Resting heart rate is associated with novel plasma atherosclerosis biomarkers

Background: Resting heart rate (RHR) is a strong predictor of adverse cardiovascular outcomes. Both soluble low-density lipoprotein receptor-related protein-1 (sLRP1) and soluble receptor for advanced glycation end products (sRAGE) are novel plasma biomarkers for atherosclerosis. In this study, we examined the potential associations between RHR and plasma sLRP1 and sRAGE levels and whether any associations might be modified by apolipoprotein E (APOE) ε4 carrier status.Methods: This cross-sectional study included 941 apparently healthy adults aged 40 years or older. Plasma sLRP1 and sRAGE levels were measured by a commercial enzyme-linked immunosorbent assay. APOE gene polymorphisms were analyzed by a polymerase chain reaction and Sanger sequencing.Results: RHR was a significant determinant of log-transformed sLRP1 (β = 0.004; 95% confidence interval [CI], 0.002–0.007; P = 0.001) and log-transformed sRAGE (β = 0.005; 95% CI, 0.002–0.007; P <0.001) independently of age, sex, body mass index, blood pressure, blood glucose, blood lipids, lifestyle, and medical history. Additionally, APOE ε4 carrier status was inversely associated with log-transformed plasma sLRP1 level (β = –0.072; 95% CI, –0.130 to –0.015; P = 0.01) and did not modify the relationship between RHR and plasma sLRP1 level. Conclusions: An elevated RHR was associated with increased sLRP1 and sRAGE values, which was not modified by APOE genotype. The underlying mechanism of this effect may be relevant to the progression of atherosclerosis

Enhancing Effect of Glycerol on the Tensile Properties of Bombyx mori Cocoon Sericin Films

An environmental physical method described herein was developed to improve the tensile properties of Bombyx mori cocoon sericin films, by using the plasticizer of glycerol, which has a nontoxic effect compared with other chemical crosslinkers. The changes in the tensile characteristics and the structure of glycerolated (0–40 wt% of glycerol) sericin films were investigated. Sericin films, both in dry and wet states, showed enhanced tensile properties, which might be regulated by the addition of different concentrations of glycerol. The introduction of glycerol results in the higher amorphous structure in sericin films as evidenced by analysis of attenuated total reflection Fourier transform infrared (ATR-FTIR) spectra, thermogravimetry (TGA) and differential scanning calorimetry (DSC) curves. Scanning Electron Microscopy (SEM) observation revealed that glycerol was homogeneously blended with sericin molecules when its content was 10 wt%, while a small amount of redundant glycerol emerged on the surface of sericin films when its content was increased to 20 wt% or higher. Our results suggest that the introduction of glycerol is a novel nontoxic strategy which can improve the mechanical features of sericin-based materials and subsequently promote the feasibility of its application in tissue engineering

Hypertension moderates the relationship between plasma beta-amyloid and cognitive impairment: a cross-sectional study in Xi’an, China

BackgroundPlasma beta-amyloid (Aβ) are important biomarkers for Alzheimer’s disease and cognitive impairment (CI), but results are controversial. It remains unclear whether hypertension modulates their relationship. This cross-sectional study investigates whether hypertension moderates the relationship between plasma Aβ and cognitive impairment (CI).MethodsThis cross-sectional study included 1488 subjects ≥ 40 years from rural areas of northwestern China. CI was defined as a Mini-Mental State Examination score lower than the cutoff. Firstly, plasma Aβ40, Aβ42, Aβ42/Aβ40 were analyzed as restricted cubic spline. Then, categories of combined plasma Aβ were created by making bisection of plasma Aβ according to average and combining them as L-Aβ40 and L-Aβ42, H-Aβ40 and L-Aβ42, L-Aβ40 and H-Aβ42, H-Aβ40 and H-Aβ42. Decreased plasma Aβ40 was defined as < 25th percentile. Multivariate logistic regression examined the relationship between plasma Aβ and CI in total population, the hypertension subgroup and the non-hypertension subgroup.Results737 participants (49.5%) had hypertension and 189 participants (12.7%) had CI. Simultaneously elevated plasma Aβ40 and Aβ42 was associated with CI in hypertension (H-Aβ40 and H-Aβ42 vs. L-Aβ40 and L-Aβ42, 21.1% vs.10.7%, P = 0.033; OR = 1.984 [95% CI, 1.067–3.691], P = 0.030) but not in the non-hypertension. Decreased plasma Aβ40 was associated with CI in the non-hypertension (14.9% vs. 9.2%, P = 0.026; OR = 1.728 [95% CI, 1.018–2.931], P = 0.043) but not in the hypertension.ConclusionHypertension is an important modulator in the relationship between plasma Aβ and CI. Simultaneously elevated plasma Aβ40 and Aβ42 in the hypertension, and decreased plasma Aβ40 in the non-hypertension, may be risk factors for CI. These findings emphasize the need to consider hypertension in CI detection

BPI-GNN: Interpretable brain network-based psychiatric diagnosis and subtyping

Converging evidence increasingly suggests that psychiatric disorders, such as major depressive disorder (MDD) and autism spectrum disorder (ASD), are not unitary diseases, but rather heterogeneous syndromes that involve diverse, co-occurring symptoms and divergent responses to treatment. This clinical heterogeneity has hindered the progress of precision diagnosis and treatment effectiveness in psychiatric disorders. In this study, we propose BPI-GNN, a new interpretable graph neural network (GNN) framework for analyzing functional magnetic resonance images (fMRI), by leveraging the famed prototype learning. In addition, we introduce a novel generation process of prototype subgraph to discover essential edges of distinct prototypes and employ total correlation (TC) to ensure the independence of distinct prototype subgraph patterns. BPI-GNN can effectively discriminate psychiatric patients and healthy controls (HC), and identify biological meaningful subtypes of psychiatric disorders. We evaluate the performance of BPI-GNN against 11 popular brain network classification methods on three psychiatric datasets and observe that our BPI-GNN always achieves the highest diagnosis accuracy. More importantly, we examine differences in clinical symptom profiles and gene expression profiles among identified subtypes and observe that our identified brain-based subtypes have the clinical relevance. It also discovers the subtype biomarkers that align with current neuro-scientific knowledge