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  1. Book ; Online: Cell Culture

    Zhan, Xianquan

    Advanced Technology and Applications in Medical and Life Sciences

    (Biochemistry ; 30)

    2022  

    Series title Biochemistry ; 30
    Keywords Cellular biology (cytology)
    Language 0|e
    Size 1 electronic resource (154 pages)
    Publisher IntechOpen
    Document type Book ; Online
    Note English ; Open Access
    HBZ-ID HT021613472
    ISBN 9781839694479 ; 1839694475
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  2. Book ; Online: Metabolomics

    Zhan, Xianquan

    Methodology and Applications in Medical Sciences and Life Sciences

    2021  

    Keywords Biochemistry ; biomarkers, drug discovery, metabolites, personalized medicine, transgenic plant, fungus
    Language English
    Size 1 electronic resource (168 pages)
    Publisher IntechOpen
    Document type Book ; Online
    Note English
    HBZ-ID HT030646847
    ISBN 9781839690853 ; 1839690852
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  3. Book ; Online: Ubiquitin : Proteasome Pathway

    Zhan, Xianquan

    2020  

    Keywords Proteins
    Size 1 electronic resource (124 pages)
    Publisher IntechOpen
    Document type Book ; Online
    Note English ; Open Access
    HBZ-ID HT021050148
    ISBN 9781838808426 ; 1838808426
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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    Inter-library loan at ZB MED

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  4. Book ; Online: Proteoforms : Concept and Applications in Medical Sciences

    Zhan, Xianquan

    2020  

    Keywords Proteins
    Size 1 electronic resource (90 pages)
    Publisher IntechOpen
    Document type Book ; Online
    Note English ; Open Access
    HBZ-ID HT021049141
    ISBN 9781839628320 ; 1839628324
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  5. Book: Greenhouse gas emission and mitigation in municipal wastewater treatment plants

    Zhan, Xinmin / Hu, Zhenhu / Wu, Guangxue

    2018  

    Author's details Xinmin Zhan, Zhenhu Hu, Guangxue Wu
    Language English
    Size x, 150 Seiten
    Publisher IWA Publishing
    Publishing place London
    Publishing country Great Britain
    Document type Book
    HBZ-ID HT018408912
    ISBN 978-1-78040-630-5 ; 9781780406312 ; 9781780409054 ; 1-78040-630-4 ; 1780406312 ; 1780409052
    Database Catalogue ZB MED Nutrition, Environment, Agriculture

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    In stock of ZB MED Bonn / Germany

    Shelf markLoan statusLocation
    188/119 available for loan (reading room) Freihandmagazin (U1)

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  6. Book ; Online: Molecular network study of pituitary adenomas

    Zhan, Xianquan / Desiderio, Dominic M.

    2020  

    Keywords Medicine ; Endocrinology ; pituitary adenoma ; signaling pathway ; molecular network ; multi-omics ; personalized medicine ; precision medicine ; therapeutic target ; molecule-panel biomarker
    Size 1 electronic resource (210 pages)
    Publisher Frontiers Media SA
    Document type Book ; Online
    Note English ; Open Access
    HBZ-ID HT021230853
    ISBN 9782889636020 ; 288963602X
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  7. Article ; Online: Predicting lncRNA-disease associations using multiple metapaths in hierarchical graph attention networks.

    Yao, Dengju / Deng, Yuexiao / Zhan, Xiaojuan / Zhan, Xiaorong

    BMC bioinformatics

    2024  Volume 25, Issue 1, Page(s) 46

    Abstract: Background: Many biological studies have shown that lncRNAs regulate the expression of epigenetically related genes. The study of lncRNAs has helped to deepen our understanding of the pathogenesis of complex diseases at the molecular level. Due to the ... ...

    Abstract Background: Many biological studies have shown that lncRNAs regulate the expression of epigenetically related genes. The study of lncRNAs has helped to deepen our understanding of the pathogenesis of complex diseases at the molecular level. Due to the large number of lncRNAs and the complex and time-consuming nature of biological experiments, applying computer techniques to predict potential lncRNA-disease associations is very effective. To explore information between complex network structures, existing methods rely mainly on lncRNA and disease information. Metapaths have been applied to network models as an effective method for exploring information in heterogeneous graphs. However, existing methods are dominated by lncRNAs or disease nodes and tend to ignore the paths provided by intermediate nodes.
    Methods: We propose a deep learning model based on hierarchical graphical attention networks to predict unknown lncRNA-disease associations using multiple types of metapaths to extract features. We have named this model the MMHGAN. First, the model constructs a lncRNA-disease-miRNA heterogeneous graph based on known associations and two homogeneous graphs of lncRNAs and diseases. Second, for homogeneous graphs, the features of neighboring nodes are aggregated using a multihead attention mechanism. Third, for the heterogeneous graph, metapaths of different intermediate nodes are selected to construct subgraphs, and the importance of different types of metapaths is calculated and aggregated to obtain the final embedded features. Finally, the features are reconstructed using a fully connected layer to obtain the prediction results.
    Results: We used a fivefold cross-validation method and obtained an average AUC value of 96.07% and an average AUPR value of 93.23%. Additionally, ablation experiments demonstrated the role of homogeneous graphs and different intermediate node path weights. In addition, we studied lung cancer, esophageal carcinoma, and breast cancer. Among the 15 lncRNAs associated with these diseases, 15, 12, and 14 lncRNAs were validated by the lncRNA Disease Database and the Lnc2Cancer Database, respectively.
    Conclusion: We compared the MMHGAN model with six existing models with better performance, and the case study demonstrated that the model was effective in predicting the correlation between potential lncRNAs and diseases.
    MeSH term(s) Humans ; Female ; RNA, Long Noncoding/genetics ; Computational Biology/methods ; MicroRNAs/genetics ; Breast Neoplasms ; Lung Neoplasms ; Algorithms
    Chemical Substances RNA, Long Noncoding ; MicroRNAs
    Language English
    Publishing date 2024-01-29
    Publishing country England
    Document type Journal Article
    ZDB-ID 2041484-5
    ISSN 1471-2105 ; 1471-2105
    ISSN (online) 1471-2105
    ISSN 1471-2105
    DOI 10.1186/s12859-024-05672-2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Sleep and psychological disorders seriously affect the quality of life of chronic rhinosinusitis patients.

    Wu, Chan / Zhan, Xiaojun

    European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery

    2024  

    Abstract: Purpose: Chronic rhinosinusitis (CRS) is a common disease that affects patients' quality of life (QoL). We aim to explore which symptoms bothered the patient most.: Methods: This is a cross-sectional study of CRS patients 2 years after endoscopic ... ...

    Abstract Purpose: Chronic rhinosinusitis (CRS) is a common disease that affects patients' quality of life (QoL). We aim to explore which symptoms bothered the patient most.
    Methods: This is a cross-sectional study of CRS patients 2 years after endoscopic sinus surgery (ESS). The main observation indicators were SNOT-22 and visual analog scale (VAS) scores. The patients were grouped according to clinical control standard of EPOS 2020. Patients' symptom scores and postoperative medication were used for analysis.
    Results: A total of 276 patients were included, among them, uncontrolled patients accounted for 23.9%, sense of taste/smell, fatigue, lacking of a good night's sleep, reduced concentration and reduced productivity were the most serious symptoms that troubled them. VAS and SNOT-22 scores were significantly different among all groups (P = 0.000), and had clinical significance for the diagnosis of clinical uncontrolled patients (both P < 0.0001). Furthermore, the duration of corticosteroids use and nasal saline irrigation in uncontrolled patients was significantly longer than that in other patients (P < 0.05).
    Conclusion: There are significant differences in the QoL of CRS patients with different clinical control, sleep and psychological disorders are main symptoms that affect the QoL of CRS patients, and more targeted management of sleep/psychological issues may be needed especially for uncontrolled patients.
    Language English
    Publishing date 2024-02-11
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 1017359-6
    ISSN 1434-4726 ; 0937-4477
    ISSN (online) 1434-4726
    ISSN 0937-4477
    DOI 10.1007/s00405-024-08505-3
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Ul II Zs.50: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  9. Article ; Online: GCNFORMER: graph convolutional network and transformer for predicting lncRNA-disease associations.

    Yao, Dengju / Li, Bailin / Zhan, Xiaojuan / Zhan, Xiaorong / Yu, Liyang

    BMC bioinformatics

    2024  Volume 25, Issue 1, Page(s) 5

    Abstract: Background: A growing body of researches indicate that the disrupted expression of long non-coding RNA (lncRNA) is linked to a range of human disorders. Therefore, the effective prediction of lncRNA-disease association (LDA) can not only suggest ... ...

    Abstract Background: A growing body of researches indicate that the disrupted expression of long non-coding RNA (lncRNA) is linked to a range of human disorders. Therefore, the effective prediction of lncRNA-disease association (LDA) can not only suggest solutions to diagnose a condition but also save significant time and labor costs.
    Method: In this work, we proposed a novel LDA predicting algorithm based on graph convolutional network and transformer, named GCNFORMER. Firstly, we integrated the intraclass similarity and interclass connections between miRNAs, lncRNAs and diseases, and built a graph adjacency matrix. Secondly, to completely obtain the features between various nodes, we employed a graph convolutional network for feature extraction. Finally, to obtain the global dependencies between inputs and outputs, we used a transformer encoder with a multiheaded attention mechanism to forecast lncRNA-disease associations.
    Results: The results of fivefold cross-validation experiment on the public dataset revealed that the AUC and AUPR of GCNFORMER achieved 0.9739 and 0.9812, respectively. We compared GCNFORMER with six advanced LDA prediction models, and the results indicated its superiority over the other six models. Furthermore, GCNFORMER's effectiveness in predicting potential LDAs is underscored by case studies on breast cancer, colon cancer and lung cancer.
    Conclusions: The combination of graph convolutional network and transformer can effectively improve the performance of LDA prediction model and promote the in-depth development of this research filed.
    MeSH term(s) Humans ; Female ; RNA, Long Noncoding/genetics ; RNA, Long Noncoding/metabolism ; MicroRNAs/genetics ; Algorithms ; Breast Neoplasms/genetics ; Colonic Neoplasms ; Computational Biology/methods
    Chemical Substances RNA, Long Noncoding ; MicroRNAs
    Language English
    Publishing date 2024-01-02
    Publishing country England
    Document type Journal Article
    ZDB-ID 2041484-5
    ISSN 1471-2105 ; 1471-2105
    ISSN (online) 1471-2105
    ISSN 1471-2105
    DOI 10.1186/s12859-023-05625-1
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article: C30 and C31 steranes in Permian fossil conifers Protophyllocladoxylon

    Zhan, Xin

    Applied geochemistry. 2022 Apr. 30,

    2022  

    Abstract: Steroids with alkyl groups at C-2 and C-3 are not known yet in any living organism, while 4-methylsteranes are previously thought specific to 4-methylsteroids bio-synthesized by limited types of organisms, such as dinoflagellates, microalgae, and certain ...

    Abstract Steroids with alkyl groups at C-2 and C-3 are not known yet in any living organism, while 4-methylsteranes are previously thought specific to 4-methylsteroids bio-synthesized by limited types of organisms, such as dinoflagellates, microalgae, and certain bacteria. Here we investigate the occurrence of C₃₀ 2α-, 3β-, 4α-methyl-24-ethylcholestanes, C₃₁ 3β,24-diethylcholestanes, and 3,4-dimethyl-24-ethylcholestane, which are tentatively identified in fourteen fossil conifer Protophyllocladoxylon trunk samples from the upper Permian Wutonggou and Guodikeng formations in northwest China. In contrast to the paucity of alkyl steranes in the background sediments, abundant C₃₀ 4α-methyl-24-ethylcholestanes in fossil conifers from diverse fluvial–lacustrine environments suggest that conifers were the most likely biological origin for the 4-methyl steranes in conifer trunk samples, although origins from associated fungi might also be possible. Alternatively, these C-4 methyl steranes might have possible algal or prokaryotic origins. It is noteworthy that diagenetic methylation or bacterial alkylation at early stages of diagenesis during burial may have caused a re-arrangement of classical 24-ethylsterols that were bio-synthesized by plants to generate C-2 and C-3 alkylated steranes. This study reports the presence of 4-methylsteranes in conifer fossils for the first time, pointing toward a likely origin from higher plants. This study also provides the first report of C-2 and C-3 alkylated steranes in plant fossils, a.llowing for the possibility that plants may produce functionalised sterol precursors of orphan alkylsteranes that are abundant in geological sediments but whose functionalised precursors are not known from any living organisms.
    Keywords Miozoa ; Permian period ; biosynthesis ; conifers ; diagenesis ; geochemistry ; methylation ; microalgae ; sterols
    Language English
    Dates of publication 2022-0430
    Publishing place Elsevier Ltd
    Document type Article
    Note Pre-press version
    ZDB-ID 1499242-5
    ISSN 0883-2927
    ISSN 0883-2927
    DOI 10.1016/j.apgeochem.2022.105328
    Database NAL-Catalogue (AGRICOLA)

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