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ently associated with disease progression. Better ACE-27 scores appear to predict improved oncologic control.In this study, Surface Enhanced Raman Spectroscopy (SERS) was used for the characterization of Hepatitis C virus (HCV) in blood serum samples. For this purpose silver nanoparticles (Ag NPs) were used as substrates and SERS spectra were acquired from different clinically diagnosed HCV positive serum samples as well as from healthy individuals. Notably, same set of samples were also evaluated with Raman spectroscopy and SERS was found to be more helpful for the identification of the spectral features associated with the development of HCV infection. Different SERS features associated with the RNA bases were observed solely in the HCV positive serum as compared to the healthy samples which can be considered as SERS spectral markers of the HCV infection. Furthermore, principal component analysis (PCA) of the SERS spectral data was found to be very helpful in differentiation of spectral data of serum samples with different viral loads PLSR model was constructed to compare the capability of SERS and Raman analysis in the prediction of viral loads. It is found that SERS shows lower root mean square error of cross validation (RMSECV) and higher goodness of the model (R2) values than Raman data.The selectivity of single-amino acid nanosensors is still not well understood. Herein, the factors that govern graphene-based nanomaterials for the selective detection of lysine are reported to guide the design of single-amino acid nanosensors. Graphene quantum dots (GQDs), nitrogen-doped GQDs (N-GQDs), and nitrogen/sulfur co-doped GQDs (N,S-GQDs) were used to sense lysine. The interaction mode and mechanism adjusted selectivity of the zero-dimensional graphene-based quantum dots to lysine ascribe to the solution behavior, molecular size, number of atoms as electron donors in graphene, and driving force. Being a basic amino acid, lysine is protonated with a positive charge below solution pH of 9. It adsorbed on the graphene-based quantum dots via electrostatic attraction, which blocked the internal charge transfer pathway inducing fluorescence enhancement at 420 nm. The protonated ɛ-amine side of lysine is responsible for the course. The small diameter of the lysine of ɛ-amine ( less then 0.35 nm) favored its approach to the quantum dots, resulting in a fluorescence change, which could not be achieved with the larger arginine. The activated sites for interaction with lysine located at the edges of the layers of graphene to reach high selectivity. Pidnarulex The N-GQDs and N,S-GQDs are much more sensitive to lysine than the GQDs because they contain nitrogen atoms as electron donors. They had similar linear detection ranges and detection limits, which suggested that the contribution of sulfur for lysine detection was minor. The results of this study provide new insights into the design of GQDs-based single-analyte nanosensors with high selectivity.A novel sensitive and simple spectrofluorimetric method has been developed then validated for the determination of trimetazidine in pure form and its tablets. This method is found on the reaction between trimetazidine’s secondary amine moiety with NBD-Cl reagent, using borate buffer at pH 8.0 yielding a highly fluorescent product whose fluorescence intensity was measured at 526 nm (excitation at 466 nm). A calibration curve plotted showed that the linear range of the presented method was (50-700 ng/ml) with a correlation coefficient of 0.9998. The limits of detection (LOD) and limits of quantitation (LOQ) values were 15.01 and 45.50 ng/ml respectively. The presented approach was validated according to ICH guidelines and successfully applied for determining trimetazidine in its tablets with a mean percentage recovery of 99.65% ± 1.04, 99.23% ± 0.80 and 98.33% ± 1.03 for Metacardia® (20 mg), Vastor ® (20 mg) and Tricardia® (20 mg) tablets respectively. Finally, the proposed method was adopted to study the content uniformity test according to USP guidelines.

Recently, deep convolutional neural network has significantly improved image classification and image segmentation. If coronary artery disease (CAD) can be diagnosed through machine learning and deep learning, it will significantly reduce the burdens of the doctors and accelerate the critical patient diagnoses. The purpose of the study is to assess the practicability of utilizing deep learning approaches to process coronary computed tomographic angiography (CCTA) imaging (termed CCTA-artificial intelligence, CCTA-AI) in coronary artery stenosis.

A CCTA reconstruction pipeline was built by utilizing deep learning and transfer learning approaches to generate auto-reconstructed CCTA images based on a series of two-dimensional (2D) CT images. 150 patients who underwent successively CCTA and digital subtraction angiography (DSA) from June 2017 to December 2017 were retrospectively analyzed. The dataset was divided into two parts comprising training dataset and testing dataset. The training dataset included theare 86% and 83%, 88% and 59%, 85% and 94%, 73% and 84%, 94% and 83%, respectively. In the aspect of identifying plaque classification, accuracy of CCTA-AI is moderate compared to traditional CCTA (AUC=0.750, P < 0.001).

The proposed CCTA-AI allows the generation of auto-reconstructed CCTA images from a series of 2D CT images. This approach is relatively accurate for detecting ≥50% stenosis and analyzing plaque features compared to traditional CCTA.

The proposed CCTA-AI allows the generation of auto-reconstructed CCTA images from a series of 2D CT images. This approach is relatively accurate for detecting ≥50% stenosis and analyzing plaque features compared to traditional CCTA.

Fatigue is an important cause of operational errors, and human errors are the main cause of accidents. This study is an exploratory study in China. Field tests were conducted on heart rate variability (HRV) parameters and physiological indicators of fatigue among miners in high-altitude, cold and low-oxygen areas. This paper studies heart activity patterns during work fatigue in miners.

Fatigue affects both the sympathetic and parasympathetic nervous systems, and it is expressed as an abnormal pattern of HRV parameters. Thirty miners were selected as subjects for a field test, and HRV was extracted from 60 groups of electrocardiography (ECG) datasets as basic signals for fatigue analysis. Then, we analyzed the HRV signals of the miners using linear (time domain and frequency domain) and nonlinear dynamics (Poincaré plot and sample entropy (SampEn)), and a Pearson’s correlation coefficient analysis and t-tests were performed on the measured indices.

The results showed that the time-domain indices (SDNN, RMSSD, SDSD, pNN50, RRn, heart rate (HR), R-wave humps (RH)) and the coefficient of variation (CV)) and the frequency-domain indices (low frequency/high frequency (LF/HF), LFnorm and HFnorm) clearly changed after fatigue.