4 results on '"Xia KJ"'
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2. Ginsenoside Rb1 alleviates colitis in mice via activation of endoplasmic reticulum-resident E3 ubiquitin ligase Hrd1 signaling pathway.
- Author
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Dong JY, Xia KJ, Liang W, Liu LL, Yang F, Fang XS, Xiong YJ, Wang L, Zhou ZJ, Li CY, Zhang WD, Wang JY, and Chen DP
- Subjects
- Animals, Apoptosis drug effects, Cell Line, Colitis chemically induced, Colitis pathology, Colon metabolism, Cytokines metabolism, Dextran Sulfate, Humans, Mice, Mice, Inbred C57BL, Signal Transduction drug effects, Sulfasalazine pharmacology, Colitis drug therapy, Endoplasmic Reticulum metabolism, Ginsenosides pharmacology, Ubiquitin-Protein Ligases metabolism
- Abstract
Endoplasmic reticulum (ER) homeostasis is regulated by ER-resident E3 ubiquitin ligase Hrd1, which has been implicated in inflammatory bowel disease (IBD). Ginsenoside Rb1 (GRb1) is the major ginsenoside in ginseng with multiple pharmacological activities. In this study we investigated the role of Hrd1 in IBD and its regulation by GRb1. Two mouse colitis models were established to mimic human IBD: drinking water containing dextran sodium sulfate (DSS) as well as intra-colonic infusion of 2, 4, 6-trinitrobenzene sulfonic acid (TNBS). Colitis mice were treated with GRb1 (20, 40 mg·kg
-1 ·d-1 , ig) or a positive control drug sulfasalazine (500 mg·kg-1 ·d-1 , ig) for 7 days. The model mice showed typical colitis symptoms and pathological changes in colon tissue. In addition to significant inflammatory responses and cell apoptosis in colon tissue, colon epithelial expression of Hrd1 was significantly decreased, the expression of ER stress markers GRP78, PERK, CHOP, and caspase 12 was increased, and the expression of Fas was increased (Fas was removed by Hrd1-induced ubiquitination). These changes were partially, or completely, reversed by GRb1 administration, whereas injection of Hrd1 inhibitor LS102 (50 mg·kg-1 · d-1 , ip, for 6 days) exacerbated colitis symptoms in colitis mice. GRb1 administration not only normalized Hrd1 expression at both the mRNA and protein levels, but also alleviated the ER stress response, Fas-related apoptosis, and other colitis symptoms. In intestinal cell line IEC-6, the expression of Hrd1 was significantly decreased by LPS treatment, but was normalized by GRb1 (200 μM). GRb1 alleviated LPS-induced ER stress and cell apoptosis in IEC-6 cells, and GRb1 action was inhibited by knockdown of Hrd1 using small interfering RNA. In summary, these results reveal a pathological role of Hrd1 in colitis, and provide a novel insight into alternative treatment of colitis using GRb1 activating Hrd1 signaling pathway., (© 2020. CPS and SIMM.)- Published
- 2021
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3. Textural differences based on apparent diffusion coefficient maps for discriminating pT3 subclasses of rectal adenocarcinoma.
- Author
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Lu ZH, Xia KJ, Jiang H, Jiang JL, and Wu M
- Abstract
Background: The accuracy of discriminating pT3a from pT3b-c rectal cancer using high-resolution magnetic resonance imaging (MRI) remains unsatisfactory, although texture analysis (TA) could improve such discrimination., Aim: To investigate the value of TA on apparent diffusion coefficient (ADC) maps in differentiating pT3a rectal adenocarcinomas from pT3b-c tumors., Methods: This was a case-control study of 59 patients with pT3 rectal adenocarcinoma, who underwent diffusion-weighted imaging (DWI) between October 2016 and December 2018. The inclusion criteria were: (1) Proven pT3 rectal adenocarcinoma; (2) Primary MRI including high-resolution T2-weighted image (T2WI) and DWI; and (3) Availability of pathological reports for surgical specimens. The exclusion criteria were: (1) Poor image quality; (2) Preoperative chemoradiation therapy; and (3) A different pathological type. First-order (ADC values, skewness, kurtosis, and uniformity) and second-order (energy, entropy, inertia, and correlation) texture features were derived from whole-lesion ADC maps. Receiver operating characteristic curves were used to determine the diagnostic value for pT3b-c tumors., Results: The final study population consisted of 59 patients (34 men and 25 women), with a median age of 66 years (range, 41-85 years). Thirty patients had pT3a, 24 had pT3b, and five had pT3c. Among the ADC first-order textural differences between pT3a and pT3b-c rectal adenocarcinomas, only skewness was significantly lower in the pT3a tumors than in pT3b-c tumors. Among the ADC second-order textural differences, energy and entropy were significantly different between pT3a and pT3b-c rectal adenocarcinomas. For differentiating pT3a rectal adenocarcinomas from pT3b-c tumors, the areas under the curves (AUCs) of skewness, energy, and entropy were 0.686, 0.657, and 0.747, respectively. Logistic regression analysis of all three features yielded a greater AUC (0.775) in differentiating pT3a rectal adenocarcinomas from pT3b-c tumors (69.0% sensitivity and 83.3% specificity)., Conclusion: TA features derived from ADC maps might potentially differentiate pT3a rectal adenocarcinomas from pT3b-c tumors., Competing Interests: Conflict-of-interest statement: No benefits in any form have been received or will be received from a commercial party related directly or indirectly to the subject of this article., (©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.)
- Published
- 2021
- Full Text
- View/download PDF
4. Deep Semantic Segmentation of Kidney and Space-Occupying Lesion Area Based on SCNN and ResNet Models Combined with SIFT-Flow Algorithm.
- Author
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Xia KJ, Yin HS, and Zhang YD
- Subjects
- Humans, Pattern Recognition, Automated, Tomography, X-Ray Computed, Diagnosis, Computer-Assisted methods, Image Processing, Computer-Assisted methods, Kidney diagnostic imaging, Machine Learning, Neural Networks, Computer
- Abstract
Renal segmentation is one of the most fundamental and challenging task in computer aided diagnosis systems. In order to overcome the shortcomings of automatic kidney segmentation based on deep network for abdominal CT images, a two-stage semantic segmentation of kidney and space-occupying lesion area based on SCNN and ResNet models combined with SIFT-flow transformation is proposed in paper, which is divided into two stages: image retrieval and semantic segmentation. To facilitate the image retrieval, a metric learning-based approach is firstly adopted to construct a deep convolutional neural network structure using SCNN and ResNet network to extract image features and minimize the impact of interference factors on features, so as to obtain the ability to represent the abdominal CT scan image with the same angle under different imaging conditions. And then, SIFT Flow transformation is introduced, which adopts MRF to fuse label information, priori spatial information and smoothing information to establish the dense matching relationship of pixels so that the semantics can be transferred from the known image to the target image so as to obtain the semantic segmentation result of kidney and space-occupying lesion area. In order to validate effectiveness and efficiency of our proposed method, we conduct experiments on self-establish CT dataset, focus on kidney organ and most of which have tumors inside of the kidney, and abnormal deformed shape of kidney. The experimental results qualitatively and quantitatively show that the accuracy of kidney segmentation is greatly improved, and the key information of the proportioned tumor occupying a small area of the image are exhibited a good segmentation results. In addition, our algorithm has also achieved ideal results in the clinical verification, which is suitable for intelligent medicine equipment applications.
- Published
- 2018
- Full Text
- View/download PDF
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