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4. Protein Tyrosine Phosphatase 1B Deficiency Potentiates PERK/eIF2α Signaling in Brown Adipocytes

Author

Bettaieb, Ahmed, Matsuo, Kosuke, Matsuo, Izumi, Wang, Shuo, Melhem, Ramzi, Koromilas, Antonis E, and Haj, Fawaz G

Subjects
Biochemistry and Cell Biology, Biomedical and Clinical Sciences, Biological Sciences, Obesity, Underpinning research, 1.1 Normal biological development and functioning, 2.1 Biological and endogenous factors, Aetiology, Metabolic and endocrine, Activating Transcription Factor 4, Adipocytes, Brown, Animals, Cell Differentiation, Cell Line, Endoplasmic Reticulum Stress, Enzyme Activation, Eukaryotic Initiation Factor-2, Gene Knockout Techniques, Humans, Male, Mice, Phosphorylation, Protein Biosynthesis, Protein Tyrosine Phosphatase, Non-Receptor Type 1, Signal Transduction, Tyrosine, Unfolded Protein Response, eIF-2 Kinase, General Science & Technology
Abstract

BackgroundProtein-tyrosine phosphatase 1B (PTP1B) is a physiological regulator of glucose homeostasis and body mass, and has been implicated in endoplasmic reticulum (ER) stress. Herein, we assess the role of PTP1B in ER stress in brown adipocytes, which are key regulators of thermogenesis and metabolic response.Methodology/principal findingsTo determine the role of PTP1B in ER stress, we utilized brown adipose tissue (BAT) from mice with adipose-specific PTP1B deletion, and brown adipocytes deficient in PTP1B and reconstituted with PTP1B wild type (WT) or the substrate-trapping PTP1B D181A (D/A) mutant. PTP1B deficiency led to upregulation of PERK-eIF2α phosphorylation and IRE1α-XBP1 sub-arms of the unfolded protein response. In addition, PTP1B deficiency sensitized differentiated brown adipocytes to chemical-induced ER stress. Moreover, PERK activation and tyrosine phosphorylation were increased in BAT and adipocytes lacking PTP1B. Increased PERK activity resulted in the induction of eIF2α phosphorylation at Ser51 and better translatability of ATF4 mRNA in response to ER stress. At the molecular level, we demonstrate direct interaction between PTP1B and PERK and identify PERK Tyr615 as a mediator of this association.ConclusionsCollectively, the data demonstrate that PTP1B is a physiologically-relevant modulator of ER stress in brown adipocytes and that PTP1B deficiency modulates PERK-eIF2α phosphorylation and protein synthesis.

Published
2012

10. A novel YOLOv3-arch model for identifying cholelithiasis and classifying gallstones on CT images

Author

Tong Ding, Wang Shuo, Sibo Qiao, Fan Meng, Shanchen Pang, Xun Wang, and Pibao Li

Subjects
Computer science, 02 engineering and technology, Gallstones, Diagnostic Radiology, Machine Learning, Cholelithiasis, 0202 electrical engineering, electronic engineering, information engineering, Medicine and Health Sciences, Cholecystitis and Biliary Colic, Tomography, Multidisciplinary, Training set, Artificial neural network, Radiology and Imaging, Gallbladder, 021001 nanoscience & nanotechnology, Liver, Medicine, 020201 artificial intelligence & image processing, Anatomy, 0210 nano-technology, Liver pathology, Biliary Disorders, Research Article, Computer and Information Sciences, Neural Networks, Imaging Techniques, Science, Neuroimaging, Gastroenterology and Hepatology, Research and Analysis Methods, Deep Learning, Artificial Intelligence, Diagnostic Medicine, medicine, Humans, business.industry, Deep learning, Biology and Life Sciences, Pattern recognition, medicine.disease, Object detection, Computed Axial Tomography, Biliary System, Gastrointestinal Imaging, Artificial intelligence, Neural Networks, Computer, business, Tomography, X-Ray Computed, Spleen, Liver and Spleen Scan, Neuroscience
Abstract

Locating diseases precisely from medical images, like ultrasonic and CT images, have been one of the most challenging problems in medical image analysis. In recent years, the vigorous development of deep learning models have greatly improved the accuracy in disease location on medical images. However, there are few artificial intelligent methods for identifying cholelithiasis and classifying gallstones on CT images, since no open source CT images dataset of cholelithiasis and gallstones is available for training the models and verifying their performance. In this paper, we build up the first medical image dataset of cholelithiasis by collecting 223846 CT images with gallstone of 1369 patients. With these CT images, a neural network is trained to "pick up" CT images of high quality as training set, and then a novel Yolo neural network, named Yolov3-arch neural network, is proposed to identify cholelithiasis and classify gallstones on CT images. Identification and classification accuracies are obtained by 10-fold cross-validations. It is obtained that our Yolov3-arch model is with average accuracy 92.7% in identifying granular gallstones and average accuracy 80.3% in identifying muddy gallstones. This achieves 3.5% and 8% improvements in identifying granular and muddy gallstones to general Yolo v3 model, respectively. Also, the average cholelithiasis identifying accuracy is improved to 86.50% from 80.75%. Meanwhile, our method can reduce the misdiagnosis rate of negative samples by the object detection model.

Published
2019

11. An artificial intelligent diagnostic system on mobile Android terminals for cholelithiasis by lightweight convolutional neural network

Author

Xun Wang, Wang Shuo, Pibao Li, Shanchen Pang, and Alfonso Rodríguez-Patón

Subjects
Computer science, Computer Vision, 02 engineering and technology, computer.software_genre, Convolutional neural network, Diagnostic Radiology, Grayscale, Mathematical and Statistical Techniques, Cholelithiasis, Medicine and Health Sciences, 0202 electrical engineering, electronic engineering, information engineering, Android (operating system), Tomography, computer.programming_language, Multidisciplinary, Artificial neural network, Radiology and Imaging, Gallbladder, 021001 nanoscience & nanotechnology, Mobile Applications, 3. Good health, Liver, Radiographic Image Interpretation, Computer-Assisted, Engineering and Technology, Medicine, 020201 artificial intelligence & image processing, Anatomy, 0210 nano-technology, Biliary Disorders, Mobile device, Algorithms, Research Article, Computer and Information Sciences, Neural Networks, Java, Imaging Techniques, Science, Neuroimaging, Gastroenterology and Hepatology, Digital Imaging, Research and Analysis Methods, Deep Learning, Diagnostic Medicine, Humans, Natural Language Processing, business.industry, Deep learning, Biology and Life Sciences, Convolution, Object detection, Computed Axial Tomography, Target Detection, Virtual machine, Biliary System, Neural Networks, Computer, Artificial intelligence, Tomography, X-Ray Computed, business, Mathematical Functions, computer, Neuroscience
Abstract

Artificial intelligence (AI) tools have been applied to diagnose or predict disease risk from medical images with recent data disclosure actions, but few of them are designed for mobile terminals due to the limited computational power and storage capacity of mobile devices. In this work, a novel AI diagnostic system is proposed for cholelithiasis recognition on mobile devices with Android platform. To this aim, a data set of CT images of cholelithiasis is firstly collected from The Third Hospital of Shandong Province, China, and then we technically use histogram equalization to preprocess these CT images. As results, a lightweight convolutional neural network is obtained in a constructive way to extract cholelith features and recognize gallstones. In terms of implementation, we compile Java and C++ to adapt to the application of deep learning algorithm on mobile devices with Android platform. Noted that, the training task is completed offline on PC, but cholelithiasis recognition tasks are performed on mobile terminals. We evaluate and compare the performance of our MobileNetV2 with MobileNetV1, Single Shot Detector (SSD), YOLOv2 and original SSD (with VGG-16) as feature extractors for object detection. It is achieved that our MobileNetV2 achieve similar accuracy rate, about 91% with the other four methods, but the number of parameters used is reduced from 36.1M (SSD 300, SSD512), 50.7M (Yolov2) and 5.1M (MobileNetV1) to 4.3M (MobileNetV2). The complete process on testing mobile devices, including Virtual machine, Xiaomi 7 and Htc One M8 can be controlled within 4 seconds in recognizing cholelithiasis as well as the degree of the disease.

Published
2019

35. The Relationship between Frontal Lobe Lesions, Course of Post-Stroke Depression, and 1-year Prognosis in Patients with First-Ever Ischemic Stroke

Author

Shi, Yu-Zhi, primary, Xiang, Yu-Tao, additional, Wu, Shuo-Lin, additional, Zhang, Ning, additional, Zhou, Juan, additional, Bai, Ying, additional, Wang, Shuo, additional, Wang, Yi-Long, additional, Zhao, Xing-Quan, additional, Ungvari, Gabor S., additional, Chiu, Helen F. K., additional, Wang, Yong-Jun, additional, and Wang, Chun-Xue, additional

Published
2014
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37. Structural Insights into Neutrophilic Migration Revealed by the Crystal Structure of the Chemokine Receptor CXCR2 in Complex with the First PDZ Domain of NHERF1

Author

Lu, Guorong, primary, Wu, Yanning, additional, Jiang, Yuanyuan, additional, Wang, Shuo, additional, Hou, Yuning, additional, Guan, Xiaoqing, additional, Brunzelle, Joseph, additional, Sirinupong, Nualpun, additional, Sheng, Shijie, additional, Li, Chunying, additional, and Yang, Zhe, additional

Published
2013
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42. 2-Deoxy--Glucose Can Complement Doxorubicin and Sorafenib to Suppress the Growth of Papillary Thyroid Carcinoma Cells.

Author

Wang, Shuo-Yu, Wei, Yau-Huei, Shieh, Dar-Bin, Lin, Li-Ling, Cheng, Shih-Ping, Wang, Pei-Wen, and Chuang, Jiin-Haur

Subjects
*THYROID cancer, *DOXORUBICIN, *DEOXY sugars, *PAPILLARY carcinoma, *CANCER cells, *ENERGY metabolism
Abstract

Tumor cells display a shift in energy metabolism from oxidative phosphorylation to aerobic glycolysis. A subset of papillary thyroid carcinoma (PTC) is refractory to surgery and radioactive iodine ablation. Doxorubicin and sorafenib are the drugs of choice for treating advanced thyroid cancer but both induce adverse effects. In this study, we assessed the anti-cancer activity of 2-deoxy--glucose (2-DG) alone and in combination with doxorubicin or sorafenib in PTC cell lines with (BCPAP) and without (CG3) the BRAFV600E mutation. BCPAP cells were more glycolytic than CG3 cells, as evidenced by their higher extracellular -lactate production, lower intracellular ATP level, lower oxygen consumption rate (OCR), and lower ratio of OCR/extracellular acidification rate. However, dose-dependent reduction in cell viability, intracellular ATP depletion, and extracellular -lactate production were observed after 2-DG treatment. Regression analysis showed that cell growth in both cell lines was dependent on ATP generation. 2-DG increased the chemosensitivity of BCPAP and CG3 cell lines to doxorubicin and sorafenib. These results demonstrate that the therapeutic effects of low combined doses of 2-DG and doxorubicin or sorafenib are similar to those of high doses of doxorubicin or sorafenib alone in PTC cell lines regardless of the BRAFV600E mutation. [ABSTRACT FROM AUTHOR]

Published
2015
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43. Plastid Genome Sequence of a Wild Woody Oil Species, Prinsepia utilis, Provides Insights into Evolutionary and Mutational Patterns of Rosaceae Chloroplast Genomes.

Author

Wang, Shuo, Shi, Chao, and Gao, Li-Zhi

Subjects
*PLASTIDS, *PLANT genomes, *NUCLEOTIDE sequence, *ROSACEAE, *CHLOROPLAST DNA, *PLANT evolution, *PLANT mutation
Abstract

Background:Prinsepia utilis Royle is a wild woody oil species of Rosaceae that yields edible oil which has been proved to possess particular benefits for human health and medical therapy. However, the lack of bred varieties has largely impeded exploiting immense potentials for high quality of its seed oil. It is urgently needed to enlarge the knowledge of genetic basis of the species and develop genetic markers to enhance modern breeding programs. Results:Here we reported the complete chloroplast (cp) genome of 156,328 bp. Comparative cp sequence analyses of P. utilis along with other four Rosaceae species resulted in similar genome structures, gene orders, and gene contents. Contraction/expansion of inverted repeat regions (IRs) explained part of the length variation in the Rosaceae cp genomes. Genome sequence alignments revealed that nucleotide diversity was associated with AT content, and large single copy regions (LSC) and small single copy regions (SSC) harbored higher sequence variations in both coding and non-coding regions than IRs. Simple sequence repeats (SSRs) were detected in the P. utilis and compared with those of the other four Rosaceae cp genomes. Almost all the SSR loci were composed of A or T, therefore it might contribute to the A-T richness of cp genomes and be associated with AT biased sequence variation. Among all the protein-coding genes, ycf1 showed the highest sequence divergence, indicating that it could accomplish the discrimination of species within Rosaceae as well as within angiosperms better than other genes. Conclusions:With the addition of this new sequenced cp genome, high nucleotide substitution rate and abundant deletions/insertions were observed, suggesting a greater genomic dynamics than previously explored in Rosaceae. The availability of the complete cp genome of P. utilis will provide chloroplast markers and genetic information to better enhance the conservation and utilization of this woody oil plant. [ABSTRACT FROM AUTHOR]

Published
2013
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