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Fani Karagianni Antreas Pavlidis Lina S. Malakou Christina Piperi Evangelia Papadavid 《International journal of molecular sciences》2022,23(3)
The serine/threonine kinase mechanistic target of rapamycin (mTOR) plays a pivotal role in the regulation of cell proliferation, survival, and motility in response to availability of energy and nutrients as well as mitogens. The mTOR signaling axis regulates important biological processes, including cellular growth, metabolism, and survival in many tissues. In the skin, dysregulation of PI3K/AKT/mTOR pathway may lead to severe pathological conditions characterized by uncontrolled proliferation and inflammation, including skin hyperproliferative as well as malignant diseases. Herein, we provide an update on the current knowledge regarding the pathogenic implication of the mTOR pathway in skin diseases with inflammatory features (such as psoriasis, atopic dermatitis, pemphigus, and acne) and malignant characteristics (such as cutaneous T cell lymphoma and melanoma) while we critically discuss current and future perspectives for therapeutic targeting of mTOR axis in clinical practice. 相似文献
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Pantelis Karatzas Georgia Melagraki Laura‐Jayne A. Ellis Iseult Lynch Dimitra‐Danai Varsou Antreas Afantitis Andreas Tsoumanis Philip Doganis Haralambos Sarimveis 《Small (Weinheim an der Bergstrasse, Germany)》2020,16(36)
This study presents the results of applying deep learning methodologies within the ecotoxicology field, with the objective of training predictive models that can support hazard assessment and eventually the design of safer engineered nanomaterials (ENMs). A workflow applying two different deep learning architectures on microscopic images of Daphnia magna is proposed that can automatically detect possible malformations, such as effects on the length of the tail, and the overall size, and uncommon lipid concentrations and lipid deposit shapes, which are due to direct or parental exposure to ENMs. Next, classification models assign specific objects (heart, abdomen/claw) to classes that depend on lipid densities and compare the results with controls. The models are statistically validated in terms of their prediction accuracy on external D. magna images and illustrate that deep learning technologies can be useful in the nanoinformatics field, because they can automate time‐consuming manual procedures, accelerate the investigation of adverse effects of ENMs, and facilitate the process of designing safer nanostructures. It may even be possible in the future to predict impacts on subsequent generations from images of parental exposure, reducing the time and cost involved in long‐term reproductive toxicity assays over multiple generations. 相似文献
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Dimitra Papadopoulou Antonios Drakopoulos Panagiotis Lagarias Georgia Melagraki George Kollias Antreas Afantitis 《International journal of molecular sciences》2021,22(19)
Tumor necrosis factor (TNF) is a regulator of several chronic inflammatory diseases, such as rheumatoid arthritis. Although anti-TNF biologics have been used in clinic, they render several drawbacks, such as patients’ progressive immunodeficiency and loss of response, high cost, and intravenous administration. In order to find new potential anti-TNF small molecule inhibitors, we employed an in silico approach, aiming to find natural products, analogs of Ampelopsin H, a compound that blocks the formation of TNF active trimer. Two out of nine commercially available compounds tested, Nepalensinol B and Miyabenol A, efficiently reduced TNF-induced cytotoxicity in L929 cells and production of chemokines in mice joints’ synovial fibroblasts, while Nepalensinol B also abolished TNF-TNFR1 binding in non-toxic concentrations. The binding mode of the compounds was further investigated by molecular dynamics and free energy calculation studies, using and advancing the Enalos Asclepios pipeline. Conclusively, we propose that Nepalensinol B, characterized by the lowest free energy of binding and by a higher number of hydrogen bonds with TNF, qualifies as a potential lead compound for TNF inhibitors’ drug development. Finally, the upgraded Enalos Asclepios pipeline can be used for improved identification of new therapeutics against TNF-mediated chronic inflammatory diseases, providing state-of-the-art insight on their binding mode. 相似文献
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Varnavas D. Mouchlis Antreas Afantitis Angela Serra Michele Fratello Anastasios G. Papadiamantis Vassilis Aidinis Iseult Lynch Dario Greco Georgia Melagraki 《International journal of molecular sciences》2021,22(4)
De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including ma-chine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures. This method has successfully been em-ployed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencoders. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine-learning methodologies and high-lights hot topics for further development. 相似文献
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Risk Governance of Emerging Technologies Demonstrated in Terms of its Applicability to Nanomaterials
Panagiotis Isigonis Antreas Afantitis Dalila Antunes Alena Bartonova Ali Beitollahi Nils Bohmer Evert Bouman Qasim Chaudhry Mihaela Roxana Cimpan Emil Cimpan Shareen Doak Damien Dupin Doreen Fedrigo Valrie Fessard Maciej Gromelski Arno C. Gutleb Sabina Halappanavar Peter Hoet Nina Jeliazkova Stphane Jomini Sabine Lindner Igor Linkov Eleonora Marta Longhin Iseult Lynch Ineke Malsch Antonio Marcomini Espen Mariussen Jesus M. de la Fuente Georgia Melagraki Finbarr Murphy Michael Neaves Rolf Packroff Stefan Pfuhler Tomasz Puzyn Qamar Rahman Elise Rundn Pran Elena Semenzin Tommaso Serchi Christoph Steinbach Benjamin Trump Ivana Vinkovi Vr
ek David Warheit Mark R. Wiesner Egon Willighagen Maria Dusinska 《Small (Weinheim an der Bergstrasse, Germany)》2020,16(36)
Nanotechnologies have reached maturity and market penetration that require nano‐specific changes in legislation and harmonization among legislation domains, such as the amendments to REACH for nanomaterials (NMs) which came into force in 2020. Thus, an assessment of the components and regulatory boundaries of NMs risk governance is timely, alongside related methods and tools, as part of the global efforts to optimise nanosafety and integrate it into product design processes, via Safe(r)‐by‐Design (SbD) concepts. This paper provides an overview of the state‐of‐the‐art regarding risk governance of NMs and lays out the theoretical basis for the development and implementation of an effective, trustworthy and transparent risk governance framework for NMs. The proposed framework enables continuous integration of the evolving state of the science, leverages best practice from contiguous disciplines and facilitates responsive re‐thinking of nanosafety governance to meet future needs. To achieve and operationalise such framework, a science‐based Risk Governance Council (RGC) for NMs is being developed. The framework will provide a toolkit for independent NMs' risk governance and integrates needs and views of stakeholders. An extension of this framework to relevant advanced materials and emerging technologies is also envisaged, in view of future foundations of risk research in Europe and globally. 相似文献
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