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  • MDPI AG  (3)
  • 1
    Online Resource
    Online Resource
    Convolutional Neural Network-Based Artificial Intelligence for Classification of Protein Localization Patterns
    MDPI AG ; 2021
    In:  Biomolecules Vol. 11, No. 2 ( 2021-02-11), p. 264-
    Details
    In: Biomolecules, MDPI AG, Vol. 11, No. 2 ( 2021-02-11), p. 264-
    Abstract: Identifying localization of proteins and their specific subpopulations associated with certain cellular compartments is crucial for understanding protein function and interactions with other macromolecules. Fluorescence microscopy is a powerful method to assess protein localizations, with increasing demand of automated high throughput analysis methods to supplement the technical advancements in high throughput imaging. Here, we study the applicability of deep neural network-based artificial intelligence in classification of protein localization in 13 cellular subcompartments. We use deep learning-based on convolutional neural network and fully convolutional network with similar architectures for the classification task, aiming at achieving accurate classification, but importantly, also comparison of the networks. Our results show that both types of convolutional neural networks perform well in protein localization classification tasks for major cellular organelles. Yet, in this study, the fully convolutional network outperforms the convolutional neural network in classification of images with multiple simultaneous protein localizations. We find that the fully convolutional network, using output visualizing the identified localizations, is a very useful tool for systematic protein localization assessment.
    Type of Medium: Online Resource
    ISSN: 2218-273X
    URL: Article
    DOI: 10.3390/biom11020264
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2701262-1
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  • 2
    Online Resource
    Online Resource
    Biomimetic Inorganic Nanovectors as Tumor-Targeting Theranostic Platform against Triple-Negative Breast Cancer
    MDPI AG ; 2023
    In:  Pharmaceutics Vol. 15, No. 10 ( 2023-10-22), p. 2507-
    Details
    In: Pharmaceutics, MDPI AG, Vol. 15, No. 10 ( 2023-10-22), p. 2507-
    Abstract: Mesoporous silicon nanoparticles (PSi NPs) are promising platforms of nanomedicine because of their good compatibility, high payload capacities of anticancer drugs, and easy chemical modification. Here, PSi surfaces were functionalized with bisphosphonates (BP) for radiolabeling, loaded with doxorubicin (DOX) for chemotherapy, and the NPs were coated with cancer cell membrane (CCm) for homotypic cancer targeting. To enhance the CCm coating, the NP surfaces were covered with polyethylene glycol prior to the CCm coating. The effects of the BP amount and pH conditions on the radiolabeling efficacy were studied. The maximum BP was (2.27 wt%) on the PSi surfaces, and higher radiochemical yields were obtained for 99mTc (97% ± 2%) and 68Ga (94.6% ± 0.2%) under optimized pH conditions (pH = 5). The biomimetic NPs exhibited a good radiochemical and colloidal stability in phosphate-buffered saline and cell medium. In vitro studies demonstrated that the biomimetic NPs exhibited an enhanced cellular uptake and increased delivery of DOX to cancer cells, resulting in better chemotherapy than free DOX or pure NPs. Altogether, these findings indicate the potential of the developed platform for cancer treatment and diagnosis.
    Type of Medium: Online Resource
    ISSN: 1999-4923
    URL: Article
    DOI: 10.3390/pharmaceutics15102507
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2527217-2
    SSG: 15,3
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  • 3
    Online Resource
    Online Resource
    Proteomic Landscape of Prostate Cancer: The View Provided by Quantitative Proteomics, Integrative Analyses, and Protein Interactomes
    MDPI AG ; 2021
    In:  Cancers Vol. 13, No. 19 ( 2021-09-27), p. 4829-
    Details
    In: Cancers, MDPI AG, Vol. 13, No. 19 ( 2021-09-27), p. 4829-
    Abstract: Prostate cancer is the second most frequent cancer of men worldwide. While the genetic landscapes and heterogeneity of prostate cancer are relatively well-known already, methodological developments now allow for studying basic and dynamic proteomes on a large scale and in a quantitative fashion. This aids in revealing the functional output of cancer genomes. It has become evident that not all aberrations at the genetic and transcriptional level are translated to the proteome. In addition, the proteomic level contains heterogeneity, which increases as the cancer progresses from primary prostate cancer (PCa) to metastatic and castration-resistant prostate cancer (CRPC). While multiple aspects of prostate adenocarcinoma proteomes have been studied, less is known about proteomes of neuroendocrine prostate cancer (NEPC). In this review, we summarize recent developments in prostate cancer proteomics, concentrating on the proteomic landscapes of clinical prostate cancer, cell line and mouse model proteomes interrogating prostate cancer-relevant signaling and alterations, and key prostate cancer regulator interactomes, such as those of the androgen receptor (AR). Compared to genomic and transcriptomic analyses, the view provided by proteomics brings forward changes in prostate cancer metabolism, post-transcriptional RNA regulation, and post-translational protein regulatory pathways, requiring the full attention of studies in the future.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    URL: Article
    DOI: 10.3390/cancers13194829
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2527080-1
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