens were collected to extract the genomic DNA. PGC-1 α gene Gly482Ser (G>A) types were identified by HRM and DNA direct sequencing. The distribution of genotype and allele in T2DM group were investigated. Results AA genotype and G allele frequencies of PGC-1 α gene GIy482Ser variant were 0.209 and 0.587 in T2DM group, and were 0.139 and 0.651 in NGT group, respectively. There were significant differences for the frequencies between the two groups (P< 0.05) . Conclusion PGC-1 α gene G1y482Ser single nucleotide polymorphism was associated with type 2 diabetes mellitus in Kunming, Yunnan province. [ABSTRACT FROM AUTHOR]

This paper proposed a more flexible and scalable method, which was based on more detailed operation characteristics: API function call name, input parameters in API functions, the two types of the combination of features. It extracted three categories above, defined the concept of the information gain value of malicious code with the help of the entropy in information theory, then, calculated the information gain value of the corresponding API and its parameters in distinguishing the malware and begin software. And then selected the characteristic having higher recognition rate to reduce the number of features and analysis time. Experiment show that, a small amount of feature selection and higher accuracy makes it more superior to the algorithm of API based detection of malware. [ABSTRACT FROM AUTHOR]