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  • 1
    In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 104, No. 29 ( 2007-07-17), p. 12140-12145
    Abstract: Establishment of functional and stable collaterals in the ischemic myocardium is crucial to restoring cardiac function after myocardial infarction. Here, we show that only dual delivery of a combination of angiogenic and arteriogenic factors to the ischemic myocardium could significantly reestablish stable collateral networks and improve myocardial perfusion and function. A combination of FGF-2 with PDGF-BB, two factors primarily targeting endothelial cells and vascular smooth muscle cells, remarkably promotes myocardial collateral growth and stabilizes the newly formed collateral networks, which significantly restore myocardial perfusion and function. Using various members of the PDGF family together with FGF-2 in an angiogenesis assay, we demonstrate that PDGFR-α is mainly involved in angiogenic synergism, whereas PDGFR-β mediates vessel stability signals. Our findings provide conceptual guidelines for the clinical development of proangiogenic/arteriogenic factors for the treatment of ischemic heart disease.
    Type of Medium: Online Resource
    ISSN: 0027-8424 , 1091-6490
    RVK:
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    Language: English
    Publisher: Proceedings of the National Academy of Sciences
    Publication Date: 2007
    detail.hit.zdb_id: 209104-5
    detail.hit.zdb_id: 1461794-8
    SSG: 11
    SSG: 12
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  • 2
    In: Bioinformatics, Oxford University Press (OUP), Vol. 38, No. 13 ( 2022-06-27), p. 3444-3453
    Abstract: Accurate ADMET (an abbreviation for ‘absorption, distribution, metabolism, excretion and toxicity’) predictions can efficiently screen out undesirable drug candidates in the early stage of drug discovery. In recent years, multiple comprehensive ADMET systems that adopt advanced machine learning models have been developed, providing services to estimate multiple endpoints. However, those ADMET systems usually suffer from weak extrapolation ability. First, due to the lack of labelled data for each endpoint, typical machine learning models perform frail for the molecules with unobserved scaffolds. Second, most systems only provide fixed built-in endpoints and cannot be customized to satisfy various research requirements. To this end, we develop a robust and endpoint extensible ADMET system, HelixADMET (H-ADMET). H-ADMET incorporates the concept of self-supervised learning to produce a robust pre-trained model. The model is then fine-tuned with a multi-task and multi-stage framework to transfer knowledge between ADMET endpoints, auxiliary tasks and self-supervised tasks. Results Our results demonstrate that H-ADMET achieves an overall improvement of 4%, compared with existing ADMET systems on comparable endpoints. Additionally, the pre-trained model provided by H-ADMET can be fine-tuned to generate new and customized ADMET endpoints, meeting various demands of drug research and development requirements. Availability and implementation H-ADMET is freely accessible at https://paddlehelix.baidu.com/app/drug/admet/train. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 3
    In: Brazilian Journal of Botany, Springer Science and Business Media LLC, Vol. 39, No. 2 ( 2016-6), p. 779-785
    Type of Medium: Online Resource
    ISSN: 0100-8404 , 1806-9959
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2016
    detail.hit.zdb_id: 2686406-X
    SSG: 12
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  • 4
    In: BMC Genomics, Springer Science and Business Media LLC, Vol. 20, No. 1 ( 2019-12)
    Abstract: Internode elongation is one of the most important traits in sugarcane because of its relation to crop productivity. Understanding the microRNA (miRNA) and mRNA expression profiles related to sugarcane internode elongation would help develop molecular improvement strategies but they are not yet well-investigated. To identify genes and miRNAs involved in internode elongation, the cDNA and small RNA libraries from the pre-elongation stage (EI), early elongation stage (EII) and rapid elongation stage (EIII) were sequenced and their expression were studied. Results Based on the sequencing results, 499,495,518 reads and 80,745 unigenes were identified from stem internodes of sugarcane. The comparisons of EI vs. EII, EI vs. EIII, and EII vs. EIII identified 493, 5035 and 3041 differentially expressed genes, respectively. Further analysis revealed that the differentially expressed genes were enriched in the GO terms oxidoreductase activity and tetrapyrrole binding. KEGG pathway annotation showed significant enrichment in “zeatin biosynthesis”, “nitrogen metabolism” and “plant hormone signal transduction”, which might be participating in internode elongation. miRNA identification showed 241 known miRNAs and 245 novel candidate miRNAs. By pairwise comparison, 11, 42 and 26 differentially expressed miRNAs were identified from EI and EII, EI and EIII, and EII and EIII comparisons, respectively. The target prediction revealed that the genes involved in “zeatin biosynthesis”, “nitrogen metabolism” and “plant hormone signal transduction” pathways are targets of the miRNAs. We found that the known miRNAs miR2592-y, miR1520-x, miR390-x, miR5658-x, miR6169-x and miR8154-x were likely regulators of genes with internode elongation in sugarcane. Conclusions The results of this study provided a global view of mRNA and miRNA regulation during sugarcane internode elongation. A genetic network of miRNA-mRNA was identified with miRNA-mediated gene expression as a mechanism in sugarcane internode elongation. Such evidence will be valuable for further investigations of the molecular regulatory mechanisms underpinning sugarcane growth and development.
    Type of Medium: Online Resource
    ISSN: 1471-2164
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2019
    detail.hit.zdb_id: 2041499-7
    SSG: 12
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