Tseng, S. Y. | Search bioRxiv

bioRxiv2026

Plasma Extracellular Vesicle and miRNA Profiles in Pregnancies Complicated by Fetal Congenital Heart Defects

Congenital heart defects (CHDs) are the most common form of fetal malformation however, our understanding of trophoblast health and communication throughout gestation in CHD pregnancies remains limited. The purpose of this study was to assess extracellular vesicles (EVs) and microRNA (miRNA) present in maternal and umbilical cord plasma from a spectrum of CHD subtypes during gestation and at time of delivery. We hypothesized that circulating placenta-derived EVs and miRNA will differ in CHD when compared to controls. Maternal plasma samples were collected between 16-24 weeks of gestation and at the time of delivery. Umbilical cord plasma was obtained following delivery. EVs were isolated from plasma samples using nanoscale flow cytometry, and total EV counts as well as counts by cellular origin were determined. MicroRNA was extracted from maternal plasma and levels quantified using qPCR. Maternal plasma from pregnancies complicated by fetal CHD exhibited higher total EV counts at delivery compared to control. Platelets derived extracellular vesicles (pdEVs) were significantly higher both in maternal and cord blood plasma at the time of delivery in CHD pregnancies compared to gestationally age-matched control pregnancies. Circulating miR22 and miR421 levels were reduced, while miR29c levels were increased in maternal plasma from CHD pregnancies between 16-24 weeks but no differences seen at time of delivery. Pregnancies complicated by CHD are associated with an altered in utero environment by changes in extracellular vesicles and miRNA profile in maternal serum. Circulating EVs and miRNA profiles may therefore serve as minimally invasive indicators of placental and maternal vascular dysfunction in CHD.

cell biology↗

bioRxiv2026

Explainable prediction and simulation of complex system dynamics through networks of manifolds

Complex systems such as brains and other interacting biological and physical processes are difficult to represent because they evolve across many variables, scales, and nonlinear interactions. To capture these multivariate, multiscale interactions we have developed Generative Manifold Networks (GMNs) a machine learning framework consisting of a network of linked dynamical systems. The network is discovered by an interaction function which can focus on causality, shared information, nonlinearity or other metric. Network nodes are low-dimensional data-driven state-space manifolds with generator functions accommodating multiscale dynamics. In contrast to many machine learning approaches GMNs have no latent or randomly initialized variables providing transparent explainability. GMNs generate short term dynamics of chaos on par with echo state networks while outperforming them in long term generation of chaos and neural dynamics, but with a markedly reduced number of dimensions and without sensitive dependence on reservoir parameters or random states. As a result of their holistic, multiscale representation GMNs can learn the complete dynamics of a complex system. We further show that GMNs are universal approximators. GMNs are demonstrated on chaotic dynamics, neural and behavioral recordings of the fruit fly and domestic rat with comparisons to echo state networks and crossformer - a time series transformer. SignificanceA major challenge in machine learning is to model complex systems accurately without losing interpretability. Many methods that succeed in prediction rely on latent variables obscuring mechanistic insight and complicating experimental testing. Generative manifold networks (GMN) construct a network of low-dimensional functional manifolds directly from observed variables with no latent or randomly initialized variables: the model remains transparent and experimentally testable. We prove that GMN are universal approximators showing that high representational power can be achieved without sacrificing explainability. GMN therefore provides a general framework for prediction and simulation in neuroscience and complex systems where unraveling the links between variables in an experimentally testable manner is as important as forecasting their behavior.

neuroscience↗