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Early Jurassic carbon cycle perturbations recorded in terrestrial sediments of the Sichuan Basin, China

Geoscience Frontiers 2025年第4期16卷

作者： Xin Jin Viktória Baranyi David B. Kemp Zhiqiang Shi Hao Zou Binbing Li Yunwang Zhang Marco Franceschi State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation and Key Laboratory of Deep-time Geography and Environment Reconstruction and Applications of Ministry of Natural Resources Chengdu University of Technology Chengdu 610059 China Institute of Sedimentary Geology Chengdu University of Technology Chengdu 610059 China University of Trieste Department of Mathematics Informatics and Geosciences 34128 Trieste Italy Department of Geology Croatian Geological Survey (HGI-CGS) Sachsova 2 Zagreb HR-10000 Croatia State Key Laboratory for Biogeology and Environmental Geology and Hubei Key Laboratory of Critical Zone Evolution School of Earth Sciences China University of Geosciences (Wuhan) Wuhan 430074 China College of Earth and Planetary Sciences Chengdu University of Technology Chengdu Sichuan 610059 China

The Toarcian Oceanic Anoxic Event (T-OAE, ∼183 Ma) is marked in the sedimentary record by a sharp negative carbon isotope excursion, thought to be causally linked to the emplacement of the Karoo-Ferrar Large Igneous Province and the associated release of 12 C-enriched carbon. The T-OAE coincided with global climate and environmental changes, as well as biotic events, indicating substantial modifications in ecosystems. Recent studies have focused on the evidence of geological responses to the T-OAE in Early Jurassic terrestrial basins in China, particularly the Sichuan Basin. Nevertheless, debate remains over the identification of this event, owing to inadequate age-constraints of many sections, and a lack of robust correlations of the carbon isotope records. Moreover, the long-term evolution of the terrestrial carbon isotope record through the Early Jurassic, and its correlation to marine records, is still not firmly established. In this paper, we present new carbon isotope analyses of carbonate ( δ 13 C carb ) from lacustrine carbonates and terrestrial organic matter ( δ 13 C org ) from bulk rocks within the Ma’anshan and Da’anzhai members of the Ziliujing Formation from the Dacao ‘D’ (DCD) section in the eastern Sichuan Basin. Palynological-palynofacies analysis reveals a predominance of Classopollis pollen together with marker taxa such as Ischyosporites variegatus , Contignisporites problematicus , in the palynological assemblage, indicating a Pliensbachian–Toarcian age. A negative carbon isotope excursion (NCIE) is recorded in the organic carbon isotope data at the topmost part of the Pliensbachian Ma’anshan Member, which can be correlated to the Pliensbachian–Toarcian Boundary Event. This is followed, in the Toarcian Da’anzhai Member, by a major NCIE recorded in both organic matter and carbonate carbon isotope data which can be correlated to the T-OAE NCIE. A long-term carbon isotope record spanning the Sinemurian to Toarcian in Sichuan terrestrial sediments i

关键词： Toarcian oceanic anoxic event Lacustrine Palynology Carbon stable isotopes Carbonate diagenesis

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Fourth Hankel Determinant for a Subclass of Starlike Functions Based on Modified Sigmoid

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Journal of Function Spaces 2021年第1期2021卷

作者： Mashwani, Wali Khan Ahmad, Bakhtiar Khan, Nazar Khan, Muhammad Ghaffar Arjika, Sama Khan, Bilal Chinram, Ronnason Institute of Numerical Sciences Kohat University of Science and Technology Kohat Pakistan Govt. Degree College Mardan Mardan 23200 Pakistan Department of Mathematics Abbottabad University of Science and Technology Abbottabad 22010 Pakistan Department of Mathematics and Informatics University of Agadez Agadez Niger International Chair of Mathematical Physics and Applications (ICMPA-UNESCO Chair) University of Abomey-Calavi Post Box 072 50 Cotonou Benin School of Mathematical Sciences Shanghai Key Laboratory of PMMP East China Normal University 500 Dongchuan Road Shanghai 200241 China Algebra and Applications Research Unit Division of Computational Science Faculty of Science Prince of Songkla University Hat Yai Songkhla 90110 Thailand

In our present investigation, we obtain the improved third-order Hankel determinant for a class of starlike functions connected with modified sigmoid functions. Further, we investigate the fourth-order Hankel determinant, Zalcman conjecture, and also evaluate the fourth-order Hankel determinants for 2-fold, 3-fold, and 4-fold symmetric starlike functions. © 2021 Wali Khan Mashwani et al.

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The (t, k) -Diagnosability of Balanced Hypercube under the PMC Model

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International Journal of Computer mathematics: Computer Systems Theory 2018年第4期3卷 230-243页

作者： Zhang, Xinxin Xu, Li Lin, Limei Huang, Yanze Wang, Xiaoding College of Mathematics and Informatics Fujian Normal University Fuzhou Fujian China Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University Fuzhou Fujian China School of Mathematics and Physics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou Fujian China

The network's diagnosability implies its self-diagnostic capability. Compared with classical sequential diagnosis that has been widely used to identify faulty nodes, the (t, k)- -diagnosis is considered as an improvement of sequential diagnosis, where t ≥ k.. To be more specific, the (t, k)- -diagnosis can identify and replace at least k faulty nodes in each iteration until all t faulty nodes are found. Meanwhile, compared with the other hypercube graphs, the balanced hypercube (denoted BH_n) has many good properties, such as a better fault-tolerant embedding capability and a smaller diameter. In this paper, we first propose a body discover algorithm (named BD) of BH_n, where a body consists of node sets that meet specific conditions. Based on bodies found by BD, we verify that the BH_n is (t, k)- -diagnosable. Then we prove that the (t, k)-diagnosability of BH_n is (2n−1, 2n) under the PMC model. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.

关键词： -Diagnosability balanced hypercube body fault tolerance PMC model

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Updating TCGA glioma classification through integration of molecular data following the latest WHO guidelines

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Scientific Data 2025年第1期12卷 1-10页

作者： Mónica Leiria de Mendonça Susana Vinga Roberta Coletti Marta B. Lopes Céline S. Gonçalves Eduarda P. Martins Bruno M. Costa INESC-ID Instituto Superior Técnico Universidade de Lisboa R. Alves Redol 9 1000-029 Lisbon Portugal IDMEC Instituto Superior Técnico Universidade de Lisboa Av. Rovisco Pais 1 1049-001 Lisbon Portugal Center for Mathematics and Applications (NOVA Math) NOVA FCT 2829-516 Caparica Portugal NOVA Laboratory for Computer Science and Informatics (NOVA LINCS) NOVA FCT 2829-516 Caparica Portugal UNIDEMI Department of Mechanical and Industrial Engineering NOVA FCT 2829-516 Caparica Portugal Life and Health Sciences Research Institute (ICVS) School of Medicine University of Minho Campus Gualtar 4710-057 Braga Portugal ICVS/3B’s - PT Government Associate Laboratory Braga/Guimarães Portugal

The understanding of glioma disease has significantly advanced through the application of genetic and molecular profiling techniques on brain tumour tissue. Molecular biomarkers have gained a crucial role in glioma diagnosis, driving groundbreaking changes in the disease classification as standardised by the 2016 and 2021 World Health Organisation (WHO) Classification of Tumours of the Central Nervous System. Recent insights from large-scale multi-omics databases, such as The Cancer Genome Atlas (TCGA), have enriched our comprehension of this cancer type. However, given the evolution of glioma classification, retrospective databases may contain outdated annotations, suboptimal for research. To address this issue, we propose two methods for updating the tumor classification of TCGA glioma samples according to the 2016 and 2021 WHO guidelines, through the integration of open-access curated molecular profiling data. Respectively, our Method-2016 and Method-2021 allowed for the diagnostic update of 98% and 87% of cases. The proposed reclassification pipelines, provided inRscripts, enable straightforward reproduction or customisation upon new WHO guideline releases.

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Why is the Winner the Best?

Why is the Winner the Best?

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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SE(3)-equivariant prediction of molecular wavefunctions and electronic densities 21

SE(3)-equivariant prediction of molecular wavefunctions and ...

引用

Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Oliver T. Unke Mihail Bogojeski Michael Gastegger Mario Geiger Tess Smidt Klaus-Robert Müller Machine Learning Group Technische Universität Berlin Berlin Germany and DFG Cluster of Excellence "Unifying Systems in Catalysis" (UniSysCat) Technische Universität Berlin Berlin Germany and BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin Germany and Google Research Brain Team Berlin Germany Machine Learning Group Technische Universität Berlin Berlin Germany and BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin Germany Machine Learning Group Technische Universität Berlin Berlin Germany and DFG Cluster of Excellence "Unifying Systems in Catalysis" (UniSysCat) Technische Universität Berlin Berlin Germany and BASLEARN – TU Berlin/BASF Joint Lab for Machine Learning Technische Universität Berlin Berlin Germany Institute of Physics École Polytechnique Fédérale de Lausanne Lausanne Switzerland Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA and Center for Advanced Mathematics for Energy Research Applications (CAMERA) Lawrence Berkeley National Laboratory Berkeley CA Machine Learning Group Technische Universität Berlin Berlin Germany Department of Artificial Intelligence Korea University Anam-dong Seongbuk-gu Seoul Korea and Max Planck Institute for Informatics Stuhlsatzenhausweg 66123 Saarbrücken Germany and BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin Germany and Google Research Brain Team Berlin Germany

ISBN: (纸本)9781713845393

Machine learning has enabled the prediction of quantum chemical properties with high accuracy and efficiency, allowing to bypass computationally costly ab initio calculations. Instead of training on a fixed set of properties, more recent approaches attempt to learn the electronic wavefunction (or density) as a central quantity of atomistic systems, from which all other observables can be derived. This is complicated by the fact that wavefunctions transform non-trivially under molecular rotations, which makes them a challenging prediction target. To solve this issue, we introduce general SE(3)-equivariant operations and building blocks for constructing deep learning architectures for geometric point cloud data and apply them to reconstruct wavefunctions of atomistic systems with unprecedented accuracy. Our model achieves speedups of over three orders of magnitude compared to ab initio methods and reduces prediction errors by up to two orders of magnitude compared to the previous state-of-the-art. This accuracy makes it possible to derive properties such as energies and forces directly from the wavefunction in an end-to-end manner. We demonstrate the potential of our approach in a transfer learning application, where a model trained on low accuracy reference wavefunctions implicitly learns to correct for electronic many-body interactions from observables computed at a higher level of theory. Such machine-learned wavefunction surrogates pave the way towards novel semi-empirical methods, offering resolution at an electronic level while drastically decreasing computational cost. Additionally, the predicted wavefunctions can serve as initial guess in conventional ab initio methods, decreasing the number of iterations required to arrive at a converged solution, thus leading to significant speedups without any loss of accuracy or robustness. While we focus on physics applications in this contribution, the proposed equivariant framework for deep learning on point clouds is p

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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： biotic

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Random conductance models with stable-like jumps: Heat kernel estimates and harnack inequalities

arXiv

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arXiv 2018年

作者： Chen, Xin Kumagai, Takashi Wang, Jian Department of Mathematics Shanghai Jiao Tong University Shanghai200240 China Research Institute for Mathematical Sciences Kyoto University Kyoto606-8502 Japan College of Mathematics and Informatics Fujian Key Laboratory of Mathematical Analysis and Applications Fujian Normal University Fuzhou350007 China

We establish two-sided heat kernel estimates for random conductance models with non-uniformly elliptic (possibly degenerate) stable-like jumps on graphs. These are long range counterparts of well known two-sided Gaussian heat kernel estimates by M.T. Barlow for nearest neighbor (short range) random walks on the supercritical percolation cluster. Unlike the cases for nearest neighbor conductance models, the idea through parabolic Harnack inequalities does not work, since even elliptic Harnack inequalities do not hold in the present setting. As an application, we establish the local limit theorem for the models. Copyright © 2018, The Authors. All rights reserved.

关键词： Solvents

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Random conductance models with stable-like jumps I: Quenched invariance principle

arXiv

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arXiv 2018年

We study the quenched invariance principle for random conductance models with long range jumps on Zd, where the transition probability from x to y is in average comparable to |x − y|−(d+α) with α ∈ (0, 2) but possibly degenerate. Under some moment conditions on the conductance, we prove that the scaling limit of the Markov process is a symmetric α-stable Lévy process on Rd. The well-known corrector method in homogenization theory does not seem to work in this setting. Instead, we utilize probabilistic potential theory for the corresponding jump processes. Two essential ingredients of our proof are the tightness estimate and the Hölder regularity of parabolic functions for non-elliptic α-stable-like processes on graphs. Our method is robust enough to apply not only for Zd but also for more general graphs whose scaling limits are nice metric measure spaces. Copyright © 2018, The Authors. All rights reserved.

关键词： Markov processes

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MicroRNA signature in patients with hepatocellular carcinoma associated with type 2 diabetes

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World Journal of Gastroenterology 2019年第42期25卷 6322-6341页

作者： Moustafa Nouh Elemeery Marwa Anwar Mohamed Marwa Ahmed Madkour Mohammed Mohammed Shamseya Noha Mahmoud Issa Ahmed Noah Badr Doaa Ahmed Ghareeb Cheol-Ho Pan Département de Neurosciences CRCHUMUniversitéde MontréalMontréalQuebec H2X 3E4Canada Medical Biotechnology Laboratory Genetic Engineering and Biotechnology Research DivisionNational Research CentreCairo 12622Egypt Natural Product Informatics Research Center KIST Gangneung Institute of Natural ProductsGangneung 25451South Korea Division of Bio-Medical Science and Technology KIST SchoolKorea University of Science and TechnologySeoul 02792South Korea Department of Chemical Pathology Medical Research InstituteAlexandria UniversityAlexandria 21511Egypt Experimental and Clinical Internal Medicine Department Medical Research InstituteAlexandria UniversityAlexandria 21511Egypt Human Genetics Department Medical Research InstituteAlexandria UniversityAlexandria 21511Egypt Food Toxicology and Contaminates Department National Research CentreDokkiCairo 12622Egypt Bioscreening and preclinical trial lab Biochemistry DepartmentFaculty of ScienceAlexandria UniversityAlexandria 12522Egypt Pharmaceutical and fermentation industries development center the city of scientific research and technological applicationsAlexandria 26411Egypt

BACKGROUND Nonalcoholic steatohepatitis-related cirrhosis is one of the liver complications in type 2 diabetes mellitus(T2DM)and reported to be a risk factor for developing hepatocellular carcinoma(HCC).A reliable screening biomarker of liver cirrhosis(LC)and HCC among T2DM patients is important to reduce the morbidity and mortality of this ***(miRNA)is considered a key player in HCC and T2DM,and it might be a hidden culprit in diabetes-associated HCC,making it a promising reliable prognostic *** To investigate the signature of serum miRNAs as early biomarkers for the screening of HCC among diabetic *** Expression profiles of miRNAs in serum samples of diabetic LC and diabetic HCC patients were assessed using Illumina sequencing;then,RT-qPCR was used to validate significantly altered miRNAs between the two *** miRNAs were tested in serum samples of 200 T2DM patients,270 LC patients,200 HCC patients,and 225 healthy control ***,receiver operating characteristic(ROC)analysis,with area under the curve(AUC),was performed to assess the diagnostic performance of the screened miRNAs for discriminating HCC from LC and nonmalignant patients(LC+T2DM).RESULTS Expression of the sequenced miRNAs in serum was different in HCC vs LCpositive T2DM *** miRNAs(miR-34a,miR-221)were significantly upregulated and five miRNAs(miR-16,miR-23-3p,miR-122-5p,miR-198,miR-199a-3p)were significantly down-regulated in HCC compared to LC *** of ROC curve demonstrated that the combination of these seven miRNAs can be used as a reliable biomarker for detection of HCC in diabetic patients,as it could identify HCC with high diagnostic accuracy in diabetic LC patients(AUC=0.993)and in diabetic nonmalignant patients(AUC=0.961).CONCLUSION This study validates a panel of serum miRNAs that can be used as a reliable noninvasive screening biomarker of HCC among T2DM cirrhotic and noncirrhotic *** study recommends furt

关键词： MicroRNA Hepatocellular carcinoma Type 2 diabetes Nonalcoholic fatty liver disease

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