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All cells generate contractile tension. This strain is crucial for mechanically controlling the cell shape, function and survival. In this study, the CellDrum technology quantifying cell's (the cellular) mechanical tension on a pico-scale was used to investigate the effect of lipopolysaccharide (LPS) on human aortic endothelial cell (HAoEC) tension. The LPS effect during gram-negative sepsis on endothelial cells is cell contraction causing endothelium permeability increase. The aim was to finding out whether recombinant activated protein C (rhAPC) would reverse the endothelial cell response in an in-vitro sepsis model. In this study, the established in-vitro sepsis model was confirmed by interleukin 6 (IL-6) levels at the proteomic and genomic levels by ELISA, real time-PCR and reactive oxygen species (ROS) activation by florescence staining. The thrombin cellular contraction effect on endothelial cells was used as a positive control when the CellDrum technology was applied. Additionally, the Ras homolog gene family, member A (RhoA) mRNA expression level was checked by real time-PCR to support contractile tension results. According to contractile tension results, the mechanical predominance of actin stress fibers was a reason of the increased endothelial contractile tension leading to enhanced endothelium contractility and thus permeability enhancement. The originality of this data supports firstly the basic measurement principles of the CellDrum technology and secondly that rhAPC has a beneficial effect on sepsis influenced cellular tension. The technology presented here is promising for future high-throughput cellular tension analysis that will help identify pathological contractile tension responses of cells and prove further cell in-vitro models.
Background
Minor changes in protein structure induced by small organic and inorganic molecules can result in significant metabolic effects. The effects can be even more profound if the molecular players are chemically active and present in the cell in considerable amounts. The aim of our study was to investigate effects of a nitric oxide donor (spermine NONOate), ATP and sodium/potassium environment on the dynamics of thermal unfolding of human hemoglobin (Hb). The effect of these molecules was examined by means of circular dichroism spectrometry (CD) in the temperature range between 25°C and 70°C. The alpha-helical content of buffered hemoglobin samples (0.1 mg/ml) was estimated via ellipticity change measurements at a heating rate of 1°C/min.
Results
Major results were:
1) spermine NONOate persistently decreased the hemoglobin unfolding temperature T u irrespectively of the Na + /K + environment,
2) ATP instead increased the unfolding temperature by 3°C in both sodium-based and potassium-based buffers and
3) mutual effects of ATP and NO were strongly influenced by particular buffer ionic compositions. Moreover, the presence of potassium facilitated a partial unfolding of alpha-helical structures even at room temperature.
Conclusion
The obtained data might shed more light on molecular mechanisms and biophysics involved in the regulation of protein activity by small solutes in the cell.
Optical coherence tomography : a potential tool to predict premature rupture of fetal membranes
(2013)
Background
True date palms (Phoenix dactylifera L.) are impressive trees and have served as an indispensable source of food for mankind in tropical and subtropical countries for centuries. The aim of this study is to differentiate date palm tree varieties by analysing leaflet cross sections with technical/optical methods and artificial neural networks (ANN).
Results
Fluorescence microscopy images of leaflet cross sections have been taken from a set of five date palm tree cultivars (Hewlat al Jouf, Khlas, Nabot Soltan, Shishi, Um Raheem). After features extraction from images, the obtained data have been fed in a multilayer perceptron ANN with backpropagation learning algorithm.
Conclusions
Overall, an accurate result in prediction and differentiation of date palm tree cultivars was achieved with average prediction in tenfold cross-validation is 89.1% and reached 100% in one of the best ANN.