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Casas, B.

Publications and source records attributed to Casas, B..

3 recordsLinked to original sources

Parent-Child Cortical Similarity Indexes Multiple Dimensions of Sensitivity in the Association Between Family Environment and Adolescent Internalizing Symptoms

Family environment is a robust predictor of adolescent internalizing symptoms, yet its strength varies across adolescents. We tested whether this variation is partly captured by how similar an adolescent's cortical structure is to their parent's and whether different forms of this similarity matter in different cortical systems. In 115 parent-child dyads from two cohorts, structural MRI quantified similarity across 400 cortical parcels in two forms: phenotypic similarity in regional morphometry and architectural similarity in interregional morphometric organization. Parent-child similarity was not directly associated with symptoms, whereas its interactions with family environment were jointly significant (chi-square(4) = 14.92, P = 0.005). Phenotypic similarity in the sensory-limbic system and architectural similarity in the association system each moderated the association between family environment and symptoms (both beta = 0.22). In both cases, the association was strongest among less similar dyads. The phenotypic interaction was distributed across sensory-limbic networks and was not reproduced by similarity to unrelated adults or by the child's cortical atypicality. The architectural interaction was concentrated in the dorsal attention and default mode networks and was distinguishable from unrelated-adult similarity but not from child atypicality. Together, these findings raise the possibility that markers of environmental sensitivity need not reside in the child alone. Parent-child cortical similarity may index a dyadic component of this sensitivity, one not adequately represented by a single similarity score but distributed across forms of similarity and cortical systems.

neuroscience↗

Diseased human pancreas and liver microphysiological system for preclinical diabetes research

Current research on metabolic disorders such as type 2 diabetes relies on animal models because multi-organ diseases cannot be well studied with the standard in vitro assays. Here, we connect models of key metabolism organs, pancreas and liver, on a microfluidic chip to enable diabetes research in a human-based preclinical system. Aided by mechanistic mathematical modelling, we developed a two-organ microphysiological system (MPS) that replicates clinically-relevant phenotypes of diabetic dysregulation both in the liver and pancreas compartments. Exposure to hyperglycemia and high cortisone created a diseased pancreas-liver MPS which displayed beta-cell dysfunction, steatosis, elevated ketone-body secretion, increased glycogen storage, and upregulated gluconeogenic machinery. In turn, normoglycemia and physiological cortisone concentration maintained glucose tolerance and stable liver and beta-cell functions. This method was evaluated for repeatability in two laboratories and was effective in multiple pancreatic islet donors. The model also provides a platform to identify new therapeutic targets as demonstrated with a liver-secreted IL-1R2 protein that induced islet proliferation.

cell biology↗

Integrated experimental-computational analysis of a liver-islet microphysiological system for human-centric diabetes research

Microphysiological systems (MPS) are powerful tools for emulating human physiology and replicating disease progression in vitro. MPS could be better predictors of human outcome than current animal models, but mechanistic interpretation and in vivo extrapolation of the experimental results remain significant challenges. Here, we address these challenges using an integrated experimental-computational approach. This approach allows for in silico representation and predictions of glucose metabolism in a previously reported MPS with two organ compartments (liver and pancreas) connected in a closed loop with circulating medium. We developed a computational model describing glucose metabolism over 15 days of culture in the MPS. The model was calibrated on an experiment-specific basis using data from seven experiments, where single-liver or liver-islet cultures were exposed to both normal and hyperglycemic conditions resembling high blood glucose levels in diabetes. The calibrated models reproduced the fast (i.e. hourly) variations in glucose and insulin observed in the MPS experiments, as well as the long-term (i.e. over weeks) decline in both glucose tolerance and insulin secretion. We also investigated the behavior of the system under hypoglycemia by simulating this condition in silico, and the model could correctly predict the glucose and insulin responses measured in new MPS experiments. Last, we used the computational model to translate the experimental results to humans, showing good agreement with published data of the glucose response to a meal in healthy subjects. The integrated experimental-computational framework opens new avenues for future investigations toward disease mechanisms and the development of new therapies for metabolic disorders.

systems biology↗