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Objective To establish a mouse model of type Ⅰ tricho-hepato-enteric syndrome(THES)induced by Ttc37 deficiency.Methods Ttc37 flox strain was established by site-specifically inserted loxP sites into Ttc37 gene via CRISPR/CAS9 technology.Ubiquitously expressed CAG-Cre was introduced for all-tissue removal of Ttc37 in Ttc37flox/flox;CAG-Cre mice.The knock-out effect was confirmed by fluorescence quantitative PCR and Western blot.Phenotypic evaluations were conducted in 8-week-old mice including hematoxylin-eosin staining of skin,spleen,liver,bladder,and gastrointestinal tract(GI),serum enzyme activity assay of aspartate aminotransferase(AST)and alanine aminotransferase(ALT),measurement of serum hemoglobin level,and ELISA for IgG and IgM level upon antigen immunization.Results Similar to type Ⅰ THES patients,Ttc37flox/flox;CAG-Cre mice exhibited impaired development of hair shaft,epidermis,B cell and eyes,while liver,GI,bladder and serum hemoglobin level seemed normal under unstressed condition.Conclusion A novel mouse model of typeⅠ THES was constructed successfully,which was applicable for pathological study.
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The new coronavirus, which has become a global pandemic, has confirmed more than 88 million cases worldwide since the first case was recorded in December 2019, causing over 1.9 million deaths. Since COIVD-19 lesions have clear imaging features on CT images, it is suitable for the auxiliary diagnosis and treatment of COVID-19. Deep learning can be used to segment the lesions areas of COVID-19 in CT images to help monitor the epidemic situation. In this paper, we propose a multi-point supervision network (MPS-Net) for segmentation of COVID-19 lung infection CT image lesions to solve the problem of a variety of lesion shapes and areas. A multi-scale feature extraction structure, a sieve connection structure (SC), a multi-scale input structure and a multi-point supervised training structure were implemented into MPS-Net. In order to increase the ability to segment various lesion areas of different sizes, the multi-scale feature extraction structure and the sieve connection structure will use different sizes of receptive fields to extract feature maps of various scales. The multi-scale input structure is used to minimize the edge loss caused by the convolution process. In order to improve the accuracy of segmentation, we propose a multi-point supervision training structure to extract supervision signals from different up-sampling points on the network. Experimental results showed that the dice similarity coefficient (DSC), sensitivity, specificity and IOU of the segmentation results of our model are 0.8325, 0.8406, 09988 and 0.742, respectively. The experimental results demonstrated that the network proposed in this paper can effectively segment COVID-19 infection on CT images. It can be used to assist the diagnosis and treatment of new coronary pneumonia.
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OBJECTIVE@#To explore the antibiotic resistance of Brucella melitensis and instruct rational use of antimicrobial agents in clinical treatment of Brucella infection.@*METHODS@#Bacteria were cultured and identified by BACTEC9120 and VITEK II automicrobic system. E-test was used to detect the minimal inhibitory concentration (MIC) of antimicrobial agents in the drug susceptivity experiment.@*RESULTS@#A total of 19 brucella strains (all Brucella melitensis) were isolated from 19 patients, who had fever between January 2010 and June 2012, and 17 samples were blood, one was bone marrow, the other sample was cerebrospinal fluid. The MIC range of ceftazidime was 2.0-8.0 mg/L, rifampicin was 0.06-2.0 mg/L, amikacin was 4.0-12.0 mg/L, levofloxacin was 2.0-8.0 mg/L, doxycycline was 8.0-32.0 mg/L, sulfamethoxazole-trimethoprim was 4.0-16.0 mg/L, ampicillin was 1.5-2.0 mg/L and gentamicin was 0.50-0.75 mg/L.@*CONCLUSIONS@#The drugs used in this experiment cover common drugs for treating Brucella. Meanwhile, the results are consistent with clinical efficacy. It is suggested personalized regimen according to patients' status in treatment of Brucella.