Evolution of influenza A(H3N2) virus hemagglutinin (HA1) gene in Shangluo, China, 2014-2015 / 中国人兽共患病学报

Comparative analysis of the variations in HA 1 gene of the influenza A (H3N2) virus and the vaccine recommended were conducted in Shangluo city of China,during the surveillance year of 2014-2015.In this study,we collected the samples of H3N2 subtype strain from the Shanglou City of China during the surveillance period of 2014-2015.The strain was cultured in MDCK cells,HA gene fragment was amplified by RT-PCR and the nucleotide sequence was determined.Sequence alignment was performed using the clustax2.1 software.The phylogenetic tree was constructed by Mega6.0 software and was analyzed by Neighboring-joining method.Results showed that the homology of isolated strain during 2014-2015 was 97.2 ％-99.9％ and homology with the recommended vaccine strain A/Texas/50/2012 was 97.3％-98.5％.The amino acid sequence of the HA 1 gene of the isolated strain was compared with that of the vaccine strain.The major antigenic determinants of the isolates in 2014,having mutations were section B,Y159F,S198P,while the major antigenic determinants of isolates in 2015,having amino acid mutations were A zone G142R,B region S159F,S198P.These results indicated that the key antigenic determinant of influenza H3N2 subtype strain in Shangluo City has changed in 2014-2015 and A/Texas/50/2012 vaccine component is no more effective.Hence,there is an urgent need to update the influenza H3N2 subtype vaccine components and in future we should be deeply concerned about the evolution ofinfluenza H3N2 gene trends.

Succession Law of Intestinal Flora after Death in SD Rats / 法医学杂志

OBJECTIVES@#To initially explore the sequential changes in the intestinal flora of corpse for the estimation of postmortem interval （PMI）.@*METHODS@#Rats were sacrificed by cervical dislocation, and samples were taken from their intestines using cotton swab to extract the DNA of intestinal flora. The 16S rRNA V3 universal primers were selected for PCR, and the PCR products were used for denatured gradient gel electrophoresis. The diversity and similarity analysis of intestinal flora were analyzed between groups, and the bands were cut from denaturing gradient gel electrophoresis. After purification, PCR and sequencing, the percentage of major bacteria in each group was obtained.@*RESULTS@#The flora diversity showed a reduced tendency from 1st to 30th day after death （ P<0.05）, while the intra-group similarity showed a downward trend （ P<0.05）. The number of bands and intra-group similarity coefficient （Cs） on the first day was higher than that of other groups （ P<0.05）. The intra-group Cs of the 25th and 30th day had a significant difference compared with the 5th day （ P<0.05）. At the genus level, the intestinal flora was mainly composed of Enterococcus sp. on the 1th and 5th day after death, Bacillus thuringienssis was the dominant species on the 10th, 15th and 20th day, and Enterococcus faecalis became the dominant species on the 25th and 30th day.@*CONCLUSIONS@#The composition and structure of intestinal flora change significantly in rats with the time after death, which indicates that the succession of intestinal flora is related to the postmortem interval.

Current Research and Prospects on Postmortem Interval Estimation / 法医学杂志

The researches on postmortem interval （PMI） estimation are very important and meaningful in forensic science. PMI estimation is also an important issue that must be solved in practice of forensic pathology. There are many defects existing in traditional methods for PMI estimation, so it is imperative to introduce new pathways. With the emergence of various new technologies, the researches on PMI estimation have a tendency from simple to complex with a growth of data. The present review firstly summarizes a series of methods used for PMI estimation, and then gives an outlook for the application of artificial intelligence algorithms in this field.

A Bayesian Stepwise Discriminant Model for Predicting Risk Factors of Preterm Premature Rupture of Membranes: A Case-control Study / 中华医学杂志(英文版)

<p><b>BACKGROUND</b>Preterm premature rupture of membrane (PPROM) can lead to serious consequences such as intrauterine infection, prolapse of the umbilical cord, and neonatal respiratory distress syndrome. Genital infection is a very important risk which closely related with PPROM. The preliminary study only made qualitative research on genital infection, but there was no deep and clear judgment about the effects of pathogenic bacteria. This study was to analyze the association of infections with PPROM in pregnant women in Shaanxi, China, and to establish Bayesian stepwise discriminant analysis to predict the incidence of PPROM.</p><p><b>METHODS</b>In training group, the 112 pregnant women with PPROM were enrolled in the case subgroup, and 108 normal pregnant women in the control subgroup using an unmatched case-control method. The sociodemographic characteristics of these participants were collected by face-to-face interviews. Vaginal excretions from each participant were sampled at 28-36+6 weeks of pregnancy using a sterile swab. DNA corresponding to Chlamydia trachomatis (CT), Ureaplasma urealyticum (UU), Candida albicans, group B streptococci (GBS), herpes simplex virus-1 (HSV-1), and HSV-2 were detected in each participant by real-time polymerase chain reaction. A model of Bayesian discriminant analysis was established and then verified by a multicenter validation group that included 500 participants in the case subgroup and 500 participants in the control subgroup from five different hospitals in the Shaanxi province, respectively.</p><p><b>RESULTS</b>The sociological characteristics were not significantly different between the case and control subgroups in both training and validation groups (all P > 0.05). In training group, the infection rates of UU (11.6% vs. 3.7%), CT (17.0% vs. 5.6%), and GBS (22.3% vs. 6.5%) showed statistically different between the case and control subgroups (all P < 0.05), log-transformed quantification of UU, CT, GBS, and HSV-2 showed statistically different between the case and control subgroups (P < 0.05). All etiological agents were introduced into the Bayesian stepwise discriminant model showed that UU, CT, and GBS infections were the main contributors to PPROM, with coefficients of 0.441, 3.347, and 4.126, respectively. The accuracy rates of the Bayesian stepwise discriminant analysis between the case and control subgroup were 84.1% and 86.8% in the training and validation groups, respectively.</p><p><b>CONCLUSIONS</b>This study established a Bayesian stepwise discriminant model to predict the incidence of PPROM. The UU, CT, and GBS infections were discriminant factors for PPROM according to a Bayesian stepwise discriminant analysis. This model could provide a new method for the early predicting of PPROM in pregnant women.</p>