Project description:Stress diseases such as affective disorders are often characterized by a disturbed regulation of the hypothalamus-pituitary-adrenocortical (HPA) axis. This dysregulation can be explained by an impaired function of the receptors involved in the HPA-axis regulation, for example, the glucocorticoid receptors (GR). The regulation process of the HPA axis and the GR function are influenced by several genes, for instance by NR3C1 coding for the GR and also by FKBP5, a co-chaperone in the GR-complex. Binder et al. showed that common polymorphisms in FKBP5 are associated with increased FKBP5 protein expression as well as correlation between cortisol levels and peripheral blood FKBP5 mRNA expression. Regarding the effects of FKPB5 genotypes on psychosocial stress reaction, Ising and colleagues tested healthy subjects with the Trier Social Stress Test, a standardized paradigm to induce psychosocial stress. Subjects homozygous for any of the FKBP5 variants showed an incomplete normalization of the stress induced cortisol secretion. Recent studies demonstrated that FKBP5 and NR3C1 are involved in the endocrine stress reaction. Therefore, we expected changes of FKBP5 and NR3C1 mRNA expression in peripheral blood after exposure to a psychosocial stress situation. To address this, we performed a pilot study where we tested six healthy young men without history of psychiatric or severe somatic disorders and applied the trier social stress test (TSST). Before and after two consecutive TSSTs, we took blood samples with a venous catheter in order to measure ACTH and cortisol in plasma and mRNA expression of the candidate genes in peripheral blood. Blood cells were stabilized using PAXgene tubes, and gene expression was processed by qPCR. Briefly after the psychosocial stress the stress hormones ACTH and cortisol increased whereas the reaction to the second TSST was lower suggesting a habituation effect. These endocrine stress responses were followed by an alteration in FKBP5 gene expression, further underlining the importance of this gene for the neuroendocrine stress reaction. NR3C1 mRNA levels did not change after the TSST. Our preliminary data indicate an effect of psychosocial stress on the FKBP5 mRNA levels. Further research with larger samples sizes is required to replicate and extend these results.
Project description:Microfluidic magnetophoresis is a powerful technique that is used to separate and/or isolate cells of interest from complex matrices for analysis. However, mechanical pumps are required to drive flow, limiting portability and making translation to point-of-care (POC) settings difficult. Microfluidic paper-based analytical devices (μPADs) offer an alternative to traditional microfluidic devices that do not require external pumps to generate flow. However, μPADs are not typically used for particle analysis because most particles become trapped in the porous fiber network. Here we report the ability of newly developed fast-flow microfluidic paper-based analytical devices (ffPADs) to perform magnetophoresis. ffPADs use capillary action in a gap between stacked layers of paper and transparency sheets to drive flow at higher velocities than traditional μPADs. The multi-layer ffPADs allow particles and cells to move through the gap without being trapped in the paper layers. We first demonstrate that ffPADs enable magnetic particle separations in a μPAD with a neodymium permanent magnet and study key factors that affect performance. To demonstrate utility, E. coli was used as a model analyte and was isolated from human urine before detection with a fluorescently labeled antibody. A capture efficiency of 61.5% was then obtained of E. coli labeled magnetic beads in human urine. Future studies will look at the improvement of the capture efficiency and to make this assay completely off-chip without the need of a fluorescent label. The assay and device described here demonstrate the first example of magnetophoresis in a paper based, pump free microfluidic device.
Project description:Many complex physical systems exhibit a rich variety of discrete behavioural modes. Often, the system complexity limits the applicability of standard modelling tools. Hence, understanding the underlying physics of different behaviours and distinguishing between them is challenging. Although traditional machine learning techniques could predict and classify behaviour well, typically they do not provide any meaningful insight into the underlying physics of the system. In this paper we present a novel method for extracting physically meaningful clusters of discrete behaviour from limited experimental observations. This method obtains a set of physically plausible functions that both facilitate behavioural clustering and aid in system understanding. We demonstrate the approach on the V-shaped falling paper system, a new falling paper type system that exhibits four distinct behavioural modes depending on a few morphological parameters. Using just 49 experimental observations, the method discovered a set of candidate functions that distinguish behaviours with an error of 2.04%, while also aiding insight into the physical phenomena driving each behaviour.