A5060407366- Computational Drug Discovery Methods
- Nanoparticles: synthesis and applications
- Machine Learning in Materials Science
- Metabolomics and Mass Spectrometry Studies
- Cell Image Analysis Techniques
- Advanced Chemical Sensor Technologies
- Graphene and Nanomaterials Applications
- Chemistry and Chemical Engineering
- Electrochemical Analysis and Applications
- Scientific Computing and Data Management
- Cholinesterase and Neurodegenerative Diseases
- Advanced Antenna and Metasurface Technologies
- Ecosystem dynamics and resilience
- Data Stream Mining Techniques
- Carbon and Quantum Dots Applications
- Software Reliability and Analysis Research
- 3D Printing in Biomedical Research
- Recycling and Waste Management Techniques
- Mineral Processing and Grinding
- Advanced materials and composites
- Healthcare Technology and Patient Monitoring
- Data Analysis with R
- Aluminum Alloys Composites Properties
- Data Mining Algorithms and Applications
- Metamaterials and Metasurfaces Applications
NovaMechanics (Cyprus)
2017-2024
National Technical University of Athens
2017-2023
The rapid advance of nanotechnology has led to the development and widespread application nanomaterials, raising concerns regarding their potential adverse effects on human health environment. Traditional (experimental) methods for assessing nanoparticles (NPs) safety are time-consuming, expensive, resource-intensive, raise ethical due reliance animals. To address these challenges, we propose an in silico workflow that serves as alternative or complementary approach conventional hazard risk...
Abstract Zeta potential is one of the most critical properties nanomaterials (NMs) which provides an estimation surface charge, and therefore electrostatic stability in medium and, practical terms, influences NM's tendency to form agglomerates interact with cellular membranes. This paper describes a robust accurate read‐across model predict NM zeta utilizing as input data set image descriptors derived from transmission electron microscopy (TEM) images NMs. The are calculated using NanoXtract...
Abstract This study presents the results of applying deep learning methodologies within ecotoxicology field, with objective training predictive models that can support hazard assessment and eventually design safer engineered nanomaterials (ENMs). A workflow two different architectures on microscopic images Daphnia magna is proposed automatically detect possible malformations, such as effects length tail, overall size, uncommon lipid concentrations deposit shapes, which are due to direct or...
Multi-walled carbon nanotubes are currently used in numerous industrial applications and products, therefore fast accurate evaluation of their biological toxicological effects is utmost importance. Computational methods techniques, previously applied the area cheminformatics for prediction adverse chemicals, can also be case nanomaterials (NMs), an effort to reduce expensive time consuming experimental procedures. In this context, a validated predictive nanoinformatics model has been...
Nanoinformatics models to predict the toxicity/ecotoxicity of nanomaterials (NMs) are urgently needed support commercialization nanotechnologies and allow grouping NMs based on their physico-chemical and/or (eco)toxicological properties, facilitate read-across knowledge from data-rich data-poor ones. Here we present first ecotoxicological for predicting ecotoxicity, which were developed in accordance with ECHA's recommended strategy as a means explore silico effects panel freshly dispersed...
We present toxFlow, a web application developed for enrichment analysis of omics data coupled with read-across toxicity prediction. A sequential workflow is suggested where users can filter using scores and incorporate their findings into correlation-based technique predicting the substance based on its analogs. Either embedded or in-house gene signature libraries be used analysis. The approach prediction diverse chemical entities; however, this article focuses multiperspective...
A key step in building regulatory acceptance of alternative or non-animal test methods has long been the use interlaboratory comparisons Round Robins (RR), which a common material and standard operating procedure is provided to all participants, who measure specific endpoint return their data for statistical comparison demonstrate reproducibility method. While there currently no approach modelling approaches, consensus emerging as “modelling equivalent” RR. We here novel evaluate performance...
Abstract In this study we present deimos, a computational methodology for optimal grouping, applied on the read‐across prediction of engineered nanomaterials’ (ENMs) toxicity‐related properties. The method is based formulation and solution mixed‐integer optimization program (MILP) problem that automatically simultaneously performs feature selection, defines grouping boundaries according to response variable develops linear regression models in each group. For group/region, characteristic...
This paper investigates the toxicological concerns associated with nickel–silicon carbide (Ni–SiC) electroplated nanocomposite coatings as an alternative to conventional chromium electrodeposition.
A key step in building regulatory acceptance of alternative or non-animal test methods has long been the use interlaboratory comparisons round-robins (RRs), which a common material and standard operating procedure is provided to all participants, who measure specific endpoint return their data for statistical comparison demonstrate reproducibility method. While there currently no approach modelling approaches, consensus emerging as “modelling equivalent” RR. We here novel evaluate...
In the present study, a novel read-across methodology for prediction of toxicity related end-points engineered nanomaterials (ENMs) is developed. The proposed method lies in interface between two main approaches, namely analogue and grouping methods, can employ single criterion or multiple criteria defining similarities among ENMs. advantage that there no need prior hypothesis. Based on formulation solution mathematical optimization problem, searches over space alternative hypotheses,...
In this study, a computational workflow is presented for grouping engineered nanomaterials (ENMs) and predicting their toxicity-related end points. A mixed integer–linear optimization program (MILP) problem formulated, which automatically filters out the noisy variables, defines boundaries, develops specific to each group predictive models. The method extended multidimensional space, by considering ENM characterization categories (e.g., biological, physicochemical, biokinetics, image etc.)...
Graphene Based Materials In article 2100637, Guo and co-workers review the surface functionalization of graphene based materials (GBMs) through lenses nanotoxicity safe-by-design, discuss computational tools that can predict interaction GBMs behavior with their toxicity, provide a concise framing current knowledge key features to be controlled for safe sustainable applications.