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Qiao Zhong,Weidong Xu,Yuanjian Wu,Hongxing Xu 대한진단검사의학회 2012 Annals of Laboratory Medicine Vol.32 No.6
Background: This study was aimed to investigate the genetic diversity and antibiotic resistance profile of the nosocomial infection agent Acinetobacter baumannii from a medical intensive care unit (ICU) in a teaching hospital in Suzhou, China. Methods: The genetic relationship among A. baumannii isolates in an ICU was investigated using multilocus sequence typing (MLST). The antibiotic resistance pattern was determined by performing an antibiotic susceptible test, which included an agar dilution method and an E-test method. Resistant determinants, e.g., carbapenemase genes, metallo-β- lactamases, and class 1 integron, were analyzed by specific PCR and DNA sequencing. Results: In the present study, 33 non-duplicate isolates were identified as 5 existing sequence types (STs) (ST92, ST75, ST112, ST145, and ST345) and 1 new sequence type STn, which has a G-A mutation at nt268 on ropD40 of ST251. These results reveal limited diversity in carbapenem non-susceptible A. baumannii (CNSAb) isolates in our ICU, which are comprised of only 2 distinct STs, with ST92 and ST75 clustering into a clonal complex (CC) 92. Most CNSAb isolates (94.4%, 17/18) harbored the OXA-23 gene, while no carbapenem-susceptible A. baumannii (CSAb) isolates harbored it. In addition, 66.7% (22/33) isolates were positive for class 1 integrase, and gene cassette analysis showed there are 3 gene arrays among them, i.e., aacA4-catB8-aadA1 (77.3%, 17/22), aacA4 (22.7%, 5/22), and aacC1-orfX-orfX’-aadA1 (4.5%, 1/22). Conclusions: When all these data are combined, the antibiotic resistance and wide distribution of CNSAb isolates in our ICU are probably caused by expansion of the CC92 clone. Background: This study was aimed to investigate the genetic diversity and antibiotic resistance profile of the nosocomial infection agent Acinetobacter baumannii from a medical intensive care unit (ICU) in a teaching hospital in Suzhou, China. Methods: The genetic relationship among A. baumannii isolates in an ICU was investigated using multilocus sequence typing (MLST). The antibiotic resistance pattern was determined by performing an antibiotic susceptible test, which included an agar dilution method and an E-test method. Resistant determinants, e.g., carbapenemase genes, metallo-β- lactamases, and class 1 integron, were analyzed by specific PCR and DNA sequencing. Results: In the present study, 33 non-duplicate isolates were identified as 5 existing sequence types (STs) (ST92, ST75, ST112, ST145, and ST345) and 1 new sequence type STn, which has a G-A mutation at nt268 on ropD40 of ST251. These results reveal limited diversity in carbapenem non-susceptible A. baumannii (CNSAb) isolates in our ICU, which are comprised of only 2 distinct STs, with ST92 and ST75 clustering into a clonal complex (CC) 92. Most CNSAb isolates (94.4%, 17/18) harbored the OXA-23 gene, while no carbapenem-susceptible A. baumannii (CSAb) isolates harbored it. In addition, 66.7% (22/33) isolates were positive for class 1 integrase, and gene cassette analysis showed there are 3 gene arrays among them, i.e., aacA4-catB8-aadA1 (77.3%, 17/22), aacA4 (22.7%, 5/22), and aacC1-orfX-orfX’-aadA1 (4.5%, 1/22). Conclusions: When all these data are combined, the antibiotic resistance and wide distribution of CNSAb isolates in our ICU are probably caused by expansion of the CC92 clone.
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Dongsheng Wu,Yuanjian Wu,Liuhong Wang,Weidong Xu,Qiao Zhong 대한진단검사의학회 2014 Annals of Laboratory Medicine Vol.34 No.1
Background: The dramatic increase in use of the IgG test for toxoplasma, rubella, cyto- megalovirus (CMV), and herpes simplex virus (HSV) [TORCH] has led to the requirement for a high-efficiency method that can be used in the clinical laboratory. This study aimed to compare the results of BGI-Array ELISA TORCH IgG (BGI-GBI, China) screening method to those of Virion/Serion TORCH IgG ELISA (Virion/Serion, Germany). Methods: Serum specimens (n=400) submitted for routine IgG testing by Virion/Serion ELISA were also tested using the BGI-Array ELISA method. The agreements of these two kinds of method were analyzed by κ-coefficients calculation. Results: Following repeat testing, the BGI-Array ELISA TORCH IgG assays demonstrated agreements of 99.5% (398/400 specimens), 98% (392/400 specimens), 99% (396/400 specimens), and 99.5% (398/400 specimens), respectively. The BGI-Array ELISA IgG as- says provided results comparable to Virion/Serion ELISA results, with κ-coefficients show- ing near-perfect agreement for the HSV (κ=0.87), rubella (κ=0.92) and CMV (κ=0.93) and substantial agreement for the toxoplasma (κ=0.80) IgG assays. The use of the BGI- Array ELISA TORCH IgG assays could reduce the turnaround time (1.5 hr vs. 5 hr by Vi- rion/Serion ELISA for 100 specimens) and were easy to use. Conclusions: BGI-Array ELISA TORCH IgG shows a good agreement with Virion/Serion ELISA methods and is suitable for clinical application.
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Yang Xu,Jin Zhao,Fangqiao Hu,Weidong Qiao,Weida Zhai,Yuequan Bao,Hui Li 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.29 No.1
Despite recent breakthroughs in deep learning and computer vision fields, the pixel-wise identification of tiny objects in high-resolution images with complex disturbances remains challenging. This study proposes a modified U-net for tiny crack segmentation in real-world steel-box-girder bridges. The modified U-net adopts the common U-net framework and a novel Self-Attention-Self-Adaption (SASA) neuron as the fundamental computing element. The Self-Attention module applies softmax and gate operations to obtain the attention vector. It enables the neuron to focus on the most significant receptive fields when processing large-scale feature maps. The Self-Adaption module consists of a multiplayer perceptron subnet and achieves deeper feature extraction inside a single neuron. For data augmentation, a grid-based crack random elastic deformation (CRED) algorithm is designed to enrich the diversities and irregular shapes of distributed cracks. Grid-based uniform control nodes are first set on both input images and binary labels, random offsets are then employed on these control nodes, and bilinear interpolation is performed for the rest pixels. The proposed SASA neuron and CRED algorithm are simultaneously deployed to train the modified U-net. 200 raw images with a high resolution of 4928 × 3264 are collected, 160 for training and the rest 40 for the test. 512 × 512 patches are generated from the original images by a sliding window with an overlap of 256 as inputs. Results show that the average IoU between the recognized and ground-truth cracks reaches 0.409, which is 29.8% higher than the regular U-net. A five-fold cross-validation study is performed to verify that the proposed method is robust to different training and test images. Ablation experiments further demonstrate the effectiveness of the proposed SASA neuron and CRED algorithm. Promotions of the average IoU individually utilizing the SASA and CRED module add up to the final promotion of the full model, indicating that the SASA and CRED modules contribute to the different stages of model and data in the training process.
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