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Artikel ; Online: Early Detection of Pancreatic Cancer: Are We Ready for Prime Time?

Wu, Wenming

2022 Band 163, Heft 5, Seite(n) 1157–1159

Mesh-Begriff(e)	Humans ; Early Detection of Cancer ; Pancreatic Neoplasms/diagnosis ; Pancreatic Neoplasms
Sprache	Englisch
Erscheinungsdatum	2022-08-02
Erscheinungsland	United States
Dokumenttyp	Editorial ; Research Support, Non-U.S. Gov't ; Comment
ZDB-ID	80112-4
ISSN	1528-0012 ; 0016-5085
ISSN (online)	1528-0012
ISSN	0016-5085
DOI	10.1053/j.gastro.2022.07.072
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Network-Based Structural Learning Nonnegative Matrix Factorization Algorithm for Clustering of scRNA-Seq Data.

Wu, Wenming / Ma, Xiaoke

IEEE/ACM transactions on computational biology and bioinformatics

2023 Band 20, Heft 1, Seite(n) 566–575

Abstract: Single-cell RNA sequencing (scRNA-seq) measures expression profiles at the single-cell level, which sheds light on revealing the heterogeneity and functional diversity among cell populations. The vast majority of current algorithms identify cell types by ...

Abstract	Single-cell RNA sequencing (scRNA-seq) measures expression profiles at the single-cell level, which sheds light on revealing the heterogeneity and functional diversity among cell populations. The vast majority of current algorithms identify cell types by directly clustering transcriptional profiles, which ignore indirect relations among cells, resulting in an undesirable performance on cell type discovery and trajectory inference. Therefore, there is a critical need for inferring cell types and trajectories by exploiting the interactions among cells. In this study, we propose a network-based structural learning nonnegative matrix factorization algorithm (aka SLNMF) for the identification of cell types in scRNA-seq, which is transformed into a constrained optimization problem. SLNMF first constructs the similarity network for cells and then extracts latent features of the cells by exploiting the topological structure of the cell-cell network. To improve the clustering performance, the structural constraint is imposed on the model to learn the latent features of cells by preserving the structural information of the networks, thereby significantly improving the performance of algorithms. Finally, we track the trajectory of cells by exploring the relationships among cell types. Fourteen scRNA-seq datasets are adopted to validate the performance of algorithms with the number of single cells varying from 49 to 26,484. The experimental results demonstrate that SLNMF significantly outperforms fifteen state-of-the-art methods with 15.32% improvement in terms of accuracy, and it accurately identifies the trajectories of cells. The proposed model and methods provide an effective strategy to analyze scRNA-seq data. (The software is coded using matlab, and is freely available for academic https://github.com/xkmaxidian/SLNMF).
Mesh-Begriff(e)	Gene Expression Profiling/methods ; Single-Cell Gene Expression Analysis ; Sequence Analysis, RNA/methods ; Single-Cell Analysis/methods ; Algorithms ; Cluster Analysis
Sprache	Englisch
Erscheinungsdatum	2023-02-03
Erscheinungsland	United States
Dokumenttyp	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1557-9964
ISSN (online)	1557-9964
DOI	10.1109/TCBB.2022.3161131
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Interpretation on the Chinese Guideline for Diagnosis and Treatment of Neuroendocrine Neoplasms from The China Anti-Cancer Association(2022)

LIANG Yun / WU Wenming / NIE Yongzhan / CHEN Jie

Xiehe Yixue Zazhi, Vol 14, Iss 1, Pp 94-

2023 Band 100

Abstract: Neuroendocrine neoplasms (NENs) are a group of malignancies arising from neuroendocrine cells and peptidergic neurons. The high heterogeneity of NENs leads to challenges and complexities in its diagnosis and treatment. Experts from relevant disciplines ... ...

Abstract	Neuroendocrine neoplasms (NENs) are a group of malignancies arising from neuroendocrine cells and peptidergic neurons. The high heterogeneity of NENs leads to challenges and complexities in its diagnosis and treatment. Experts from relevant disciplines were convened by the Society of Neuroendocrine neoplasm of China Anti-Cancer Association to develop this first edition of the Chinese Guideline for diagnosis and treatment of NENs (2022) based on existing evidence combined with domestic and international guidelines and consensus. In this article, we summarize the important contents of this guideline and make further discussion on some controversial issues, with the aim to provide treatment reference in clinical practice.
Schlagwörter	neuroendocrine neoplasms ; diagnosis ; treatment ; guideline ; interpretation ; Medicine ; R
Sprache	Chinesisch
Erscheinungsdatum	2023-01-01T00:00:00Z
Verlag	Editorial Office of Medical Journal of Peking Union Medical College Hospital
Dokumenttyp	Artikel ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel ; Online: A pressure-driven gas-diffusion/permeation micropump for self-activated sample transport in an extreme micro-environment.

Wu, Wenming

The Analyst

2018 Band 143, Heft 20, Seite(n) 4819–4835

Abstract: The micropump is the most important functional unit of a micro total analysis system (μTAS). An ideal microfluidics system should adopt simple, stable, robust, inexpensive, and integrated on-chip strategies to transport samples for downstream ... ...

Abstract	The micropump is the most important functional unit of a micro total analysis system (μTAS). An ideal microfluidics system should adopt simple, stable, robust, inexpensive, and integrated on-chip strategies to transport samples for downstream applications, with little or no external energy consumption and limited manual intervention. Nevertheless, it remains a key challenge for traditional micropumps to be directly integrated into self-contained and disposable μTAS for velocity-stable and passive sample transport. The best way to assess the capability of passive micropumps is to evaluate their pumping performance in extreme environments, e.g. a 3D configurated microchannel instead of a 2D configuration, high temperature conditions instead of at room temperature, a long microchannel instead of short microchannel, a complex topological microsystem (e.g. a microvascular network interconnecting multiple inlets and outlets) instead of a simple topological microsystem (e.g. a one-directional microchannel connecting only one inlet and one outlet), and multi-phase microdroplet transport instead of single-phase plug transport. In this review, a novel micropumping methodology - a pressure-driven gas-diffusion/permeation micropump - is described, which is the first review paper dedicated to this subject. A comprehensive overview is provided for comparison between this novel micropumping methodology and traditional passive micropumps, especially for applications in stable velocity control in the aforementioned extreme environments. Compared with mainstream conventional micropumps, we confirm that pressure-driven gas-diffusion/permeation micropumps combine a number of superior properties all into one device, such as small size, simple structure, without the need for microfabrication procedures or external power consumption, strong transport capacity, homogeneous flow velocity, delivery capacity for both multi-phase microdroplets and single-phase plugs, long-distance transport, persistent pumping for both 3D microchannels and complex topological microsystems (e.g. a biomimetic microvasculature), low cost, ease of microdevice integration, bubble suppression and amazing stability at high temperatures. An advanced outlook and perspectives for the future development of this novel micropump are also discussed, which may serve as a starting point for researchers in the microfluidics fields to harness pressure-driven gas-diffusion/permeation micropumps for downstream applications.
Sprache	Englisch
Erscheinungsdatum	2018-09-18
Erscheinungsland	England
Dokumenttyp	Journal Article
ZDB-ID	210747-8
ISSN	1364-5528 ; 0003-2654
ISSN (online)	1364-5528
ISSN	0003-2654
DOI	10.1039/c8an01120f
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Layer-Specific Modules Detection in Cancer Multi-Layer Networks.

Ma, Xiaoke / Zhao, Wei / Wu, Wenming

IEEE/ACM transactions on computational biology and bioinformatics

2023 Band 20, Heft 2, Seite(n) 1170–1179

Abstract: Multi-layer networks provide an effective and efficient tool to model and characterize complex systems with multiple types of interactions, which differ greatly from the traditional single-layer networks. Graph clustering in multi-layer networks is ... ...

Abstract	Multi-layer networks provide an effective and efficient tool to model and characterize complex systems with multiple types of interactions, which differ greatly from the traditional single-layer networks. Graph clustering in multi-layer networks is highly non-trivial since it is difficult to balance the connectivity of clusters and the connection of various layers. The current algorithms for the layer-specific clusters are criticized for the low accuracy and sensitivity to the perturbation of networks. To overcome these issues, a novel algorithm for the layer-specific module in multi-layer networks based on nonnegative matrix factorization (LSNMF) is proposed by explicitly exploring the specific features of vertices. LSNMF first extract features of vertices in multi-layer networks by using nonnegative matrix factorization (NMF) and then decompose features of vertices into the common and specific components. The orthogonality constraint is imposed on the specific components to ensure the specificity of features of vertices, which provides a better strategy to characterize and model the structure of layer-specific modules. The extensive experiments demonstrate that the proposed algorithm dramatically outperforms state-of-the-art baselines in terms of various measurements. Furthermore, LSNMF efficiently extracts stage-specific modules, which are more likely to enrich the known functions, and also associate with the survival time of patients.
Mesh-Begriff(e)	Humans ; Algorithms ; Neoplasms ; Cluster Analysis
Sprache	Englisch
Erscheinungsdatum	2023-04-03
Erscheinungsland	United States
Dokumenttyp	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1557-9964
ISSN (online)	1557-9964
DOI	10.1109/TCBB.2022.3176859
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Multi-View Clustering With Graph Learning for scRNA-Seq Data.

Wu, Wenming / Zhang, Wensheng / Hou, Weimin / Ma, Xiaoke

IEEE/ACM transactions on computational biology and bioinformatics

2023 Band 20, Heft 6, Seite(n) 3535–3546

Abstract: Advances in single-cell biotechnologies have generated the single-cell RNA sequencing (scRNA-seq) of gene expression profiles at cell levels, providing an opportunity to study cellular distribution. Although significant efforts developed in their ... ...

Abstract	Advances in single-cell biotechnologies have generated the single-cell RNA sequencing (scRNA-seq) of gene expression profiles at cell levels, providing an opportunity to study cellular distribution. Although significant efforts developed in their analysis, many problems remain in studying cell types distribution because of the heterogeneity, high dimensionality, and noise of scRNA-seq. In this study, a multi-view clustering with graph learning algorithm (MCGL) for scRNA-seq data is proposed, which consists of multi-view learning, graph learning, and cell type clustering. In order to avoid a single feature space of scRNA-seq being inadequate to comprehensively characterize the functions of cells, MCGL constructs the multiple feature spaces and utilizes multi-view learning to comprehensively characterize scRNA-seq data from different perspectives. MCGL adaptively learns the similarity graphs of cells that overcome the dependence on fixed similarity, transforming scRNA-seq analysis into the analysis of multi-view clustering. MCGL decomposes the networks of cells into view-specific and common networks in multi-view learning, which better characterizes the topological relationship of cells. MCGL simultaneously utilizes multiple types of cell-cell networks and fully exploits the connection relationship between cells through the complementarity between networks to improve clustering performance. The graph learning, graph factorization, and cell-type clustering processes are accomplished simultaneously under one optimization framework. The performance of the MCGL algorithm is validated with ten scRNA-seq datasets from different scales, and experimental results imply that the proposed algorithm significantly outperforms fourteen state-of-the-art scRNA-seq algorithms.
Mesh-Begriff(e)	Single-Cell Gene Expression Analysis ; Single-Cell Analysis/methods ; Algorithms ; Transcriptome ; Cluster Analysis ; Sequence Analysis, RNA/methods ; Gene Expression Profiling/methods
Sprache	Englisch
Erscheinungsdatum	2023-12-25
Erscheinungsland	United States
Dokumenttyp	Journal Article
ISSN	1557-9964
ISSN (online)	1557-9964
DOI	10.1109/TCBB.2023.3298334
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Clustering of Multilayer Networks Using Joint Learning Algorithm With Orthogonality and Specificity of Features.

Wu, Wenming / Gong, Maoguo / Ma, Xiaoke

IEEE transactions on cybernetics

2023 Band 53, Heft 8, Seite(n) 4972–4985

Abstract: Complex systems in nature and society consist of various types of interactions, where each type of interaction belongs to a layer, resulting in the so-called multilayer networks. Identifying specific modules for each layer is of great significance for ... ...

Abstract	Complex systems in nature and society consist of various types of interactions, where each type of interaction belongs to a layer, resulting in the so-called multilayer networks. Identifying specific modules for each layer is of great significance for revealing the structure-function relations in multilayer networks. However, the available approaches are criticized undesirable because they fail to explicitly the specificity of modules, and balance the specificity and connectivity of modules. To overcome these drawbacks, we propose an accurate and flexible algorithm by joint learning matrix factorization and sparse representation (jMFSR) for specific modules in multilayer networks, where matrix factorization extracts features of vertices and sparse representation discovers specific modules. To exploit the discriminative latent features of vertices in multilayer networks, jMFSR incorporates linear discriminant analysis (LDA) into non-negative matrix factorization (NMF) to learn features of vertices that distinguish the categories. To explicitly measure the specificity of features, jMFSR decomposes features of vertices into common and specific parts, thereby enhancing the quality of features. Then, jMFSR jointly learns feature extraction, common-specific feature factorization, and clustering of multilayer networks. The experiments on 11 datasets indicate that jMFSR significantly outperforms state-of-the-art baselines in terms of various measurements.
Sprache	Englisch
Erscheinungsdatum	2023-07-18
Erscheinungsland	United States
Dokumenttyp	Journal Article
ISSN	2168-2275
ISSN (online)	2168-2275
DOI	10.1109/TCYB.2022.3152723
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Research Advances of Hyperlipidemic Pancreatitis

ZHAO Huijia / WU Dong / WU Wenming

Xiehe Yixue Zazhi, Vol 13, Iss 4, Pp 637-

2022 Band 643

Abstract: Hyperlipidemic pancreatitis (HLP) is one of the most important causes of acute pancreatitis. The pathogenesis is associated with the accumulation of free fatty acids which can activate the inflammatory response. Compared with other causes, HLP is more ... ...

Abstract	Hyperlipidemic pancreatitis (HLP) is one of the most important causes of acute pancreatitis. The pathogenesis is associated with the accumulation of free fatty acids which can activate the inflammatory response. Compared with other causes, HLP is more likely to have the clinical features characterized by more severe symptoms, more complications, and higher likelihood of persistent organ failure. The HLP patients always need lipid lowering therapy such as apheresis or insulin therapy to reduce serum triglyceride rapidly. Assessment of the presence of hyperlipidemia in patients of acute pancreatitis and prompt management are crucial for the prognosis and the prevention of recurrence. This article reviews the etiology, pathogenesis, clinical features, and treatment of hypertriglyceridemia-induced acute pancreatitis.
Schlagwörter	hyperlipidemic pancreatitis ; hypertriglyceridemia ; acute pancreatitis ; Medicine ; R
Sprache	Chinesisch
Erscheinungsdatum	2022-07-01T00:00:00Z
Verlag	Editorial Office of Medical Journal of Peking Union Medical College Hospital
Dokumenttyp	Artikel ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel ; Online: Expression and related mechanisms of miR-330-3p and S100B in an animal model of cartilage injury.

Wu, Wenming / Liang, Dongming

The Journal of international medical research

2021 Band 49, Heft 9, Seite(n) 3000605211039471

Abstract: Objective: To investigate the roles of and relationship between microRNA (miR)-330-3p and S100 calcium-binding protein B (S100B) in an animal model of cartilage injury.: Methods: This study included 30 New Zealand male rabbits randomly divided into ... ...

Abstract	Objective: To investigate the roles of and relationship between microRNA (miR)-330-3p and S100 calcium-binding protein B (S100B) in an animal model of cartilage injury. Methods: This study included 30 New Zealand male rabbits randomly divided into three groups: an intervention group, a model group and a sham surgery control group. Modelling was performed in the intervention and model groups, but in the sham surgery group, only the skin was cut. After modelling, the intervention and model groups were injected with the miR-330-3p overexpression vector GV268-miR-330-3p or the control GV268-N-ODN vector, respectively, twice a week for 7 weeks. Results: Levels of interleukin-1β and tumour necrosis factor-α in the synovial fluid were significantly higher in the model group than in the intervention and control groups. The level of miR-330-3p in the cartilage tissue was significantly higher in the control group than in the model group but it was significantly lower compared with the intervention group. Levels of S100B, fibroblast growth factor receptor 1 and fibroblast growth factor-2 in the cartilage tissue of rabbits in the model group were significantly higher compared with the control and intervention groups. Conclusion: These findings demonstrate that the upregulation of miR-330-3p can inhibit the expression of S100B.
Mesh-Begriff(e)	Animals ; Cartilage ; Cartilage Diseases ; Disease Models, Animal ; Male ; MicroRNAs/genetics ; Rabbits ; Synovial Fluid
Chemische Substanzen	MicroRNAs
Sprache	Englisch
Erscheinungsdatum	2021-09-28
Erscheinungsland	England
Dokumenttyp	Journal Article
ZDB-ID	184023-x
ISSN	1473-2300 ; 0300-0605 ; 0142-2596
ISSN (online)	1473-2300
ISSN	0300-0605 ; 0142-2596
DOI	10.1177/03000605211039471
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Gut resistome profiling reveals high diversity and fluctuations in pancreatic cancer cohorts.

Liu, Xudong / Li, Kexin / Yang, Yun / Cao, Dingyan / Xu, Xinjie / He, Zilong / Wu, Wenming

Frontiers in cellular and infection microbiology

2024 Band 14, Seite(n) 1354234

Abstract: Background: Pancreatic cancer is one of the deadliest cancer, with a 5-year overall survival rate of 11%. Unfortunately, most patients are diagnosed with advanced stage by the time they present with symptoms. In the past decade, microbiome studies have ... ...

Abstract	Background: Pancreatic cancer is one of the deadliest cancer, with a 5-year overall survival rate of 11%. Unfortunately, most patients are diagnosed with advanced stage by the time they present with symptoms. In the past decade, microbiome studies have explored the association of pancreatic cancer with the human oral and gut microbiomes. However, the gut microbial antibiotic resistance genes profiling of pancreatic cancer patients was never reported compared to that of the healthy cohort. Results: In this study, we addressed the gut microbial antibiotic resistance genes profile using the metagenomic data from two online public pancreatic cancer cohorts. We found a high degree of data concordance between the two cohorts, which can therefore be used for cross-sectional comparisons. Meanwhile, we used two strategies to predict antibiotic resistance genes and compared the advantages and disadvantages of these two approaches. We also constructed microbe-antibiotic resistance gene networks and found that most of the hub nodes in the networks were antibiotic resistance genes. Conclusions: In summary, we describe the panorama of antibiotic resistance genes in the gut microbes of patients with pancreatic cancer. We hope that our study will provide new perspectives on treatment options for the disease.
Mesh-Begriff(e)	Humans ; Cross-Sectional Studies ; Anti-Bacterial Agents/pharmacology ; Anti-Bacterial Agents/therapeutic use ; Drug Resistance, Microbial ; Microbiota/genetics ; Pancreatic Neoplasms/genetics ; Metagenomics
Chemische Substanzen	Anti-Bacterial Agents
Sprache	Englisch
Erscheinungsdatum	2024-02-07
Erscheinungsland	Switzerland
Dokumenttyp	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2619676-1
ISSN	2235-2988 ; 2235-2988
ISSN (online)	2235-2988
ISSN	2235-2988
DOI	10.3389/fcimb.2024.1354234
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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