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Article ; Online: Tripartite motif 2b (

Fang, Hong / Wu, Xiao Man / Zheng, Si Yao / Chang, Ming Xian

2024 , Page(s) e0015824

Abstract: Tripartite motif (TRIM) proteins are involved in different cellular functions, including regulating virus infection. In teleosts, two orthologous genes of mammalian TRIM2 are identified. However, the functions and molecular mechanisms of piscine TRIM2 ... ...

Abstract	Tripartite motif (TRIM) proteins are involved in different cellular functions, including regulating virus infection. In teleosts, two orthologous genes of mammalian TRIM2 are identified. However, the functions and molecular mechanisms of piscine TRIM2 remain unclear. Here, we show that
Language	English
Publishing date	2024-05-02
Publishing country	United States
Document type	Journal Article
ZDB-ID	80174-4
ISSN	1098-5514 ; 0022-538X
ISSN (online)	1098-5514
ISSN	0022-538X
DOI	10.1128/jvi.00158-24
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Zs.A 609: Show issues

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Book ; Thesis: Towards onions and shallots (Allium cepa L.) resistant to beet armyworm (Spodoptera exigua Hübner) by transgenesis and conventional breeding

Zheng, Si-Jun

2000

Author's details	Si-Jun Zheng
Keywords	Küchenzwiebel ; Zuckerrübeneule ; Resistenzzüchtung ; Transgene Pflanzen
Subject	Laphygma exigua ; Spodoptera exigua ; Caradrina exigua ; Laphygma flavimaculata ; Heerwurm ; Allium cepa ; Gemeine Zwiebel ; Speisezwiebel ; Zwiebel ; Gentechnisch veränderte Pflanzen
Language	English
Size	146 S. : Ill., graph. Darst.
Publishing country	Netherlands
Document type	Book ; Thesis
Thesis / German Habilitation thesis	Wageningen, Univ., Diss., 2000
Note	Zsfassung in chines. und niederländ. Sprache
HBZ-ID	HT012882854
ISBN	90-5808-296-2 ; 978-90-5808-296-1
Database	Catalogue ZB MED Nutrition, Environment, Agriculture

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008/2031	available for loan (reading room)	Freihandmagazin (U1)

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Article ; Online: Biomimetic Design of Peptide Inhibitor to Block CD47/SIRPα Interactions.

Zheng, Si / Ji, Yufan / Li, Nanxing / Zhang, Lin

Langmuir : the ACS journal of surfaces and colloids

2023 Volume 39, Issue 49, Page(s) 18101–18112

Abstract: CD47 on the surface of tumor cells has become a research hot spot in immunotherapy and anticancer therapy, as it can bind to SIRPα protein on the surface of macrophages, which ultimately leads to immune escape of tumor cells. In the present study, ... ...

Abstract	CD47 on the surface of tumor cells has become a research hot spot in immunotherapy and anticancer therapy, as it can bind to SIRPα protein on the surface of macrophages, which ultimately leads to immune escape of tumor cells. In the present study, molecular interactions between CD47 and human SIRPα proteins (including variant 1, V1 and variant 2, V2) were analyzed through molecular dynamics (MD) simulation and the molecular mechanics-Poisson-Boltzmann surface area (MM-PBSA) method. Hydrophobic interactions were found as the main driving force for the binding of CD47 on SIRPα. The residues including pyroglutamate acid (Z)1, L2, E35, Y37, E97, L101, and T102 of CD47 were identified with a significant favorable contribution to the binding of CD47 on SIRPα (both V1 and V2). Based on this, a peptide inhibitor library with the sequence ZLXRTLXEXY was designed (X represents the arbitrary residue of 20 standard amino acids) and then screened using molecular docking, MD simulations, and experimental validation. Finally, a peptide ZLIRTLHEWY was determined with high affinity with SIRPα from 8000 candidates, containing 6/10 residues favorable for the binding on SIRPα V1 and 8/10 residues favorable for the binding on SIRPα V2, which was thus considered to have potential anticancer function.
MeSH term(s)	Humans ; CD47 Antigen/genetics ; CD47 Antigen/metabolism ; Receptors, Immunologic/genetics ; Receptors, Immunologic/metabolism ; Molecular Docking Simulation ; Biomimetics ; Antigens, Differentiation/chemistry ; Antigens, Differentiation/metabolism ; Peptides/pharmacology ; Peptide Library ; Phagocytosis ; Neoplasms
Chemical Substances	CD47 Antigen ; Receptors, Immunologic ; Antigens, Differentiation ; Peptides ; Peptide Library ; CD47 protein, human
Language	English
Publishing date	2023-12-01
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2005937-1
ISSN	1520-5827 ; 0743-7463
ISSN (online)	1520-5827
ISSN	0743-7463
DOI	10.1021/acs.langmuir.3c02898
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Profiling DNA Cargos in Single Extracellular Vesicles via Hydrogel-Based Droplet Digital Multiple Displacement Amplification.

Jiao, Yufeng / Gao, Liyang / Zhang, Tao / He, Ziyi / Zheng, Si-Yang / Liu, Wu

Analytical chemistry

2024 Volume 96, Issue 3, Page(s) 1293–1300

Abstract: Due to the substantial heterogeneity among extracellular vesicle (EV) subpopulations, single-EV analysis has the potential to elucidate the mechanisms behind EV biogenesis and shed light on the myriad functions, leading to the development of novel ... ...

Abstract	Due to the substantial heterogeneity among extracellular vesicle (EV) subpopulations, single-EV analysis has the potential to elucidate the mechanisms behind EV biogenesis and shed light on the myriad functions, leading to the development of novel diagnostics and therapeutics. While many studies have been devoted to reveal between-EV variations in surface proteins and RNAs, DNA cargos (EV-DNA) have received little attention. Here, we report a hydrogel-based droplet digital multiple displacement amplification approach for the comprehensive analysis of EV-DNA at the single-EV level. Single EVs are dispersed in thousands of hydrogel droplets and lysed for DNA amplification and identification. The droplet microfluidics strategy empowers the assay with single-molecule sensitivity and capability for absolute quantification of DNA-containing EVs. In particular, our findings indicate that 5-40% EVs are associated with DNA, depending on the cell of origin. Large EVs exhibit a higher proportion of DNA-containing EVs and a more substantial presence of intraluminal DNA, compared to small EVs. These DNA-containing EVs carry multiple DNA fragments on average. Furthermore, both double-stranded DNA and single-stranded DNA were able to be detected at the single-EV level. Utilizing this method, the abundance, distribution, and biophysical properties of EV-DNA in various EV populations are evaluated. The DNA level within EVs provides insight into the status of the originating cells and offers valuable information on the outcomes of anticancer treatments. The utilization of single-EV analysis for EV-DNA holds significant promise for early cancer detection and treatment response monitoring.
MeSH term(s)	Hydrogels/metabolism ; Extracellular Vesicles/metabolism ; DNA/metabolism ; RNA/metabolism ; Membrane Proteins/metabolism
Chemical Substances	Hydrogels ; DNA (9007-49-2) ; RNA (63231-63-0) ; Membrane Proteins
Language	English
Publishing date	2024-01-08
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1508-8
ISSN	1520-6882 ; 0003-2700
ISSN (online)	1520-6882
ISSN	0003-2700
DOI	10.1021/acs.analchem.3c04666
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Zs.A 4033: Show issues

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Article ; Online: Anti-stroke biologics: from recombinant proteins to stem cells and organoids.

Miao, Zhu-Wei / Wang, Zhi / Zheng, Si-Li / Wang, Shu-Na / Miao, Chao-Yu

Stroke and vascular neurology

2024

Abstract: The use of biologics in various diseases has dramatically increased in recent years. Stroke, a cerebrovascular disease, is the second most common cause of death, and the leading cause of disability with high morbidity worldwide. For biologics applied in ... ...

Abstract	The use of biologics in various diseases has dramatically increased in recent years. Stroke, a cerebrovascular disease, is the second most common cause of death, and the leading cause of disability with high morbidity worldwide. For biologics applied in the treatment of acute ischaemic stroke, alteplase is the only thrombolytic agent. Meanwhile, current clinical trials show that two recombinant proteins, tenecteplase and non-immunogenic staphylokinase, are most promising as new thrombolytic agents for acute ischaemic stroke therapy. In addition, stem cell-based therapy, which uses stem cells or organoids for stroke treatment, has shown promising results in preclinical and early clinical studies. These strategies for acute ischaemic stroke mainly rely on the unique properties of undifferentiated cells to facilitate tissue repair and regeneration. However, there is a still considerable journey ahead before these approaches become routine clinical use. This includes optimising cell delivery methods, determining the ideal cell type and dosage, and addressing long-term safety concerns. This review introduces the current or promising recombinant proteins for thrombolysis therapy in ischaemic stroke and highlights the promise and challenges of stem cells and cerebral organoids in stroke therapy.
Language	English
Publishing date	2024-01-29
Publishing country	England
Document type	Journal Article ; Review
ISSN	2059-8696
ISSN (online)	2059-8696
DOI	10.1136/svn-2023-002883
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Complete Chloroplast Genomes of Four Oaks from the Section

Wang, Ling-Ling / Li, Yu / Zheng, Si-Si / Kozlowski, Gregor / Xu, Jin / Song, Yi-Gang

Genes

2024 Volume 15, Issue 2

Abstract: ... ...

Abstract	Quercus
MeSH term(s)	Phylogeny ; Genome, Chloroplast ; Quercus/genetics ; Ecosystem ; Genomics
Language	English
Publishing date	2024-02-11
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2527218-4
ISSN	2073-4425 ; 2073-4425
ISSN (online)	2073-4425
ISSN	2073-4425
DOI	10.3390/genes15020230
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Article: Editorial: Microbial interaction with banana: mechanisms, symbiosis, and integrated diseases control.

Zheng, Si-Jun / Hu, Huigang / Li, Yunfeng / Chen, Jian / Li, Xundong / Bai, Tingting

Frontiers in microbiology

2024 Volume 15, Page(s) 1390969

Language	English
Publishing date	2024-04-05
Publishing country	Switzerland
Document type	Editorial
ZDB-ID	2587354-4
ISSN	1664-302X
ISSN	1664-302X
DOI	10.3389/fmicb.2024.1390969
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Article: Development of the Antithrombotic Peptide LEKNSTY Targeting the Collagen Surface: I. Design and Validation

Zheng, Si / Hao, Tanyi / Zhang, Lin

Langmuir. 2022 May 27, v. 38, no. 23

2022

Abstract: Exposed collagen on the diseased vessel wall is crucial for arterial thrombosis. The currently developed antithrombotic drugs mostly target blood components such as platelets and suffer from the risk of bleeding. Therefore, anticollagen therapy of ... ...

Abstract	Exposed collagen on the diseased vessel wall is crucial for arterial thrombosis. The currently developed antithrombotic drugs mostly target blood components such as platelets and suffer from the risk of bleeding. Therefore, anticollagen therapy of covering the collagen surface was proposed as an alternative in our previous study, and an antithrombotic peptide LWWNSYY was designed and validated. However, its application was hindered due to the poor water solubility. In the present study, in order to develop a novel antithrombotic peptide with enhanced water solubility, redesigning of LWWNSYY to LEKNSTY using the EK pattern was proposed. Improved solubility was obtained for LEKNSTY. Moreover, the binding of LEKNSTY on the collagen surface was confirmed by molecular docking, molecular dynamics simulations, and experimental validation. A Kd of 0.91 ± 0.44 μM was observed. The effective inhibition of platelet adhesion on the collagen surface by LEKNSTY was demonstrated at an IC₅₀ of 2.48 ± 0.59 μg/mL. Therefore, the successful design of the antithrombotic peptide LEKNSTY was confirmed, which would facilitate the research into the interface involving thrombus and the development of antithrombotic agents.
Keywords	adhesion ; collagen ; molecular dynamics ; peptides ; risk ; therapeutics ; thrombosis ; water solubility
Language	English
Dates of publication	2022-0527
Size	p. 7107-7113.
Publishing place	American Chemical Society
Document type	Article
ZDB-ID	2005937-1
ISSN	1520-5827 ; 0743-7463
ISSN (online)	1520-5827
ISSN	0743-7463
DOI	10.1021/acs.langmuir.2c00586
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: MGREL: A multi-graph representation learning-based ensemble learning method for gene-disease association prediction.

Wang, Ziyang / Gu, Yaowen / Zheng, Si / Yang, Lin / Li, Jiao

Computers in biology and medicine

2023 Volume 155, Page(s) 106642

Abstract: The identification of gene-disease associations plays an important role in the exploration of pathogenic mechanisms and therapeutic targets. Computational methods have been regarded as an effective way to discover the potential gene-disease associations ... ...

Abstract	The identification of gene-disease associations plays an important role in the exploration of pathogenic mechanisms and therapeutic targets. Computational methods have been regarded as an effective way to discover the potential gene-disease associations in recent years. However, most of them ignored the combination of abundant genetic, therapeutic information, and gene-disease network topology. To this end, we re-organized the current gene-disease association benchmark dataset by extracting the newest gene-disease associations from the OMIM database. Then, we developed a multi-graph representation learning-based ensemble model, named MGREL to predict gene-disease associations. MGREL integrated two feature generation channels to extract gene and disease features, including a knowledge extraction channel which learned high-order representations from genetic and therapeutic information, and a graph learning channel which acquired network topological representations through multiple advanced graph representation learning methods. Then, an ensemble learning method with 5 machine learning models was used as the classifier to predict the gene-disease association. Comprehensive experiments have demonstrated the significant performance achieved by MGREL compared to 5 state-of-the-art methods. For the major measurements (AUC = 0.925, AUPR = 0.935), the relative improvements of MGREL compared to the suboptimal methods are 3.24%, and 2.75%, respectively. MGREL also achieved impressive improvements in the challenging tasks of predicting potential associations for unknown genes/diseases. In addition, case studies implied potential applications for MGREL in the discovery of potential therapeutic targets.
MeSH term(s)	Machine Learning ; Computational Biology/methods
Language	English
Publishing date	2023-02-10
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2023.106642
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Article ; Online: Employing Molecular Conformations for Ligand-Based Virtual Screening with Equivariant Graph Neural Network and Deep Multiple Instance Learning.

Gu, Yaowen / Li, Jiao / Kang, Hongyu / Zhang, Bowen / Zheng, Si

Molecules (Basel, Switzerland)

2023 Volume 28, Issue 16

Abstract: Ligand-based virtual screening (LBVS) is a promising approach for rapid and low-cost screening of potentially bioactive molecules in the early stage of drug discovery. Compared with traditional similarity-based machine learning methods, deep learning ... ...

Abstract	Ligand-based virtual screening (LBVS) is a promising approach for rapid and low-cost screening of potentially bioactive molecules in the early stage of drug discovery. Compared with traditional similarity-based machine learning methods, deep learning frameworks for LBVS can more effectively extract high-order molecule structure representations from molecular fingerprints or structures. However, the 3D conformation of a molecule largely influences its bioactivity and physical properties, and has rarely been considered in previous deep learning-based LBVS methods. Moreover, the relative bioactivity benchmark dataset is still lacking. To address these issues, we introduce a novel end-to-end deep learning architecture trained from molecular conformers for LBVS. We first extracted molecule conformers from multiple public molecular bioactivity data and consolidated them into a large-scale bioactivity benchmark dataset, which totally includes millions of endpoints and molecules corresponding to 954 targets. Then, we devised a deep learning-based LBVS called EquiVS to learn molecule representations from conformers for bioactivity prediction. Specifically, graph convolutional network (GCN) and equivariant graph neural network (EGNN) are sequentially stacked to learn high-order molecule-level and conformer-level representations, followed with attention-based deep multiple-instance learning (MIL) to aggregate these representations and then predict the potential bioactivity for the query molecule on a given target. We conducted various experiments to validate the data quality of our benchmark dataset, and confirmed EquiVS achieved better performance compared with 10 traditional machine learning or deep learning-based LBVS methods. Further ablation studies demonstrate the significant contribution of molecular conformation for bioactivity prediction, as well as the reasonability and non-redundancy of deep learning architecture in EquiVS. Finally, a model interpretation case study on CDK2 shows the potential of EquiVS in optimal conformer discovery. The overall study shows that our proposed benchmark dataset and EquiVS method have promising prospects in virtual screening applications.
MeSH term(s)	Ligands ; Molecular Conformation ; Benchmarking ; Data Accuracy ; Neural Networks, Computer
Chemical Substances	Ligands
Language	English
Publishing date	2023-08-09
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	1413402-0
ISSN	1420-3049 ; 1431-5165 ; 1420-3049
ISSN (online)	1420-3049
ISSN	1431-5165 ; 1420-3049
DOI	10.3390/molecules28165982
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