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Abstract: Glioblastoma GBM: Glioblastoma multiforme (GBM) remains one of the most lethal and treatment-resistant brain cancers, driven in part by the complexity of its tumour microenvironment (TME). While organ-on-chip (OoC) platforms offer more physiologically relevant models than traditional 2D or static 3D systems, their design remains largely empirical, lacking predictive control over flow conditions, biochemical gradients, and mechanical cues. Computational Fluid Dynamics (CFD) has emerged as a powerful tool to enhance the design, precision, and biological fidelity of OoC platforms. This comprehensive review highlights current limitations in replicating GBM’s biological complexity and technical constraints in device fabrication and maintenance, mapping them to specific CFD strategies. It synthesises current strategies into a structured workflow for integrating CFD into the design, optimisation, and validation of microfluidic tumour models—bridging engineering precision with biological complexity. In addition, validation frameworks reported in the literature are highlighted and mapped onto GBM-on-chip applications have been recommended, drawing on widely recognised international standards for engineering validation and regulatory modelling practices. Finally, this review positions CFD as a core component of GBM-on-chip development and explores how its integration with AI-based optimisation can advance the creation of more predictive, scalable, and biologically relevant in vitro tumour models.
Keywords: AI, computational fluid dynamics, glioblastoma, In silicosimulation, in vitro modelling, microfluidic perfusion, organ-on-chip, tumour microenvironment
Published in DiRROS: 13.02.2026; Views: 382; Downloads: 153
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Abstract: Brown algal forests (Cystoseira sensu lato) are key habitat-forming components of temperate rocky coasts but have experienced widespread decline across the Mediterranean Sea. This study investigates the current distribution and potential future shifts in brown algal forests across the Adriatic Sea under ongoing climate change. We combined non-destructive field-based mapping along the Slovenian coastline with remote-sensing products and spatial environmental predictors to model basin-wide habitat suitability. A multiscale geographically weighted regression (MGWR) framework was applied to account for spatial non-stationarity and to explicitly capture the fact that environmental drivers of habitat suitability operate at different spatial scales—an assumption that global models such as GAM or standard GWR cannot adequately address. Habitat suitability maps were generated for present-day conditions and projected under mid- and late-century climate scenarios. The results reveal pronounced latitudinal gradients, identify areas of ongoing canopy decline in the northern Adriatic, and highlight parts of the southern Adriatic as potential climate refugia. Overall, the study demonstrates a likely north–south contraction of suitable habitat for brown algal forests and underscores the value of multiscale spatial modelling for informing marine spatial planning, conservation prioritization, and climate- adaptive restoration under European policy frameworks.
Keywords: brown algal forests, Cystoseira s.l., habitat suitability, spatial modelling, Adriatic Sea, climate change, shifts
Published in DiRROS: 21.01.2026; Views: 315; Downloads: 247
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Abstract: Karst areas are highly susceptible to contamination due to rapid recharge and throughflow caused by their heterogeneous structure with unknown networks of conduits embedded in a matrix of low conductivity. Vulnerability methods have been used to ensure adequate protection of drinking water resources. However, most of the studies assessing the vulnerability of karst aquifers consider it as a constant value in time and, therefore, under special hydrological conditions in space, which is an oversimplification of reality. In this work, the behaviour of an Alpine karst system characterised by rapid flow and transfer through vertical shafts has been studied by discrete numerical modelling using MODFLOW 6. Six numerical models have been designed with the aim of representing simple common geometrical configurations found in Alpine karst systems. These models simulate how the flow and transport response at the system outlet is influenced by the aquifer geometry and recharge conditions. The results confirm that the arrival of the tracer at the spring strongly depends on the conduit geometry and the recharge conditions. This demonstrates that karst aquifer vulnerability cannot be defined as a constant value but should be specifically assessed depending on the spatio-temporal conditions.
Keywords: Alpine karst system, numerical flow and transport modelling, vulnerability, overflow, MODFLOW 6
Published in DiRROS: 10.01.2026; Views: 438; Downloads: 208
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Abstract: Biological ageing is a systemic, multifactorial process driven by progressive molecular and cellular alterations whose complexity necessitates systems-level approaches. Advances in high-throughput omics technologies now allow simultaneous quantification of millions of biomolecules from a single specimen, enabling longitudinal, integrative profiling across multiple molecular layers. This review synthesizes recent progress in applying genomics, epigenomics, metabolomics and microbiomics to ageing research, highlighting their contributions to biomarker discovery, mechanistic insight, and translational opportunities. Genomic studies reveal genetic variants that promote extreme longevity, while epigenetic clocks provide robust predictors of biological age. The blood proteome can be used to calculate proteome-based scores and evaluate temporal changes in ageing trajectories in an organ- and sex-specific manner. Metabolomic signatures identify key metabolites reflecting ageing trajectories, and microbiome research demonstrates that gut microbial composition mirrors and modulates biological ageing, with microbiome clocks emerging. The omics approaches have further elucidated the impact of exercise and diet providing evidence that interventions can reduce biological age. The integration of multi-omics with clinical and lifestyle data, powered by machine learning and artificial intelligence, is paving the way for a holistic definition of biological age and the development of personalized healthy ageing strategies. This review highlights how the omics technologies and computational modelling are transforming ageing biology into strategies for personalized healthy ageing.
Keywords: ageing, biological ageing, omics, physical fitness, nutrition, computational modelling
Published in DiRROS: 08.01.2026; Views: 570; Downloads: 185
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Abstract: The characterization and assessment of soil functions is a prerequisite for agricultural and environmental policies aimed at soil health. However, there is a lack of satisfactory models for the assessment of soil functions supply to support national and intergovernmental initiatives. In this study we fill this gap by restructuring models developed to assess the multifunctionality of agricultural soils at the field scale. The multi-criteria decision models rely on soil properties, site characteristics and management information to assess the following five soil functions: (1) water regulation, (2) climate regulation, (3) nutrient cycling, (4) primary productivity and (5) provision of habitat for biodiversity. We develop models to assess soil functions supply at regional and national scales by adapting their structure to cope with the general lack of information on soil management at larger geographical scales. The restructured models are verified and a sensitivity analysis of the new model structure is performed. We further applied a comparison of the upscaled models with results from validated field-scale models using real data from 94 sites spanning across 13 European countries. We found that the upscaled models showed a similar sensitivity to the variability of the input data from the 94 sampling sites as the base models from which they were developed and that their overall supply is expected to be comparable. We describe the model structure of the upscaled models as well as their qualitative scales and integration rules. We propose the application of the models can serve for large-scale assessment of soil functions supply as part of soil health assessment for regional and national environmental and agricultural policies.
Keywords: multifunctionality, soil health, soil quality, ecosystem services, multi-attribute modelling
Published in DiRROS: 07.01.2026; Views: 373; Downloads: 199
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