Eduardo Reátegui

The paper develops a Bayesian physics-informed neural network to model neutrophil swarming dynamics in human and murine cells, revealing species-specific differences in recruitment and self-limitation.

This paper introduces SPARQ, a method to quantify functional AAV vectors at single-particle resolution, revealing functional heterogeneity and improving dose analysis in gene therapy.

This study develops multidimensional in vitro models to investigate anti-estrogen resistance and dormancy in invasive lobular carcinoma, identifying p27Kip1 signaling as central to dormant phenotype.

This paper introduces LEVA, a label-free, high-resolution, tunable platform for EV adsorption and characterization, demonstrating its versatility with various EV sources and applications.

The paper shows that human differentiated adipocytes can produce EVs similar to mature adipocytes, providing a reliable alternative for studying adipocyte-derived extracellular vesicles in obesity research.

This study uses BEST and MBA to track extracellular vesicle cargo in gastric cancer, showing saliva as the best biofluid for diagnosis.

The paper develops a multiparametric pipeline to isolate and characterize adipose-derived extracellular vesicles from human biospecimens, identifying cell-specific biomarkers and pathways associated with obesity.

The paper introduces a technique to combine optical and non-optical single-EP analyses by limiting Brownian motion, enhancing immunogold phenotyping and enabling orthogonal measurements.