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Institution

Nanchang University

EducationNanchang, China
About: Nanchang University is a education organization based out in Nanchang, China. It is known for research contribution in the topics: Population & Cancer. The organization has 35808 authors who have published 26148 publications receiving 357348 citations. The organization is also known as: Nánchāng Dàxué.
Topics: Population, Cancer, Cell growth, Catalysis, Apoptosis


Papers
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Journal ArticleDOI
Yi Chen1, Mingyong Xie1, Shaoping Nie1, Chang Li1, Yuanxing Wang1 
TL;DR: A water-soluble protein-bound polysaccharide was extracted from the fruiting bodies of Ganoderma atrum and isolated by gel-filtration chromatography as mentioned in this paper.

523 citations

Journal ArticleDOI
TL;DR: In this article, a deep learning algorithm was used to detect the presence of COVID-19 in CT images during the 2015-2016 influenza season, achieving an accuracy of 89.5% with a specificity of 0.88 and sensitivity of 0.87.
Abstract: The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 26 million cases of Corona virus disease (COVID-19) in the world so far. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment are a priority. Pathogenic laboratory testing is typically the gold standard, but it bears the burden of significant false negativity, adding to the urgent need of alternative diagnostic methods to combat the disease. Based on COVID-19 radiographic changes in CT images, this study hypothesized that artificial intelligence methods might be able to extract specific graphical features of COVID-19 and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. We collected 1065 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the inception transfer-learning model to establish the algorithm, followed by internal and external validation. The internal validation achieved a total accuracy of 89.5% with a specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with a specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images, the first two nucleic acid test results were negative, and 46 were predicted as COVID-19 positive by the algorithm, with an accuracy of 85.2%. These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. • The study evaluated the diagnostic performance of a deep learning algorithm using CT images to screen for COVID-19 during the influenza season. • As a screening method, our model achieved a relatively high sensitivity on internal and external CT image datasets. • The model was used to distinguish between COVID-19 and other typical viral pneumonia, both of which have quite similar radiologic characteristics.

512 citations

Journal ArticleDOI
TL;DR: This review elaborates upon existing optical nanoprobes that exploit ratiometric measurements for improved sensing and imaging, including fluorescence, surface enhanced Raman scattering (SERS), and photoacoustic nanoprops, and their potential biomedical applications for targeting specific biomolecule populations.
Abstract: Exploring and understanding biological and pathological changes are of great significance for early diagnosis and therapy of diseases. Optical sensing and imaging approaches have experienced major progress in this field. Particularly, an emergence of various functional optical nanoprobes has provided enhanced sensitivity, specificity, targeting ability, as well as multiplexing and multimodal capabilities due to improvements in their intrinsic physicochemical and optical properties. However, one of the biggest challenges of conventional optical nanoprobes is their absolute intensity-dependent signal readout, which causes inaccurate sensing and imaging results due to the presence of various analyte-independent factors that can cause fluctuations in their absolute signal intensity. Ratiometric measurements provide built-in self-calibration for signal correction, enabling more sensitive and reliable detection. Optimizing nanoprobe designs with ratiometric strategies can surmount many of the limitations encountered by traditional optical nanoprobes. This review first elaborates upon existing optical nanoprobes that exploit ratiometric measurements for improved sensing and imaging, including fluorescence, surface enhanced Raman scattering (SERS), and photoacoustic nanoprobes. Next, a thorough discussion is provided on design strategies for these nanoprobes, and their potential biomedical applications for targeting specific biomolecule populations (e.g. cancer biomarkers and small molecules with physiological relevance), for imaging the tumor microenvironment (e.g. pH, reactive oxygen species, hypoxia, enzyme and metal ions), as well as for intraoperative image guidance of tumor-resection procedures.

509 citations

Journal ArticleDOI
TL;DR: It is suggested that hiPSC-MSC-Exos can facilitate cutaneous wound healing by promoting collagen synthesis and angiogenesis and not only promoted the generation of newly formed vessels, but also accelerated their maturation in wound sites.
Abstract: Human induced pluripotent stem cell-derived mesenchymal stem cells (hiPSC-MSCs) have emerged as a promising alternative for stem cell transplantation therapy. Exosomes derived from mesenchymal stem cells (MSC-Exos) can play important roles in repairing injured tissues. However, to date, no reports have demonstrated the use of hiPSC-MSC-Exos in cutaneous wound healing, and little is known regarding their underlying mechanisms in tissue repair. hiPSC-MSC-Exos were injected subcutaneously around wound sites in a rat model and the efficacy of hiPSC-MSC-Exos was assessed by measuring wound closure areas, by histological and immunofluorescence examinations. We also evaluated the in vitro effects of hiPSC-MSC-Exos on both the proliferation and migration of human dermal fibroblasts and human umbilical vein endothelial cells (HUVECs) by cell-counting and scratch assays, respectively. The effects of exosomes on fibroblast collagen and elastin secretion were studied in enzyme-linked immunosorbent assays and quantitative reverse-transcriptase–polymerase chain reaction (qRT-PCR). In vitro capillary network formation was determined in tube-formation assays. Transplanting hiPSC-MSC-Exos to wound sites resulted in accelerated re-epithelialization, reduced scar widths, and the promotion of collagen maturity. Moreover, hiPSC-MSC-Exos not only promoted the generation of newly formed vessels, but also accelerated their maturation in wound sites. We found that hiPSC-MSC-Exos stimulated the proliferation and migration of human dermal fibroblasts and HUVECs in a dose-dependent manner in vitro. Similarly, Type I, III collagen and elastin secretion and mRNA expression by fibroblasts and tube formation by HUVECs were also increased with increasing hiPSC-MSC-Exos concentrations. Our findings suggest that hiPSC-MSC-Exos can facilitate cutaneous wound healing by promoting collagen synthesis and angiogenesis. These data provide the first evidence for the potential of hiPSC-MSC-Exos in treating cutaneous wounds.

503 citations

Journal ArticleDOI
TL;DR: In this paper, a UAV-enabled MEC wireless powered system is investigated under both partial and binary computation offloading modes, subject to the energy harvesting causal constraint and the UAV's speed constraint.
Abstract: Mobile-edge computing (MEC) and wireless power transfer are two promising techniques to enhance the computation capability and to prolong the operational time of low-power wireless devices that are ubiquitous in Internet of Things. However, the computation performance and the harvested energy are significantly impacted by the severe propagation loss. In order to address this issue, an unmanned aerial vehicle (UAV)-enabled MEC wireless-powered system is studied in this paper. The computation rate maximization problems in a UAV-enabled MEC wireless powered system are investigated under both partial and binary computation offloading modes, subject to the energy-harvesting causal constraint and the UAV’s speed constraint. These problems are non-convex and challenging to solve. A two-stage algorithm and a three-stage alternative algorithm are, respectively, proposed for solving the formulated problems. The closed-form expressions for the optimal central processing unit frequencies, user offloading time, and user transmit power are derived. The optimal selection scheme on whether users choose to locally compute or offload computation tasks is proposed for the binary computation offloading mode. Simulation results show that our proposed resource allocation schemes outperform other benchmark schemes. The results also demonstrate that the proposed schemes converge fast and have low computational complexity.

496 citations


Authors

Showing all 35970 results

NameH-indexPapersCitations
Rui Zhang1512625107917
Zhen Li127171271351
Xuan Zhang119153065398
Wei Xu103149249624
Wei Chen103143844994
Jian Chen96171852917
Peng Li95154845198
Yu Wang92168747472
Xiaojun Wu91108831687
Aiwen Lei8756926268
Yen Wei8564925805
Bo Li8389128722
Zhigang Shuai8142423039
Tianxi Liu8141121036
Jiquan Chen8046827525
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202390
2022306
20213,877
20203,667
20192,818
20182,315