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Marti-Sarrias, A.

Publications and source records attributed to Marti-Sarrias, A..

2 recordsLinked to original sources

CBM KG: A Comorbidity-Centric Knowledge Graph Uncovering Causal Pathomechanisms Between COVID-19 and Neurodegenerative Diseases

SummaryCOVID-19 is increasingly recognized as a potential trigger or accelerator of neurodegenerative diseases such as Alzheimers and Parkinsons. To systematically explore the putative molecular and clinical associations between them, we present CBM KG (Causal Biological Mechanisms Knowledge Graph)--a manually curated, comorbidity-centric resource developed within the EU-funded COMMUTE project. CBM KG integrates over 2,800 cause-and-effect or correlative relationships from 63 peer-reviewed publications, highlighting key mechanisms such as viral entry routes, blood-brain barrier alteration, microglial activation, neuroinflammation, and APOE {varepsilon}4-associated susceptibility. Each relationship in the graph is fully traceable to its source evidence, ensuring transparency and reproducibility. Unlike general-purpose or single disease-focused knowledge graphs, CBM KG is specifically designed to represent causal biological mechanisms spanning both infectious and neurodegenerative processes. By encoding directional, cause-and-effect relationships, it supports the interpretation of clinical co-occurrences through plausible mechanistic links between overlapping disease pathways, offering high-resolution insights at both molecular and clinical levels. Availability and implementationThe BEL files, Neo4j database, and Cytoscape visualization files are publicly available at: https://github.com/SCAI-BIO/CBM-Comorbidity-KG.

bioinformatics↗

Variability vs Phenotype: multimodal analysis of Dravet Syndrome Brain Organoids powered by Deep Learning

Brain organoids (BO) have risen as a reliable model for neurodelopmental disorders (ND), reproducing human brain development milestones. However, their significant intra- and inter-organoid variability compromises their use in advanced tasks such as drug testing. Overcoming experimental variability is crucial for models prone to variation, like unguided BO. BO modelling in Dravet Syndrome, a late-onset epileptic ND, represents a great challenge since BO variability accumulates with time, when phenotype shows in vitro. Leveraging deep learning, we developed ImPheNet, a predictive tool grounded in BO live imaging datasets. ImPheNet accurately classified phenotypes and assessed drug toxicity in BO derived from DS, revealing differences between genotypes and upon antiseizure drug exposure. These results are supported by transcriptomic and functional data, revealing an excitatory-inhibitory imbalance during the maturation of DS organoids. Altogether, our DL-predictive live imaging strategy, ImPheNet, emerges as a powerful tool enhancing BO research and advancing ND treatments.

developmental biology↗