Longitudinal associations between depressive symptoms and cell deformability: do glucocorticoids play a role?

Cell deformability of all major blood cell types is increased in depressive disorders (DD). Furthermore, impaired glucocorticoid secretion is associated with DD, as well as depressive symptoms in general and known to alter cell mechanical properties. Nevertheless, there are no longitudinal studies examining accumulated glucocorticoid output and depressive symptoms regarding cell deformability.

A new hyperelastic lookup table for RT-DC.

Real-time deformability cytometry (RT-DC) is an established method that quantifies features like size, shape, and stiffness for whole cell populations on a single-cell level in real-time. A lookup table (LUT) disentangles the experimentally derived steady-state cell deformation and the projected area to extract the cell stiffness in the form of the Young's modulus. So far, two lookup tables exist but are limited to simple linear material models and cylindrical channel geometries.

Depressive disorders are associated with increased peripheral blood cell deformability: a cross-sectional case-control study (Mood-Morph).

Pathophysiological landmarks of depressive disorders are chronic low-grade inflammation and elevated glucocorticoid output. Both can potentially interfere with cytoskeleton organization, cell membrane bending and cell function, suggesting altered cell morpho-rheological properties like cell deformability and other cell mechanical features in depressive disorders. We performed a cross-sectional case-control study using the image-based morpho-rheological characterization of unmanipulated blood samples facilitating real-time deformability cytometry (RT-DC).

Simulation of Morphogen and Tissue Dynamics.

Morphogenesis, the process by which an adult organism emerges from a single cell, has fascinated humans for a long time. Modeling this process can provide novel insights into development and the principles that orchestrate the developmental processes. This chapter focuses on the mathematical description and numerical simulation of developmental processes.

Speeding up all-against-all protein comparisons while maintaining sensitivity by considering subsequence-level homology.

Orthology inference and other sequence analyses across multiple genomes typically start by performing exhaustive pairwise sequence comparisons, a process referred to as "all-against-all". As this process scales quadratically in terms of the number of sequences analysed, this step can become a bottleneck, thus limiting the number of genomes that can be simultaneously analysed. Here, we explored ways of speeding-up the all-against-all step while maintaining its sensitivity.