Objective To understand the status of Toxoplasma gondii(TOX)infection in pregnant women in Qingdao area. Methods Antibody capture ELISA was used to detect the TOX-IgM,IgG and TOX-DNA in 1 341 pregnant women and 201 infer-tility,miscarriage,stillbirth pregnant women from June 2011 to July 2013. Results Among 201 abnormal pregnant women,43 cases were TOX antibody positive,accounting for 22.39%,and among the normal pregnant women,84 cases were TOX antibody positive,accounting for 6.26%,and there was a significant difference(χ2 =10.60,P<0.05). The TOX positive rate of 20-29 years old women was higher than that of the women aged over 30 years(χ2=21.9,P<0.05). The TOX antibody positive rates of families with pets and families without pets were 26.63%and 8.77%respectively(χ2=10.93,P<0.05). The TOX antibody posi-tive rates were 10.94%(22/201)in unemployed women,3.98%(8/201)in farmers,3.98%(8/201)in staff,and 3.48%(7/201)in workers. Conclusions The TOX infection rate in abnormal pregnant women is significantly higher than that in normal pregnancy women;therefore,the health education should be strengthened. In addition,in young women,especially 20-29 years old women, and some special occupation women,the control measures should be enhanced.

Objective To establish and identify transgenic mice model that specifically express delta drosophila homolog-like 1 (DLK1) in liver for the functional study of DLK1 on liver differentiation,hepatopathy,and hepatocellular carcinoma.Methods The coding sequence of DLK1 cDNA was inserted downstream of mouse albumin gene enhancer/promoter to construct a liver-specific DLK1 expression recombinant vector.The DNA fragment of transgene digested from the recombinant vector by Pine I was transfected to Hepl-6 cells to verify the expression of DLK1 in vitro.Then the transgene fragment was microinjeeted into fertilized eggs of C57×CBA F1 mice and 20 transgenic founder mice were generated.The F1 mice of DLK1 transgenie founder mice were used to identify the expression of the transgene in liver and other tissues.Results RT-PCR and cellular immunofluorescence showed DLK1 expression when the transgene fragment was transfeeted into Hep1-6.For transgenic mice,RT-PCR and immunohistochemistry showed that DLK1 was specifically expressed in adult F1 mice liver.Conclusion Successfully established the liver-specific DLK1 expression transgenic mice model.

To prepare polyclonal antibodies (PcAb) against UspA1 of Moraxella catarrhalis (Mc), we used bioinformatic analysis to determine the surface exposed region in this protein that holds the antigen epitopes. Then the corresponding coding sequences for this fragment was artificially synthesized according to the codon usage of Escherichia coli. The gene fragment was then subcloned into the prokaryotic expression vector pET-28a(+) and expressed in E. coli rosseta (DE3), and then the recombinant UspA1-His proteins were purified. Two New Zealand white rabbits were immunized with this protein to prepare antiserum. The resulting PcAb was then purified from the antiserum with Protein A affinity column. The results of fluorescence antibody assay, enzyme linked immunosorbent assay and Western blotting analysis showed that the PcAb could specifically recognize the surface exposed region of UspA1 on Mc. The preparation of the PcAb laid a foundation of further development of rapid detection technique for M. catarrhalis.

OBJECTIVES: To investigate the reliability and accuracy of deep learning technology in automatic sex estimation using the 3D reconstructed images of the computed tomography (CT) from the Chinese Han population. METHODS: The pelvic CT images of 700 individuals (350 males and 350 females) of the Chinese Han population aged 20 to 85 years were collected and reconstructed into 3D virtual skeletal models. The feature region images of the medial aspect of the ischiopubic ramus (MIPR) were intercepted. The Inception v4 was adopted as the image recognition model, and two methods of initial learning and transfer learning were used for training. Eighty percent of the individuals' images were randomly selected as the training and validation dataset, and the remaining were used as the test dataset. The left and right sides of the MIPR images were trained separately and combinedly. Subsequently, the models' performance was evaluated by overall accuracy, female accuracy, male accuracy, etc. RESULTS: When both sides of the MIPR images were trained separately with initial learning, the overall accuracy of the right model was 95.7%, the female accuracy and male accuracy were both 95.7%; the overall accuracy of the left model was 92.1%, the female accuracy was 88.6% and the male accuracy was 95.7%. When the left and right MIPR images were combined to train with initial learning, the overall accuracy of the model was 94.6%, the female accuracy was 92.1% and the male accuracy was 97.1%. When the left and right MIPR images were combined to train with transfer learning, the model achieved an overall accuracy of 95.7%, and the female and male accuracies were both 95.7%. CONCLUSIONS The use of deep learning model of Inception v4 and transfer learning algorithm to construct a sex estimation model for pelvic MIPR images of Chinese Han population has high accuracy and well generalizability in human remains, which can effectively estimate the sex in adults.
Adult ; Female ; Humans ; Male ; Deep Learning ; Imaging, Three-Dimensional ; Pelvis ; Reproducibility of Results ; Tomography, X-Ray Computed ; Young Adult ; Middle Aged ; Aged ; Aged, 80 and over