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Kalium channelrhodopsin 1 from Hyphochytrium catenoides (HcKCR1) is a light-gated channel used for optogenetic silencing of mammalian neurons. It selects K+ over Na+ in the absence of the canonical tetrameric K+ selectivity filter found universally in voltage- and ligand-gated channels. The genome of H. catenoides also encodes a highly homologous cation channelrhodopsin (HcCCR), a Na+ channel with >100-fold larger Na+ to K+ permeability ratio. Here, we use cryo-electron microscopy to determine atomic structures of these two channels embedded in peptidiscs to elucidate structural foundations of their dramatically different cation selectivity. Together with structure-guided mutagenesis, we show that K+ versus Na+ selectivity is determined at two distinct sites on the putative ion conduction pathway: in a patch of critical residues in the intracellular segment (Leu69/Phe69, Ile73/Ser73 and Asp116) and within a cluster of aromatic residues in the extracellular segment (primarily, Trp102 and Tyr222). The two filters are on the opposite sides of the photoactive site involved in channel gating. Recently discovered kalium channelrhodopsins (KCRs) are optogenetic tools for neuronal silencing. Here, authors report cryo-electron microscopy structures of KCR1 from Hyphochytriumcatenoides and a highly homologous but sodium-selective channel from the same organism. [ABSTRACT FROM AUTHOR]

Heart failure (HF) and type 2 diabetes mellitus (T2DM) have a synergistic effect on cardiovascular (CV) morbidity and mortality in patients with established CV disease (CVD). The aim of this review is to summarize the knowledge regarding the discriminative abilities of conventional and novel biomarkers in T2DM patients with established HF or at higher risk of developing HF. While conventional biomarkers, such as natriuretic peptides and high-sensitivity troponins demonstrate high predictive ability in HF with reduced ejection fraction (HFrEF), this is not the case for HF with preserved ejection fraction (HFpEF). HFpEF is a heterogeneous disease with a high variability of CVD and conventional risk factors including T2DM, hypertension, renal disease, older age, and female sex; therefore, the extrapolation of predictive abilities of traditional biomarkers on this population is constrained. New biomarker-based approaches are disputed to be sufficient for improving risk stratification and the prediction of poor clinical outcomes in patients with HFpEF. Novel biomarkers of biomechanical stress, fibrosis, inflammation, oxidative stress, and collagen turn-over have shown potential benefits in determining prognosis in T2DM patients with HF regardless of natriuretic peptides, but their role in point-to-care and in routine practice requires elucidation in large clinical trials. [ABSTRACT FROM AUTHOR]

Smoking is well known to be correlated with cardiovascular abnormalities, in particular atherosclerosis and heart diseases. This article investigates the effect and relationship of smoking tobacco on the thickness of the intima–media (IMT) belonging to the common carotid arteries (CCAs), and also blood concentration of the lipid profile (LP), mainly the total cholesterol, High Density Lipoprotein (HDL), Low Density Lipoprotein (LDL) and also triglycerides. Nineteen male tobacco smokers and thirty-five healthy male non-smoking Saudi participants were involved in this study after obtaining their informed consent. An ultrasound and a spectrophotometer were used to determine the IMTs and lipid parameters, respectively. The thicknesses of the smokers' right (RCA) and left carotid (LCA) arteries (0.72 and 0.7 mm, respectively) were significantly greater than the thicknesses of the arteries of the non-smokers (0.58 and 0.62 mm, respectively) (p-value = 0.005 and 0.04). Insignificant differences between the means of the other parameters in the two groups were studied. Smoking is a risk factor for stroke, because it significantly increases the IMTs of both the right and left carotid arteries. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]