Institution
Sun Yat-sen University
Education•Guangzhou, Guangdong, China•
About: Sun Yat-sen University is a education organization based out in Guangzhou, Guangdong, China. It is known for research contribution in the topics: Population & Cancer. The organization has 115149 authors who have published 113763 publications receiving 2286465 citations. The organization is also known as: Zhongshan University & SYSU.
Topics: Population, Cancer, Metastasis, Cell growth, Apoptosis
Papers published on a yearly basis
Papers
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TL;DR: Real-time RT-PCR results of all respiratory and faecal samples from patients with coronavirus disease 2019 (COVID-19) at the Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China, throughout the course of their illness and obligated quarantine period show associations that should be interpreted with caution because of the possibility of confounding.
1,320 citations
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TL;DR: In this evaluation of retinal images from multiethnic cohorts of patients with diabetes, the DLS had high sensitivity and specificity for identifying diabetic retinopathy and related eye diseases.
Abstract: Importance A deep learning system (DLS) is a machine learning technology with potential for screening diabetic retinopathy and related eye diseases. Objective To evaluate the performance of a DLS in detecting referable diabetic retinopathy, vision-threatening diabetic retinopathy, possible glaucoma, and age-related macular degeneration (AMD) in community and clinic-based multiethnic populations with diabetes. Design, Setting, and Participants Diagnostic performance of a DLS for diabetic retinopathy and related eye diseases was evaluated using 494 661 retinal images. A DLS was trained for detecting diabetic retinopathy (using 76 370 images), possible glaucoma (125 189 images), and AMD (72 610 images), and performance of DLS was evaluated for detecting diabetic retinopathy (using 112 648 images), possible glaucoma (71 896 images), and AMD (35 948 images). Training of the DLS was completed in May 2016, and validation of the DLS was completed in May 2017 for detection of referable diabetic retinopathy (moderate nonproliferative diabetic retinopathy or worse) and vision-threatening diabetic retinopathy (severe nonproliferative diabetic retinopathy or worse) using a primary validation data set in the Singapore National Diabetic Retinopathy Screening Program and 10 multiethnic cohorts with diabetes. Exposures Use of a deep learning system. Main Outcomes and Measures Area under the receiver operating characteristic curve (AUC) and sensitivity and specificity of the DLS with professional graders (retinal specialists, general ophthalmologists, trained graders, or optometrists) as the reference standard. Results In the primary validation dataset (n = 14 880 patients; 71 896 images; mean [SD] age, 60.2 [2.2] years; 54.6% men), the prevalence of referable diabetic retinopathy was 3.0%; vision-threatening diabetic retinopathy, 0.6%; possible glaucoma, 0.1%; and AMD, 2.5%. The AUC of the DLS for referable diabetic retinopathy was 0.936 (95% CI, 0.925-0.943), sensitivity was 90.5% (95% CI, 87.3%-93.0%), and specificity was 91.6% (95% CI, 91.0%-92.2%). For vision-threatening diabetic retinopathy, AUC was 0.958 (95% CI, 0.956-0.961), sensitivity was 100% (95% CI, 94.1%-100.0%), and specificity was 91.1% (95% CI, 90.7%-91.4%). For possible glaucoma, AUC was 0.942 (95% CI, 0.929-0.954), sensitivity was 96.4% (95% CI, 81.7%-99.9%), and specificity was 87.2% (95% CI, 86.8%-87.5%). For AMD, AUC was 0.931 (95% CI, 0.928-0.935), sensitivity was 93.2% (95% CI, 91.1%-99.8%), and specificity was 88.7% (95% CI, 88.3%-89.0%). For referable diabetic retinopathy in the 10 additional datasets, AUC range was 0.889 to 0.983 (n = 40 752 images). Conclusions and Relevance In this evaluation of retinal images from multiethnic cohorts of patients with diabetes, the DLS had high sensitivity and specificity for identifying diabetic retinopathy and related eye diseases. Further research is necessary to evaluate the applicability of the DLS in health care settings and the utility of the DLS to improve vision outcomes.
1,309 citations
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Richard A. Gibbs1, Jeffrey Rogers2, Michael G. Katze3, Roger E. Bumgarner3 +174 more•Institutions (28)
TL;DR: The genome sequence of an Indian-origin Macaca mulatta female is determined and compared with chimpanzees and humans to reveal the structure of ancestral primate genomes and to identify evidence for positive selection and lineage-specific expansions and contractions of gene families.
Abstract: The rhesus macaque (Macaca mulatta) is an abundant primate species that diverged from the ancestors of Homo sapiens about 25 million years ago. Because they are genetically and physiologically similar to humans, rhesus monkeys are the most widely used nonhuman primate in basic and applied biomedical research. We determined the genome sequence of an Indian-origin Macaca mulatta female and compared the data with chimpanzees and humans to reveal the structure of ancestral primate genomes and to identify evidence for positive selection and lineage-specific expansions and contractions of gene families. A comparison of sequences from individual animals was used to investigate their underlying genetic diversity. The complete description of the macaque genome blueprint enhances the utility of this animal model for biomedical research and improves our understanding of the basic biology of the species.
1,297 citations
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TL;DR: The new ASAS classification criteria for peripheral SpA performed well in patients presenting with peripheral arthritis, enthesitis and/or dactylitis, particularly regarding sensitivity.
Abstract: Objective To evaluate new classifi cation criteria for peripheral spondyloarthritis (SpA) in patients with SpA with peripheral manifestations only. Methods In this Assessment of SpondyloArthritis international Society (ASAS) study, two prespecifi ed sets of criteria were compared against the European Spondylarthropathy Study Group (ESSG) and Amor criteria in newly referred consecutive patients with undiagnosed peripheral arthritis, and/or enthesitis, and/ or dactylitis that usually began before 45 years of age. The clinical diagnosis (SpA vs no SpA) made by the ASAS rheumatologist served as reference standard. Results In all, 24 ASAS centres included 266 patients, with a fi nal diagnosis of SpA being made in 66.2%. After adjustments a fi nal set of criteria showed the best balance between sensitivity (77.8%) and specifi city (82.9%): arthritis and/or enthesitis and/or dactylitis plus (A) one or more of the following parameters: psoriasis, infl ammatory bowel disease, preceding infection, human leucocyte antigen B27, uveitis, sacroiliitis on imaging, or (B) two or more other parameters: arthritis, enthesitis, dactylitis, infl ammatory back pain in the past, family history of SpA. The new criteria performed better than modifi ed versions of the ESSG (sensitivity 62.5%, specifi city 81.1%) and the Amor criteria (sensitivity 39.8%, specifi city 97.8%), particularly regarding sensitivity. In the entire ASAS population of 975 patients the combined use of ASAS criteria for axial SpA and ASAS criteria for peripheral SpA also had a better balance (sensitivity 79.5%, specifi city 83.3%) than the modifi ed ESSG (sensitivity 79.1%, specifi city 68.8%) and Amor criteria (sensitivity 67.5%, specifi city 86.7%), respectively. Conclusions The new ASAS classifi cation criteria for peripheral SpA performed well in patients presenting with peripheral arthritis, enthesitis and/or dactylitis.
1,276 citations
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TL;DR: PLAT was as effective as surgical resection in the treatment of solitary and small HCC and had the advantage over surgical resections in being less invasive.
Abstract: Objective:To compare the results of percutaneous local ablative therapy (PLAT) with surgical resection in the treatment of solitary and small hepatocellular carcinoma (HCC).Summary Background Data:PLAT is effective in small HCC. Whether it is as effective as surgical resection in the long-term survi
1,269 citations
Authors
Showing all 115971 results
Name | H-index | Papers | Citations |
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Yi Chen | 217 | 4342 | 293080 |
Jing Wang | 184 | 4046 | 202769 |
Yang Gao | 168 | 2047 | 146301 |
Yang Yang | 164 | 2704 | 144071 |
Peter Carmeliet | 164 | 844 | 122918 |
Frank J. Gonzalez | 160 | 1144 | 96971 |
Xiang Zhang | 154 | 1733 | 117576 |
Rui Zhang | 151 | 2625 | 107917 |
Seeram Ramakrishna | 147 | 1552 | 99284 |
Joseph J.Y. Sung | 142 | 1240 | 92035 |
Joseph Lau | 140 | 1048 | 99305 |
Bin Liu | 138 | 2181 | 87085 |
Georgios B. Giannakis | 137 | 1321 | 73517 |
Kwok-Yung Yuen | 137 | 1173 | 100119 |
Shu Li | 136 | 1001 | 78390 |