Plasma Cell-Free DNA Is a Potential Biomarker for Diagnosis of Calcific Aortic Valve Disease.

INTRODUCTION: Calcific aortic valve disease (CAVD) is the third most common cardiovascular disease in aging populations. Despite a growing number of biomarkers having been shown to be associated with CAVD, a marker suitable for routine testing in clinical practice is still needed. Plasma cell-free DNA (cfDNA) has been suggested as a biomarker for diagnosis and prognosis in multiple diseases. In this study, we aimed to test whether cfDNA could be used as a biomarker for the diagnosis of CAVD. METHODS: Serum samples were collected from 137 diagnosed CAVD patients and 180 normal controls. The amount of cfDNA was quantified by amplifying a short fragment (ALU 115) and a long fragment (ALU 247) using quantitative real-time PCR. The cfDNA integrity (cfDI) was calculated as the ratio of ALU247 to ALU115. The association between CAVD and cfDI was evaluated using regression analysis. RESULTS: CAVD patients had increased ALU 115 fragments (median, 185.14 (416.42) versus 302.83 (665.41), p < 0.05) but a decreased value of cfDI (mean, 0.50 ± 0.25 vs. 0.41 ± 0.26, p < 0.01) in their serum when compared to controls. This difference was more dramatic in non-rheumatic CAVD patients (p < 0.001) versus rheumatic CAVD patients (no significant difference). Similarly, CAVD patients with bicuspid aortic valve (BAV) (p < 0.01) showed a greater difference than non-BAV CAVD patients (p < 0.05). Linear regression and logistic regression showed that cfDI was independently and significantly associated with the presence of CAVD (95% CI, 0.096 to 0.773, p < 0.05). The ROC assay revealed that cfDI combined with clinical characteristics had a better diagnostic value than cfDI alone (AUC = 0.6191, p < 0.001). CONCLUSION: cfDI may be a potential biomarker for diagnosis of CAVD.

Dual-comb spectroscopy of methane based on a free-running Erbium-doped fiber laser.

Dual-comb spectroscopy has been developed into a high-precision technique that is capable of sensing many important species of samples, such as methane. Recent studies on single-cavity, dual-comb light sources further reduce the system complexity of such schemes. In contrast to the previous demonstrations around the lasing spectrum, this work significantly expands the spectral coverage of a dual-comb spectroscopy setup using one free-running laser to a region far beyond the laser's emission wavelengths. Nonlinear wavelength conversion based on soliton self-frequency shift is adopted to convert and tune the wavelengths of both dual-comb pulses to ~1650nm. It is shown that this process has introduced little additional intensity noise. The 2ν3 absorption band of methane from 1647 nm to 1663nm is measured with very good agreement with HITRAN, and the standard deviation of the residual is < ~0.006 after averaging ~1.96 seconds of data. Our results further elucidate the potential of dual-comb spectroscopy using one laser, and could pave the way for the development of low-cost, power-efficient, and compact dual-comb instrument targeting more spectral regions.

Improved broadband dispersion engineering in coupled silicon nitride waveguides with a partially etched gap.

In order to meet the targeted dispersion requirements of applications, hybridized modes of coupled waveguides, whose dispersion characteristics can be significantly altered around their mode cross-point, have been recently investigated. The applications have been applied to microresonators based on concentric waveguides with a thin silicon nitride layer. However, it is still challenging to achieve a low and flattened anomalous dispersion profile just by optimizing the gap width between the waveguides. We propose to investigate the dispersion characteristics of coupled silicon nitride waveguides with a partially etched gap. It is shown that low and broadband anomalous dispersion can be achieved based on relatively thin 515 nm thick silicon nitride layer waveguides. The mechanism of a partially etched gap on dispersion engineering can be attributed to the tuning of the coupling strength between the waveguides. Therefore, when combined with other design parameters, it offers an additional "tuning knob" of advanced dispersion engineering when designing such coupled-waveguide devices for nonlinear photonic applications.

The relationship between plasma nickel concentrations and type 2 diabetes mellitus risk: A protective effect within a specific range.

BACKGROUND: Nickel is considered an essential nutrient for certain microbial, plant, and animal species, but its role in human health remains controversial. Some studies have reported the relationship between nickel and type 2 diabetes mellitus (T2DM), but the results are not consistent and the mechanism is not clear, which needs further exploration. AIM: To investigate the possible correlation between nickel and T2DM. METHODS: We conducted a case-control study of 192 patients with T2DM and 189 healthy controls at a hospital in central China. Plasma concentrations of nickel and six other trace elements were measured with inductively coupled plasma mass spectrometry. Logistic regression models, restricted cubic spline models (RCS), and Bayesian kernel machine regression (BKMR) were used to evaluate the relationship between plasma nickel and T2DM and its metabolic risk factors, as well as the presence or absence of interactions between nickel and other elements. RESULTS: The T2DM group exhibited considerably lower plasma nickel levels than the control group (P < 0.001). Whether using a crude or adjusted model, logistic regression analysis finds a negative correlation between nickel levels and the risk of T2DM (P trend < 0.001). According to the RCS, the risk of T2DM reduces with rising nickel levels when the value is below 6.1 µg/L; nickel has a negative linear correlation with fasting plasma glucose (FPG), an inverse U-shaped connection with superoxide dismutase (SOD), and a positive linear correlation with malondialdehyde (MDA) (all P overall < 0.05). The plasma nickel concentration was positively correlated with zinc, vanadium, and chromium (r = 0.23, 0.11, and 0.19, respectively; all P < 0.05) and negatively correlated with copper (r = - 0.11, P < 0.05). In the BKMR model, interactions of nickel with zinc on T2DM and SOD, nickel with chromium on T2DM and homeostasis model assessment of ß cell (HOMA-ß), and nickel with copper on FPG, homeostasis model assessment of insulin (HOMA-IR), and MDA were observed. CONCLUSION: Nickel may have a dual effect on the risk of T2DM, with a protective range of less than 6.1 µg/L. Potential interactions between nickel, copper, zinc, and chromium existed in their associations with T2DM and its metabolic risk factors.

Deconvoluting Kinetic Rate Constants of Catalytic Substrates from Scanning Electrochemical Approach Curves with Artificial Neural Networks.

Extracting information from experimental measurements in the chemical sciences typically requires curve fitting, deconvolution, and/or solving the governing partial differential equations via numerical (e.g., finite element analysis) or analytical methods. However, using numerical or analytical methods for high-throughput data analysis typically requires significant postprocessing efforts. Here, we show that deep learning artificial neural networks can be a very effective tool for extracting information from experimental data. As an example, reactivity and topography information from scanning electrochemical microscopy (SECM) approach curves are highly convoluted. This study utilized multilayer perceptrons and convolutional neural networks trained on simulated SECM data to extract kinetic rate constants of catalytic substrates. Our key findings were that multilayer perceptron models performed very well when the experimental data were close to the ideal conditions with which the model was trained. However, convolutional neural networks, which analyze images as opposed to direct data, were able to accurately predict the kinetic rate constant of Fe-doped nickel (oxy)hydroxide catalyst at different applied potentials even though the experimental approach curves were not ideal. Due to the speed at which machine learning models can analyze data, we believe this study shows that artificial neural networks could become powerful tools in high-throughput data analysis.

Analysis of factors associated with 6MWD among older patients with chronic heart failure.

OBJECTIVES: To study the factors that influence the 6-minute walking distance (6MWD) among older patients with chronic heart failure. METHODS: This was a single-center, retrospective, observational study. A total of 123 patients from the First Affiliated Hospital of Nanjing Medical University was selected. The factors associated with the 6MWD were analyzed using Pearson correlation analysis and multivariate linear regression. RESULTS: The 6MWD of older patients was negatively correlated with age, fall risk, nutritional score, frailty, and depression but was positively correlated with educational level, fall efficacy, self-care ability, and plasma albumin. The results of independent variable multiple linear regression analysis showed that age (ß = -0.098), fall risk (ß = -0.262), fall efficacy (ß = 0.011), self-care ability (ß = -0.021), nutrition (ß = -0.405), frailty (ß = -0.653), and plasma albumin (ß = 0.127) influenced the 6MWD. CONCLUSIONS: The 6MWD of older patients with chronic heart failure was related to age, self-care ability, fall risk, nutrition, frailty, and depression.