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  1. Article ; Online: Corrigendum to "Combination of low-energy shock-wave therapy and bone marrow mesenchymal stem cell transplantation to improve the erectile function of diabetic rats" by Hai-Tao Shan

    Shan, Hai-Tao / Zhang, Hai-Bo / Chen, Wen-Tao / Chen, Feng-Zhi / Wang, Tao / Luo, Jin-Tai / Yue, Min / Lin, Ji-Hong / Wei, An-Yang

    Asian journal of andrology

    2016  Volume 19, Issue 1, Page(s) 140

    Abstract: This corrects the article DOI: 10.4103/1008-682X.184271.]. ...

    Abstract [This corrects the article DOI: 10.4103/1008-682X.184271.].
    Language English
    Publishing date 2016-10-04
    Publishing country China
    Document type Journal Article ; Published Erratum
    ZDB-ID 2075824-8
    ISSN 1745-7262 ; 1008-682X
    ISSN (online) 1745-7262
    ISSN 1008-682X
    DOI 10.4103/1008-682X.189623
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 5739: Show issues Location:
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  2. Article ; Online: The need for innovation in burn pain management.

    Song, Hai-Tao / Zhang, Xiu-Hang

    Burns : journal of the International Society for Burn Injuries

    2024  

    Language English
    Publishing date 2024-03-08
    Publishing country Netherlands
    Document type Letter
    ZDB-ID 197308-3
    ISSN 1879-1409 ; 0305-4179
    ISSN (online) 1879-1409
    ISSN 0305-4179
    DOI 10.1016/j.burns.2024.03.010
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  3. Article ; Online: Fabrication and characterization of salidroside W/O/W emulsion with sodium alginate.

    Zhang, Qian / Wang, Yu-Qiao / Li, Lin / Song, Hao-Lin / Wu, Hai-Tao / Zhu, Bei-Wei

    Food chemistry: X

    2024  Volume 22, Page(s) 101260

    Abstract: Salidroside (Sal), the main bioactive substance ... ...

    Abstract Salidroside (Sal), the main bioactive substance in
    Language English
    Publishing date 2024-02-29
    Publishing country Netherlands
    Document type Journal Article
    ISSN 2590-1575
    ISSN (online) 2590-1575
    DOI 10.1016/j.fochx.2024.101260
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  4. Article ; Online: Function and structure of bradykinin receptor 2 for drug discovery.

    Shen, Jin-Kang / Zhang, Hai-Tao

    Acta pharmacologica Sinica

    2022  Volume 44, Issue 3, Page(s) 489–498

    Abstract: Type 2 bradykinin receptor (B2R) is an essential G protein-coupled receptor (GPCR) that regulates the cardiovascular system as a vasodepressor. Dysfunction of B2R is also closely related to cancers and hereditary angioedema (HAE). Although several B2R ... ...

    Abstract Type 2 bradykinin receptor (B2R) is an essential G protein-coupled receptor (GPCR) that regulates the cardiovascular system as a vasodepressor. Dysfunction of B2R is also closely related to cancers and hereditary angioedema (HAE). Although several B2R agonists and antagonists have been developed, icatibant is the only B2R antagonist clinically used for treating HAE. The recently determined structures of B2R have provided molecular insights into the functions and regulation of B2R, which shed light on structure-based drug design for the treatment of B2R-related diseases. In this review, we summarize the structure and function of B2R in relation to drug discovery and discuss future research directions to elucidate the remaining unknown functions of B2R dimerization.
    MeSH term(s) Bradykinin B2 Receptor Antagonists ; Drug Discovery ; Receptor, Bradykinin B2/agonists ; Receptors, Bradykinin ; Humans
    Chemical Substances Bradykinin B2 Receptor Antagonists ; Receptor, Bradykinin B2 ; Receptors, Bradykinin
    Language English
    Publishing date 2022-09-08
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 1360774-1
    ISSN 1745-7254 ; 0253-9756 ; 1671-4083
    ISSN (online) 1745-7254
    ISSN 0253-9756 ; 1671-4083
    DOI 10.1038/s41401-022-00982-8
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    Zs.A 1904: Show issues Location:
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  5. Article ; Online: Resilience of hybrid herbivore-plant-pollinator networks.

    Wang, Guangwei / Chen, Guanrong / Zhang, Hai-Tao

    Chaos (Woodbury, N.Y.)

    2023  Volume 33, Issue 9

    Abstract: The concept of network resilience has gained increasing attention in the last few decades owing to its great potential in strengthening and maintaining complex systems. From network-based approaches, researchers have explored resilience of real ... ...

    Abstract The concept of network resilience has gained increasing attention in the last few decades owing to its great potential in strengthening and maintaining complex systems. From network-based approaches, researchers have explored resilience of real ecological systems comprising diverse types of interactions, such as mutualism, antagonist, and predation, or mixtures of them. In this paper, we propose a dimension-reduction method for analyzing the resilience of hybrid herbivore-plant-pollinator networks. We qualitatively evaluate the contribution of species toward maintaining resilience of networked systems, as well as the distinct roles played by different categories of species. Our findings demonstrate that the strong contributors to network resilience within each category are more vulnerable to extinction. Notably, among the three types of species in consideration, plants exhibit a higher likelihood of extinction, compared to pollinators and herbivores.
    Language English
    Publishing date 2023-09-20
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1472677-4
    ISSN 1089-7682 ; 1054-1500
    ISSN (online) 1089-7682
    ISSN 1054-1500
    DOI 10.1063/5.0169946
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  6. Article ; Online: A Data-Driven Bayesian Koopman Learning Method for Modeling Hysteresis Dynamics.

    Huang, Xiang / Zhang, Hai-Tao / Wang, Jun

    IEEE transactions on neural networks and learning systems

    2023  Volume PP

    Abstract: Exploring the mechanism of hysteresis dynamics may facilitate the analysis and controller design to alleviate detrimental effects. Conventional models, such as the Bouc-Wen and Preisach models consist of complicated nonlinear structures, limiting the ... ...

    Abstract Exploring the mechanism of hysteresis dynamics may facilitate the analysis and controller design to alleviate detrimental effects. Conventional models, such as the Bouc-Wen and Preisach models consist of complicated nonlinear structures, limiting the applications of hysteresis systems for high-speed and high-precision positioning, detection, execution, and other operations. In this article, a Bayesian Koopman (B-Koopman) learning algorithm is therefore developed to characterize hysteresis dynamics. Essentially, the proposed scheme establishes a simplified linear representation with time delay for hysteresis dynamics, where the properties of the original nonlinear system are preserved. Furthermore, model parameters are optimized via sparse Bayesian learning together with an iterative strategy, which simplifies the identification procedure and reduces modeling errors. Extensive experimental results on piezoelectric positioning are elaborated to substantiate the effectiveness and superiority of the proposed B-Koopman algorithm for learning hysteresis dynamics.
    Language English
    Publishing date 2023-07-04
    Publishing country United States
    Document type Journal Article
    ISSN 2162-2388
    ISSN (online) 2162-2388
    DOI 10.1109/TNNLS.2023.3288752
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  7. Article ; Online: Electrocatalytic ring-opening dihydroalkoxylation of

    Zhang, Zhang / Wang, Ying-Chun / Tang, Hai-Tao / Pan, Ying-Ming / Meng, Xiu-Jin

    Organic & biomolecular chemistry

    2023  Volume 21, Issue 15, Page(s) 3177–3182

    Abstract: The electrocatalytic ring-opening dihydroalkoxylation ... ...

    Abstract The electrocatalytic ring-opening dihydroalkoxylation of
    Language English
    Publishing date 2023-04-12
    Publishing country England
    Document type Journal Article
    ZDB-ID 2097583-1
    ISSN 1477-0539 ; 1477-0520
    ISSN (online) 1477-0539
    ISSN 1477-0520
    DOI 10.1039/d3ob00178d
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  8. Article ; Online: E2EDA: Protein Domain Assembly Based on End-to-End Deep Learning.

    Zhu, Hai-Tao / Xia, Yu-Hao / Zhang, Gui-Jun

    Journal of chemical information and modeling

    2023  Volume 63, Issue 20, Page(s) 6451–6461

    Abstract: With the development of deep learning, almost all single-domain proteins can be predicted at experimental resolution. However, the structure prediction of multi-domain proteins remains a challenge. Achieving end-to-end protein domain assembly and further ...

    Abstract With the development of deep learning, almost all single-domain proteins can be predicted at experimental resolution. However, the structure prediction of multi-domain proteins remains a challenge. Achieving end-to-end protein domain assembly and further improving the accuracy of the full-chain modeling by accurately predicting inter-domain orientation while improving the assembly efficiency will provide significant insights into structure-based drug discovery. In this work, we propose an End-to-End Domain Assembly method based on deep learning, named E2EDA. We first develop RMNet, an EfficientNetV2-based deep learning model that fuses multiple features using an attention mechanism to predict inter-domain rigid motion. Then, the predicted rigid motions are transformed into inter-domain spatial transformations to directly assemble the full-chain model. Finally, the scoring strategy RMscore is designed to select the best model from multiple assembled models. The experimental results show that the average TM-score of the model assembled by E2EDA on the benchmark set (282) is 0.827, which is better than those of other domain assembly methods SADA (0.792) and DEMO (0.730). Meanwhile, on our constructed multi-domain data set from AlphaFold DB, the model reassembled by E2EDA is 7.0% higher in TM-score compared to the full-chain model predicted by AlphaFold2, indicating that E2EDA can capture more accurate inter-domain orientations to improve the quality of the model predicted by AlphaFold2. Furthermore, compared to SADA and AlphaFold2, E2EDA reduced the average runtime on the benchmark by 64.7% and 19.2%, respectively, indicating that E2EDA can significantly improve assembly efficiency through an end-to-end approach. The online server is available at http://zhanglab-bioinf.com/E2EDA.
    MeSH term(s) Protein Domains ; Deep Learning ; Proteins/chemistry
    Chemical Substances Proteins
    Language English
    Publishing date 2023-10-03
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 190019-5
    ISSN 1549-960X ; 0095-2338
    ISSN (online) 1549-960X
    ISSN 0095-2338
    DOI 10.1021/acs.jcim.3c01387
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  9. Article: DHEA down-regulates mitochondrial dynamics and promotes apoptosis of lung adenocarcinoma cells through FASTKD2.

    Zhang, Yan-Fei / Yuan, Liu-Liu / Wang, Zong-Can / Zhuang, Wen-Bin / Zhang, Wen-Jia / Liu, Hai-Tao / Li, Ming / Fan, Li-Hong

    Journal of Cancer

    2024  Volume 15, Issue 8, Page(s) 2110–2122

    Abstract: Background: ...

    Abstract Background:
    Language English
    Publishing date 2024-02-24
    Publishing country Australia
    Document type Journal Article
    ZDB-ID 2573318-7
    ISSN 1837-9664
    ISSN 1837-9664
    DOI 10.7150/jca.93373
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  10. Article ; Online: Letter to the editor: Cautious interpretation of the compensation of insulin-mTORC1 activation to promote liver regeneration in the absence of hepatocyte β-catenin.

    Ning, Cong / Zhang, Xin-Mu / Sang, Xin-Ting / Zhao, Hai-Tao

    Hepatology (Baltimore, Md.)

    2023  Volume 77, Issue 5, Page(s) E84–E85

    MeSH term(s) Humans ; beta Catenin/physiology ; Cell Proliferation ; Hepatocytes/pathology ; Hyperplasia ; Insulin ; Liver/pathology ; Liver Regeneration/physiology ; Mechanistic Target of Rapamycin Complex 1/metabolism
    Chemical Substances beta Catenin ; Insulin ; Mechanistic Target of Rapamycin Complex 1 (EC 2.7.11.1)
    Language English
    Publishing date 2023-04-17
    Publishing country United States
    Document type Research Support, Non-U.S. Gov't ; Letter ; Comment
    ZDB-ID 604603-4
    ISSN 1527-3350 ; 0270-9139
    ISSN (online) 1527-3350
    ISSN 0270-9139
    DOI 10.1002/hep.32695
    Database MEDical Literature Analysis and Retrieval System OnLINE

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