Effect of Xuanfuhua decoction on a mouse model of nonalcoholic steatohepatitis induced by high-fat, high-fructose, and high-cholesterol diet / 临床肝胆病杂志

Objective To investigate the intervention effect of Xuanfuhua decoction on mice with nonalcoholic steatohepatitis (NASH) induced by high-fat, high-fructose, and high-cholesterol diet. Methods A total of 32 male C57/BL6J mice were randomly divided into normal group, model group, Xuanfuhua decoction group, and obeticholic acid group, with 8 mice in each group. Since week 24 of modeling using high-fat, high-fructose, and high-cholesterol diet, each group was given the corresponding drug for intervention at a dose of 14.19 g/kg by gavage for the Xuanfuhua decoction group and 10 mg/kg by gavage for the obeticholic acid group and a volume of 20 mL/kg for gavage, once a day for 6 consecutive weeks. HE staining, oil red O staining, Sirius Red staining, and Masson staining were used to observe the pathological changes, lipid deposition, and collagen deposition of liver tissue; related kits were used to measure the serum levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and glucose, as well as the content of TG and hydroxyproline (Hyp) in liver tissue; quantitative real-time PCR was used to measure the expression of genes associated with lipid metabolism, inflammation, and fibrosis in liver tissue; immunohistochemical staining was used to observe the positive expression of F4/80 and α-SMA in liver tissue. A one-way analysis of variance was used for comparison of continuous data between multiple groups, and the least significant difference t -test was used for further comparison between two groups. Results Compared with the normal group, the model group had significant increases in body weight, liver wet weight, and serum levels of AST, ALT, TC, TG, LDL-C and glucose (all P < 0.01). HE staining showed hepatocyte steatosis, a large number of fat vacuoles, hepatocyte ballooning degeneration, and inflammatory cell infiltration in liver tissue of the mice in the model group, and the model group had a significant increase in NAFLD activity score (NAS) compared with the normal group ( P < 0.01). Oil red O staining showed the deposition of a large number of red lipid droplets with different sizes in hepatocytes of the mice in the model group, and compared with the normal group, the model group had significant increases in the area percentage of oil red O staining and the content of TG in the liver ( P < 0.01). Sirius Red staining and Masson staining showed significant collagen fiber hyperplasia in the perisinusoidal area, the central vein, and the portal area in the model group, and the model group had a significant increase in the content of Hyp in liver tissue compared with the normal group ( P < 0.05). Compared with the model group, the Xuanfuhua decoction group had significant reductions in the serum levels of AST, ALT, TC, TG, LDL-C, and glucose (all P < 0.05), significant improvements in hepatic steatosis, inflammatory infiltration, lipid droplet deposition, and collagen fiber hyperplasia, and significant reductions in NAS score, area percentage of oil red O staining, and content of TG and Hyp in the liver (all P < 0.05). Compared with the normal group, the model group had significant increases in the mRNA expression levels of lipid metabolism-related genes (SREBP-1c, FASN, SCD-1, PPAR-γ, and CD36), inflammation-related genes (F4/80, TNF-α, CCL2, and CD11b), and the fibrosis-related gene α-SMA (all P < 0.05), and immunohistochemical staining showed significant increases in the positive expression of F4/80 and α-SMA ( P < 0.01). Compared with the model group, the Xuanfuhua decoction group had significant reductions in the mRNA expression levels of SREBP-1c, FASN, SCD-1, PPAR-γ, CD36, F4/80, TNF-α, CCL2, CD11b, and α-SMA in liver tissue (all P < 0.05), and immunohistochemical staining showed significant reductions in the positive expression of F4/80 and α-SMA ( P < 0.01). Conclusion Xuanfuhua decoction has a good intervention effect on mice with NASH induced by high fat, high fructose, and high-cholesterol diet and can significantly inhibit hepatic lipid deposition, inflammatory response, and liver fibrosis.

A detection model of colorectal polyps based on YOLO and ResNet deep convolutional neural networks (with video) / 中华消化内镜杂志

Objective:To establish a deep convolutional neural network (DCNN) model based on YOLO and ResNet algorithm for automatic detection of colorectal polyps and to test its function.Methods:Colonoscopy images and videos collected from the database of Digestive Endoscopy Center of Renmin Hospital of Wuhan University from January 2018 to March 2019 were divided into three databases (database 1, 3, 4). The public database CVC-ClinicDB (composed of 612 polyp images extracted from 29 colonoscopy videos provided by Barcelona Hospital, Spain) was used as the database 2. Database 1 (4 700 colonoscopy images from January 2018 to November 2018, including 3 700 intestinal polyp images and 1 000 non-polyp images) was used for establishing training and verifying the DCNN model. Database 2 (CVC-ClinicDB) and database 3 (720 colonoscopy images from January 2019 to March 2019, including 320 intestinal polyp images and 400 non-polyp images) were used for testing the DCNN model on image detection. Database 4 (15 colonoscopy videos in December 2019, containing 33 polyps) was used for testing the DCNN model on video detection. The sensitivity, specificity, accuracy and false positive rate of the DCNN model for detecting intestinal polyps were calculated.Results:The sensitivity of the DCNN model for detecting intestinal polyps in database 2 was 93.19% (602/646). In database 3, the DCNN model showed the accuracy of 95.00% (684/720), sensitivity of 98.13% (314/320), specificity of 92.50% (370/400), and false positive rate of 7.50% (30/400) for detecting intestinal polyps. In database 4, the DCNN model achieved a per-polyp-sensitivity of 100.00% (33/33), a per-image-accuracy of 96.29% (133 840/138 998), a per-image-sensitivity of 90.24% (4 066/4 506), a per-image-specificity of 96.49% (129 774/134 492), and a per-image-false positive rate of 3.51% (4 718/134 492).Conclusion:The DCNN model constructed in the study has a high sensitivity and specificity for automatic detection of colorectal polyps both in the colonoscopy images and videos, has a low false positive rate in the videos, and has the potential to assist endoscopists in diagnosis of colorectal polyps.

Artificial intelligence-assisted diagnosis system of benign and malignant gastric ulcer based on deep learning / 中华消化内镜杂志

Objective:To construct an artificial intelligence-assisted diagnosis system to detect gastric ulcer lesions and identify benign and malignant gastric ulcers automatically.Methods:A total of 1 885 endoscopy images were collected from November 2016 to April 2019 in the Digestive Endoscopy Center of Renmin Hospital of Wuhan University. Among them, 636 were normal images, 630 were with benign gastric ulcers, and 619 were with malignant gastric ulcers. A total of 1 735 images belonged to training data set and 150 images were used for validation. These images were input into the Res-net50 model based on the fastai framework, the Res-net50 model based on the Keras framework, and the VGG-16 model based on the Keras framework respectively. Three separate binary classification models of normal gastric mucosa and benign ulcers, normal gastric mucosa and malignant ulcers, and benign and malignant ulcers were constructed.Results:The VGG-16 model showed the best ability of classification. The accuracy of the validation set was 98.0%, 98.0% and 85.0%, respectively, for distinguishing normal gastric mucosa from benign ulcers, normal gastric mucosa from malignant ulcers, and benign ulcers from malignant ulcers.Conclusion:The artificial intelligence-assisted diagnosis system obtained in this study shows noteworthy ability of detection of ulcerous lesions, and is expected to be used in clinical to assist doctors to detect ulcer and identify benign and malignant ulcers.

Application of artificial intelligence in real-time monitoring of withdrawal speed of colonoscopy / 中华消化内镜杂志

Objective:To construct a real-time monitoring system based on computer vision for monitoring withdrawal speed of colonoscopy and to validate its feasibility and performance.Methods:A total of 35 938 images and 63 videos of colonoscopy were collected in endoscopic database of Renmin Hospital of Wuhan University from May to October 2018. The images were divided into two datasets, one dataset included in vitro, in vivo and unqualified colonoscopy images, and another dataset included ileocecal and non-cecal area images. And then 3 594 and 2 000 images were selected respectively from the two datasets for testing the deep learning model, and the remaining images were used to train the model. Three colonoscopy videos were selected to evaluate the feasibility of real-time monitoring system, and 60 colonoscopy videos were used to evaluate its performance.Results:The accuracy rate of the deep learning model for classification for in vitro, in vivo, and unqualified colonoscopy images was 90.79% (897/988), 99.92% (1 300/1 301), and 99.08% (1 293/1 305), respectively, and the overall accuracy rate was 97.11% (3 490/3 594). The accuracy rate of identifying ileocecal and non-cecal area was 96.70% (967/1 000) and 94.90% (949/1 000), respectively, and the overall accuracy rate was 95.80% (1 916/2 000). In terms of feasibility evaluation, 3 colonoscopy videos data showed a linear relationship between the retraction speed and the image processing interval, which indicated that the real-time monitoring system automatically monitored the retraction speed during the colonoscopy withdrawal process. In terms of performance evaluation, the real-time monitoring system correctly predicted entry time and withdrawal time of all 60 examinations, and the results showed that the withdrawal speed and withdrawal time was significantly negative-related ( R=-0.661, P<0.001). The 95% confidence interval of withdrawal speed for the colonoscopy with withdrawal time of less than 5 min, 5-6 min, and more than 6 min was 43.90-49.74, 40.19-45.43, and 34.89-39.11 respectively. Therefore, 39.11 was set as the safe withdrawal speed and 45.43 as the alarm withdrawal speed. Conclusion:The real-time monitoring system we constructed can be used to monitor real-time withdrawal speed of colonoscopy and improve the quality of endoscopy.

Establishment of cross priming amplification for influenza A virus (H1N1) and its clinical application / 中国人兽共患病学报

In this study ,we established Cross Priming Amplification (CPA ) technology for detection of influenza A virus (H1N1) approach ,and evaluated the method through clinical specimens .A set of specific primers were designed for CPA ac‐cording to the conservative gene sequences ,designed and realized in the same temperature reverse transcription of RNA and DNA amplification . The amplification products can be totally enclosed nucleic acid detection device for testing . Fourteen healthy pharyngeal swab specimens ,seven other respiratory viruses ,and six arboviruses strains were used as the controls .We used a method that application of gradient dilution to the H 1N1 virus strain as the control to test the sensitivity of the CPA .We also used 102 clinical pharyngeal swab specimens of H1N1 patients for detection object to evaluate the feasibility of CPA clinical detection .Results showed that the CPA reaction did not appear cross reaction on health cases samples and other viruses .The sensitivity of the CPA was approximately 10 copies/uL in the established method that exactly titer H1N1 virus strain gradient dilution test .As to the positive results among the clinical pharyngeal swab samples collected from patients at different stages after onset ,the CPA had the highest positive detection rate during the first three days after onset (100% ) .While the detection rate from day 4 to day 6 after onset was 79 .31% .After 7 days ,the detection rate was 9 .09% .The established CPA assay was a highly sensitive ,specific and reproducible approach for rapid detection of H1N1 virus ,which is conducive to the early diagno‐sis of influenza A virus (H1N1) for basic medical units .

Genetic Evolution of Neuraminidase Gene of Influenza A/H1N1 Virus / 中山大学学报(医学科学版)

[Objective]This study was designed to investigate the genetic evolution of the neuraminidase(NA)gene of seasonal A/H1N1 and 2009 novel A/H1N1 inflilenza virus,and discuss the genetic variation of influenza A virus.[Methods]The virus strains were separately isolated from the clinical samples collected in 2006 and 2009,and then identified as seasonal A/H1N1 and novel A/H1N1.The full length of the NA gene of these strains was amplified by RT-PCR.Then the genetic evolution and mutations of important functional sites were analyzed.[Results]The homology of NA gene between the 2009 novel A/H1N1 isolates and 2006 seasonal A/H1N1 isolates was low(77.9%～78.8%),so was the homology of NA gene between the 2009 novel A/H1N1 isolates and representative strains of different periods and 1979-2001 WHO recommended vaccine strains(78.1%～79.3%).But compared with the WHO recommended vaccine strains of 2009 novel A/H1N1,the homology reached more than 99%.The genetic evolution analysis revealed that NA gene of 2009 novel A/H1N1 had the closest genetic relationship with the swine influenza A virus(A/swine/Belgium/1/1983)from Eurasian Iineage,and some of the antigenic sites and neuraminidase active sites of NA gene of seasonal A/H1N1 were mutated after 2005.[Conclusion]The NA gene of 2009 novel A/H1N1 may originate from Eurasian Iineage of swine influenza virus.The variation of NA gene of seasonal A/H1N1 has occurred in a certain degree.Hence,it is very necessary to continuously monitor the variant of influenza A virus.