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  1. AU="Liu, Weihuang"
  2. AU="Nijhuis, Monique"
  3. AU="Ye, Jin-Rong"
  4. AU="Van Not, Hans Pieter"
  5. AU="Liang, Xiao-Hui"
  6. AU="Romano, Raffaella"
  7. AU="Gilles Subra"
  8. AU="Potocnik, Ana"
  9. AU="Butt, Christine"

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  1. Artikel ; Online: Lycopene suppresses gastric cancer cell growth without affecting normal gastric epithelial cells.

    Zhou, Ying / Fu, Rishun / Yang, Mei / Liu, Weihuang / Tong, Zan

    The Journal of nutritional biochemistry

    2023  Band 116, Seite(n) 109313

    Abstract: Gastric cancer is one of the leading causes of cancer-related death worldwide. Lycopene, a natural carotenoid, has potent antioxidant activity and anti-cancer effects against several types of cancers. However, the mechanism for the anti-gastric cancer ... ...

    Abstract Gastric cancer is one of the leading causes of cancer-related death worldwide. Lycopene, a natural carotenoid, has potent antioxidant activity and anti-cancer effects against several types of cancers. However, the mechanism for the anti-gastric cancer effects of lycopene remains to be fully clarified. Normal gastric epithelial cell line GES-1 and gastric cancer cell line AGS, SGC-7901, Hs746T cells were treated with different concentrations of lycopene and the effects of lycopene were compared. Lycopene specifically suppressed cell growth monitored by Real-Time Cell Analyzer, induced cell cycle arrest and cell apoptosis detected by flow cytometry, and lowered mitochondrial membrane potentials assessed by JC-1 staining of AGS and SGC-7901 cells, while did not affect those of GES-1 cells. Lycopene did not affect the cell growth of Hs746T cells harboring TP53 mutation. Further bioinformatics analysis predicted 57 genes with up-regulated expression levels in gastric cancer and decreased function in cells after lycopene treatment. Quantitative PCR and Western Blot were used to check the critical factors in the cell cycle and apoptosis signaling pathway. Lycopene decreased the high expression levels of CCNE1 and increased the levels of TP53 in AGS and SGC-7901 cells without affecting those in GES-1 cells. In summary, lycopene could effectively suppress gastric cancer cells with CCNE1-amplification, which could be a promising target therapy reagent for gastric cancer.
    Mesh-Begriff(e) Humans ; Lycopene/pharmacology ; Stomach Neoplasms/genetics ; Apoptosis ; Epithelial Cells/metabolism ; Cell Proliferation ; Cell Line, Tumor
    Chemische Substanzen Lycopene (SB0N2N0WV6)
    Sprache Englisch
    Erscheinungsdatum 2023-03-04
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1014929-6
    ISSN 1873-4847 ; 0955-2863
    ISSN (online) 1873-4847
    ISSN 0955-2863
    DOI 10.1016/j.jnutbio.2023.109313
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel: Deep learning for COVID-19 chest CT (computed tomography) image analysis: A lesson from lung cancer.

    Jiang, Hao / Tang, Shiming / Liu, Weihuang / Zhang, Yang

    Computational and structural biotechnology journal

    2021  Band 19, Seite(n) 1391–1399

    Abstract: As a recent global health emergency, the quick and reliable diagnosis of COVID-19 is urgently needed. Thus, many artificial intelligence (AI)-base methods are proposed for COVID-19 chest CT (computed tomography) image analysis. However, there are very ... ...

    Abstract As a recent global health emergency, the quick and reliable diagnosis of COVID-19 is urgently needed. Thus, many artificial intelligence (AI)-base methods are proposed for COVID-19 chest CT (computed tomography) image analysis. However, there are very limited COVID-19 chest CT images publicly available to evaluate those deep neural networks. On the other hand, a huge amount of CT images from lung cancer are publicly available. To build a reliable deep learning model trained and tested with a larger scale dataset, the proposed model builds a public COVID-19 CT dataset, containing 1186 CT images synthesized from lung cancer CT images using CycleGAN. Additionally, various deep learning models are tested with synthesized or real chest CT images for COVID-19 and Non-COVID-19 classification. In comparison, all models achieve excellent results in accuracy, precision, recall and F1 score for both synthesized and real COVID-19 CT images, demonstrating the reliable of the synthesized dataset. The public dataset and deep learning models can facilitate the development of accurate and efficient diagnostic testing for COVID-19.
    Sprache Englisch
    Erscheinungsdatum 2021-03-02
    Erscheinungsland Netherlands
    Dokumenttyp Journal Article
    ZDB-ID 2694435-2
    ISSN 2001-0370
    ISSN 2001-0370
    DOI 10.1016/j.csbj.2021.02.016
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Buch ; Online: Explicit Visual Prompting for Universal Foreground Segmentations

    Liu, Weihuang / Shen, Xi / Pun, Chi-Man / Cun, Xiaodong

    2023  

    Abstract: Foreground segmentation is a fundamental problem in computer vision, which includes salient object detection, forgery detection, defocus blur detection, shadow detection, and camouflage object detection. Previous works have typically relied on domain- ... ...

    Abstract Foreground segmentation is a fundamental problem in computer vision, which includes salient object detection, forgery detection, defocus blur detection, shadow detection, and camouflage object detection. Previous works have typically relied on domain-specific solutions to address accuracy and robustness issues in those applications. In this paper, we present a unified framework for a number of foreground segmentation tasks without any task-specific designs. We take inspiration from the widely-used pre-training and then prompt tuning protocols in NLP and propose a new visual prompting model, named Explicit Visual Prompting (EVP). Different from the previous visual prompting which is typically a dataset-level implicit embedding, our key insight is to enforce the tunable parameters focusing on the explicit visual content from each individual image, i.e., the features from frozen patch embeddings and high-frequency components. Our method freezes a pre-trained model and then learns task-specific knowledge using a few extra parameters. Despite introducing only a small number of tunable parameters, EVP achieves superior performance than full fine-tuning and other parameter-efficient fine-tuning methods. Experiments in fourteen datasets across five tasks show the proposed method outperforms other task-specific methods while being considerably simple. The proposed method demonstrates the scalability in different architectures, pre-trained weights, and tasks. The code is available at: https://github.com/NiFangBaAGe/Explicit-Visual-Prompt.

    Comment: arXiv admin note: substantial text overlap with arXiv:2303.10883
    Schlagwörter Computer Science - Computer Vision and Pattern Recognition
    Thema/Rubrik (Code) 004
    Erscheinungsdatum 2023-05-29
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  4. Artikel: Correction of Out-of-focus Microscopic Images by Deep Learning

    Zhang, Chi / Jiang, Hao / Liu, Weihuang / Li, Junyi / Tang, Shimin / Juhas, Mario / Zhang, Yang

    Computational and Structural Biotechnology Journal. 2022 Apr. 02,

    2022  

    Abstract: Microscopic images are widely used in basic biomedical research, disease diagnosis and medical discovery. Obtaining high-quality in-focus microscopy images has been a cornerstone of the microscopy. However, images obtained by microscopes are often out-of- ...

    Abstract Microscopic images are widely used in basic biomedical research, disease diagnosis and medical discovery. Obtaining high-quality in-focus microscopy images has been a cornerstone of the microscopy. However, images obtained by microscopes are often out-of-focus, resulting in poor performance in research and diagnosis. To solve the out-of-focus issue in microscopy, we developed a Cycle Generative Adversarial Network (CycleGAN) based model and a multi-component weighted loss function. We train and test our network in two self-collected datasets, namely Leishmania parasite dataset captured by a bright-field microscope, and bovine pulmonary artery endothelial cells (BPAEC) captured by a confocal fluorescence microscope. In comparison to other GAN-based deblurring methods, the proposed model reached state-of-the-art performance in correction. Another publicly available dataset, human cells dataset from the Broad Bioimage Benchmark Collection is used for evaluating the generalization abilities of the model. Our model showed excellent generalization capability, which could transfer to different types of microscopic image datasets. Code and dataset are publicly available at: https://github.com/jiangdat/COMI.
    Schlagwörter Leishmania ; bioimaging ; biomedical research ; biotechnology ; cattle ; data collection ; disease diagnosis ; fluorescence microscopes ; humans ; models ; parasites ; pulmonary artery
    Sprache Englisch
    Erscheinungsverlauf 2022-0402
    Erscheinungsort Elsevier B.V.
    Dokumenttyp Artikel
    Anmerkung Pre-press version
    ZDB-ID 2694435-2
    ISSN 2001-0370
    ISSN 2001-0370
    DOI 10.1016/j.csbj.2022.04.003
    Datenquelle NAL Katalog (AGRICOLA)

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  5. Artikel: Fine-Grained Breast Cancer Classification With Bilinear Convolutional Neural Networks (BCNNs).

    Liu, Weihuang / Juhas, Mario / Zhang, Yang

    Frontiers in genetics

    2020  Band 11, Seite(n) 547327

    Abstract: Classification of histopathological images of cancer is challenging even for well-trained professionals, due to the fine-grained variability of the disease. Deep Convolutional Neural Networks (CNNs) showed great potential for classification of a number ... ...

    Abstract Classification of histopathological images of cancer is challenging even for well-trained professionals, due to the fine-grained variability of the disease. Deep Convolutional Neural Networks (CNNs) showed great potential for classification of a number of the highly variable fine-grained objects. In this study, we introduce a Bilinear Convolutional Neural Networks (BCNNs) based deep learning method for fine-grained classification of breast cancer histopathological images. We evaluated our model by comparison with several deep learning algorithms for fine-grained classification. We used bilinear pooling to aggregate a large number of orderless features without taking into consideration the disease location. The experimental results on BreaKHis, a publicly available breast cancer dataset, showed that our method is highly accurate with 99.24% and 95.95% accuracy in binary and in fine-grained classification, respectively.
    Sprache Englisch
    Erscheinungsdatum 2020-09-04
    Erscheinungsland Switzerland
    Dokumenttyp Journal Article
    ZDB-ID 2606823-0
    ISSN 1664-8021
    ISSN 1664-8021
    DOI 10.3389/fgene.2020.547327
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  6. Artikel: Correction of out-of-focus microscopic images by deep learning.

    Zhang, Chi / Jiang, Hao / Liu, Weihuang / Li, Junyi / Tang, Shiming / Juhas, Mario / Zhang, Yang

    Computational and structural biotechnology journal

    2022  Band 20, Seite(n) 1957–1966

    Abstract: Motivation: Microscopic images are widely used in basic biomedical research, disease diagnosis and medical discovery. Obtaining high-quality in-focus microscopy images has been a cornerstone of the microscopy. However, images obtained by microscopes are ...

    Abstract Motivation: Microscopic images are widely used in basic biomedical research, disease diagnosis and medical discovery. Obtaining high-quality in-focus microscopy images has been a cornerstone of the microscopy. However, images obtained by microscopes are often out-of-focus, resulting in poor performance in research and diagnosis.
    Results: To solve the out-of-focus issue in microscopy, we developed a Cycle Generative Adversarial Network (CycleGAN) based model and a multi-component weighted loss function. We train and test our network in two self-collected datasets, namely Leishmania parasite dataset captured by a bright-field microscope, and bovine pulmonary artery endothelial cells (BPAEC) captured by a confocal fluorescence microscope. In comparison to other GAN-based deblurring methods, the proposed model reached state-of-the-art performance in correction. Another publicly available dataset, human cells dataset from the Broad Bioimage Benchmark Collection is used for evaluating the generalization abilities of the model. Our model showed excellent generalization capability, which could transfer to different types of microscopic image datasets.
    Availability and implementation: Code and dataset are publicly available at: https://github.com/jiangdat/COMI.
    Sprache Englisch
    Erscheinungsdatum 2022-04-20
    Erscheinungsland Netherlands
    Dokumenttyp Journal Article
    ZDB-ID 2694435-2
    ISSN 2001-0370
    ISSN 2001-0370
    DOI 10.1016/j.csbj.2022.04.003
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  7. Buch ; Online: CoordFill

    Liu, Weihuang / Cun, Xiaodong / Pun, Chi-Man / Xia, Menghan / Zhang, Yong / Wang, Jue

    Efficient High-Resolution Image Inpainting via Parameterized Coordinate Querying

    2023  

    Abstract: Image inpainting aims to fill the missing hole of the input. It is hard to solve this task efficiently when facing high-resolution images due to two reasons: (1) Large reception field needs to be handled for high-resolution image inpainting. (2) The ... ...

    Abstract Image inpainting aims to fill the missing hole of the input. It is hard to solve this task efficiently when facing high-resolution images due to two reasons: (1) Large reception field needs to be handled for high-resolution image inpainting. (2) The general encoder and decoder network synthesizes many background pixels synchronously due to the form of the image matrix. In this paper, we try to break the above limitations for the first time thanks to the recent development of continuous implicit representation. In detail, we down-sample and encode the degraded image to produce the spatial-adaptive parameters for each spatial patch via an attentional Fast Fourier Convolution(FFC)-based parameter generation network. Then, we take these parameters as the weights and biases of a series of multi-layer perceptron(MLP), where the input is the encoded continuous coordinates and the output is the synthesized color value. Thanks to the proposed structure, we only encode the high-resolution image in a relatively low resolution for larger reception field capturing. Then, the continuous position encoding will be helpful to synthesize the photo-realistic high-frequency textures by re-sampling the coordinate in a higher resolution. Also, our framework enables us to query the coordinates of missing pixels only in parallel, yielding a more efficient solution than the previous methods. Experiments show that the proposed method achieves real-time performance on the 2048$\times$2048 images using a single GTX 2080 Ti GPU and can handle 4096$\times$4096 images, with much better performance than existing state-of-the-art methods visually and numerically. The code is available at: https://github.com/NiFangBaAGe/CoordFill.

    Comment: Accepted by AAAI 2023
    Schlagwörter Computer Science - Computer Vision and Pattern Recognition
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2023-03-15
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  8. Artikel ; Online: Absence of PD-L1 signaling hinders macrophage defense against Mycobacterium tuberculosis via upregulating STAT3/IL-6 pathway.

    Qu, Peijie / Li, Xinyu / Liu, Weihuang / Zhou, Fangting / Xu, Xiaoxu / Tang, Jun / Sun, Mengmeng / Li, Junli / Li, Haifeng / Han, Yunlin / Hu, Chengjun / Lei, Yueshan / Pan, Qin / Zhan, Lingjun

    Microbes and infection

    2024  , Seite(n) 105352

    Abstract: The blockade of programmed death-ligand 1 (PD-L1) pathway has been clinically used in cancer immunotherapy, while its effects on infectious diseases remain elusive. Roles of PD-L1 signaling in the macrophage-mediated innate immune defense against M.tb is ...

    Abstract The blockade of programmed death-ligand 1 (PD-L1) pathway has been clinically used in cancer immunotherapy, while its effects on infectious diseases remain elusive. Roles of PD-L1 signaling in the macrophage-mediated innate immune defense against M.tb is unclear. In this study, the outcomes of tuberculosis (TB) in wild-type (WT) mice treated with anti-PD-1/PD-L1 therapy and macrophage-specific Pdl1-knockout (Pdl1
    Sprache Englisch
    Erscheinungsdatum 2024-05-08
    Erscheinungsland France
    Dokumenttyp Journal Article
    ZDB-ID 1465093-9
    ISSN 1769-714X ; 1286-4579
    ISSN (online) 1769-714X
    ISSN 1286-4579
    DOI 10.1016/j.micinf.2024.105352
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  9. Artikel ; Online: Evaluation of Immune Restoration Potential of PD-1 Blockers.

    Liu, Weihuang / Tong, Zan

    Journal of immunoassay & immunochemistry

    2015  Band 36, Heft 6, Seite(n) 567–572

    Abstract: The programmed death 1 (PD-1) receptor and its primary ligand (PD-L1) have crucial roles in tumor-induced immune suppression. PD-1/PD-L1 blockers are designed to restore the immune system and induce potent antitumor effects. In this study we established ... ...

    Abstract The programmed death 1 (PD-1) receptor and its primary ligand (PD-L1) have crucial roles in tumor-induced immune suppression. PD-1/PD-L1 blockers are designed to restore the immune system and induce potent antitumor effects. In this study we established a direct and reliable method to evaluate the immune restoration potential of human PD-1 blockers. We found anti-human PD-1 antibody could reverse PD-L1 induced suppression of human CD3+ cells proliferation and IL-2 production. This method is suitable for all kinds of PD-1 blockers including antibodies and chemical drugs. This function assay could be easily applied and provide valuable information for drug development.
    Mesh-Begriff(e) Adult ; Antibodies/pharmacology ; B7-H1 Antigen/immunology ; Female ; Humans ; Leukocytes, Mononuclear/immunology ; Male ; Programmed Cell Death 1 Receptor/antagonists & inhibitors ; Programmed Cell Death 1 Receptor/immunology
    Chemische Substanzen Antibodies ; B7-H1 Antigen ; CD274 protein, human ; PDCD1 protein, human ; Programmed Cell Death 1 Receptor
    Sprache Englisch
    Erscheinungsdatum 2015
    Erscheinungsland England
    Dokumenttyp Evaluation Studies ; Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2050610-7
    ISSN 1532-4230 ; 1532-1819
    ISSN (online) 1532-4230
    ISSN 1532-1819
    DOI 10.1080/15321819.2015.1017104
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 1606: Hefte anzeigen Standort:
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  10. Artikel ; Online: Transcriptomic Signature of 3D Hierarchical Porous Chip Enriched Exosomes for Early Detection and Progression Monitoring of Hepatocellular Carcinoma.

    Yi, Kezhen / Wang, Yike / Rong, Yuan / Bao, Yiru / Liang, Yingxue / Chen, Yiyi / Liu, Fusheng / Zhang, Shikun / He, Yuan / Liu, Weihuang / Zhu, Chengliang / Wu, Long / Peng, Jin / Chen, Hao / Huang, Weihua / Yuan, Yufeng / Xie, Min / Wang, Fubing

    Advanced science (Weinheim, Baden-Wurttemberg, Germany)

    2024  Band 11, Heft 14, Seite(n) e2305204

    Abstract: Hepatocellular carcinoma (HCC) is a highly lethal malignant tumor, and the current non-invasive diagnosis method based on serum markers, such as α-fetoprotein (AFP), and des-γ-carboxy-prothrombin (DCP), has limited efficacy in detecting it. Therefore, ... ...

    Abstract Hepatocellular carcinoma (HCC) is a highly lethal malignant tumor, and the current non-invasive diagnosis method based on serum markers, such as α-fetoprotein (AFP), and des-γ-carboxy-prothrombin (DCP), has limited efficacy in detecting it. Therefore, there is a critical need to develop novel biomarkers for HCC. Recent studies have highlighted the potential of exosomes as biomarkers. To enhance exosome enrichment, a silicon dioxide (SiO
    Mesh-Begriff(e) Humans ; Carcinoma, Hepatocellular/diagnosis ; Carcinoma, Hepatocellular/genetics ; alpha-Fetoproteins/analysis ; Liver Neoplasms/diagnosis ; Liver Neoplasms/genetics ; Biomarkers, Tumor/genetics ; Exosomes/genetics ; Exosomes/chemistry ; RNA, Long Noncoding ; Porosity ; Silicon Dioxide ; Gene Expression Profiling
    Chemische Substanzen alpha-Fetoproteins ; Biomarkers, Tumor ; RNA, Long Noncoding ; Silicon Dioxide (7631-86-9)
    Sprache Englisch
    Erscheinungsdatum 2024-02-07
    Erscheinungsland Germany
    Dokumenttyp Journal Article
    ZDB-ID 2808093-2
    ISSN 2198-3844 ; 2198-3844
    ISSN (online) 2198-3844
    ISSN 2198-3844
    DOI 10.1002/advs.202305204
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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