From microscopy data to in silico environments for in vivo-oriented simulations

In our previous study, we introduced a combination methodology of Fluorescence Correlation Spectroscopy (FCS) and Transmission Electron Microscopy (TEM), which is powerful to investigate the effect of intracellular environment to biochemical reaction processes. Now, we developed a reconstruction method of realistic simulation spaces based on our TEM images. Interactive raytracing visualization of this space allows the perception of the overall 3D structure, which is not directly accessible from 2D TEM images. Simulation results show that the diffusion in such generated structures strongly depends on image post-processing. Frayed structures corresponding to noisy images hinder the diffusion much stronger than smooth surfaces from denoised images. This means that the correct identification of noise or structure is significant to reconstruct appropriate reaction environment in silico in order to estimate realistic behaviors of reactants in vivo. Static structures lead to anomalous diffusion due to the partial confinement. In contrast, mobile crowding agents do not lead to anomalous diffusion at moderate crowding levels. By varying the mobility of these non-reactive obstacles (NRO), we estimated the relationship between NRO diffusion coefficient (Dnro) and the anomaly in the tracer diffusion (α). For Dnro=21.96 to 44.49 μ m2/s, the simulation results match the anomaly obtained from FCS measurements. This range of the diffusion coefficient from simulations is compatible with the range of the diffusion coefficient of structural proteins in the cytoplasm. In addition, we investigated the relationship between the radius of NRO and anomalous diffusion coefficient of tracers by the comparison between different simulations. The radius of NRO has to be 58 nm when the polymer moves with the same diffusion speed as a reactant, which is close to the radius of functional protein complexes in a cell.ISSN:1687-4145ISSN:1687-415

Infection risk in hemodialysis patient

Chronic care patients undergoing hemodialysis for treatment of end-stage renal failure experience higher rates of bloodstream-associated infection due to the patients' compromised immune system and management of the bloodstream through catheters. Staphylococcus species are a common cause of hemodialysis catheter-related bloodstream infections. We investigated environmental bacterial contamination of dialysis wards and contamination of hemodialysis devices to determine the source of bacteria for these infections. All bacterial samples were collected by the swab method and the agarose stamp method. And which bacterium were identified by BBL CRYSTAL Kit or 16s rRNA sequences. In our data, bacterial cell number of hemodialysis device was lower than environment of patient surrounds. But Staphylococcus spp. were found predominantly on the hemodialysis device (46.8%), especially on areas frequently touched by healthcare-workers (such as Touch screen). Among Staphylococcus spp., Staphylococcus epidermidis was most frequently observed (42.1% of Staphylococcus spp.), and more surprising, 48.2% of the Staphylococcus spp. indicated high resistance for methicillin. Our finding suggests that hemodialysis device highly contaminated with bloodstream infection associated bacteria. This study can be used as a source to assess the risk of contamination-related infection and to develop the cleaning system for the better prevention for bloodstream infections in patients with hemodialysis

Detailed analysis of distraction induced by in-vehicle verbal interactions on visual search performance

We examined the negative effect of in-vehicle verbal interaction on visual search performance. Twenty participants performed a primary visual search task and a secondary verbal interaction task concurrently. We found that visual search performance deteriorated when the secondary task involving memory retrieval and speech production was performed concurrently. Moreover, a detailed analysis of the reaction time as a function of set size revealed that the increased reaction time was attributed not to the slowing of inspecting each item but to the increased processing time other than the inspection of each visual item, possibly due to task switching between the primary visual search task and the secondary verbal task. These findings have implications for providing information from in-vehicle information devices while reducing the risk of driver distraction

Multiple biomarkers of sepsis identified by novel time-lapse proteomics of patient serum.

Serum components of sepsis patients vary with the severity of infection, the resulting inflammatory response, per individual, and even over time. Tracking these changes is crucial in properly treating sepsis. Hence, several blood-derived biomarkers have been studied for their potential in assessing sepsis severity. However, the classical approach of selecting individual biomarkers is problematic in terms of accuracy and efficiency. We therefore present a novel approach for detecting biomarkers using longitudinal proteomics data. This does not require a predetermined set of proteins and can therefore reveal previously unknown related proteins. Our approach involves examining changes over time of both protein abundance and post-translational modifications in serum, using two-dimensional gel electrophoresis (2D-PAGE). 2D-PAGE was conducted using serum from n = 20 patients, collected at five time points, starting from the onset of sepsis. Changes in protein spots were examined using 49 spots for which the signal intensity changed by at least two-fold over time. These were then screened for significant spikes or dips in intensity that occurred exclusively in patients with adverse outcome. Individual level variation was handled by a mixed effects model. Finally, for each time transition, partial correlations between spots were estimated through a Gaussian graphical model (GGM) based on the ridge penalty. Identifications of spots of interest by tandem mass spectrometry revealed that many were either known biomarkers for inflammation (complement components), or had previously been suggested as biomarkers for kidney failure (haptoglobin) or liver failure (ceruloplasmin). The latter two are common complications in severe sepsis. In the GGM, many of the tightly connected spots shared known biological functions or even belonged to the same protein; including hemoglobin chains and acute phase proteins. Altogether, these results suggest that our screening method can successfully identify biomarkers for disease states and cluster biologically related proteins using longitudinal proteomics data derived from 2D-PAGE

Diagnosis of Sepsis by AI-Aided Proteomics Using 2D Electrophoresis Images of Patient Serum Incorporating Transfer Learning for Deep Neural Networks

An accuracy of ≥98% was achieved in sepsis diagnosis using serum samples from 30 sepsis patients and 68 healthy individuals and a high-performance two-dimensional polyacrylamide gel electrophoresis (HP-2D-PAGE) method developed here with deep learning and transfer learning algorithms. In this method, small-scale target domain data, which are collected to achieve our objective, are inputted directly into a model constructed with source domain data which are collected from a different domain from the target; target vectors are estimated with the outputted target domain data and applied to refine the model. Recognition performance of small-scale data is improved by reusing all layers, including the output layers of the neural network. Proteomics is generally considered the ultimate bio-diagnostic technique and provides extremely high information density in its two-dimensional electrophoresis images, but extracting the data has posed a basic problem. The present study is expected to solve that problem and will be an important breakthrough for practical utilization and future perspectives of proteomics in clinics after evaluation in clinical settings