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Background: Dose-response and nonlinear relationships of cigarette exposure with sleep disturbances and depression are warranted, and the potential mechanism of sex hormones in such associations remains unclear. Methods: Cigarette exposure, trouble sleeping, and depression were assessed by standard questionnaires, and the levels of cotinine and sex steroid hormones were determined among 9900 adults from the National Health and Nutrition Examination Survey (NHANES). Multiple linear regression, logistic regression, and mediation models were conducted to evaluate the associations between smoking, sex steroid hormones, trouble sleeping, and depression. Results: With never smokers as a reference, current smokers had a higher prevalence of trouble sleeping (OR = 1.931, 95% CI: 1.680, 2.219) and depression (OR = 2.525, 95% CI: 1.936, 3.293) as well as testosterone level (β = 0.083, 95% CI: 0.028, 0.140). Pack-years of smoking and cigarettes per day were positively associated with the prevalence of trouble sleeping and depression as well as testosterone level (Ptrend <0.05). The restricted cubic spline model showed linear relationships of cotinine with trouble sleeping, depression, and testosterone. The positive associations of cigarettes per day with trouble sleeping and depression were greater in females than that in males (Pmodification <0.05). However, the potential role of sex hormones was not observed in the association of cotinine with trouble sleeping or depression (Pmediation >0.05). Conclusion: Smoking may induce sex hormone disturbance and increase the risk of sleep problems and depression symptoms, and ceasing smoking may reduce the risk of such complications. [ABSTRACT FROM AUTHOR]

Different wheat bran antifreeze polysaccharides (WBAP) were first isolated by ice shell separation method. The composition and antifreeze activity of varied WBAP were characterized and their cryoprotective effects on frozen dough were comprehensively studied with the emphasis on ice crystals, yeast and gluten proteins. WBAP with enriched arabinoxylan (AX) of high arabinofuranose/xylopyranose ratio (A/X) showed superior effect on inhibiting ice recrystallization compared with WBAP with low A/X. WBAP could significantly improve the quality of frozen dough steamed bread, and the effect coincided with antifreeze activity. WBAP effectively inhibited the ice recrystallization and altered the shape of ice crystal from cubic to lamellate in frozen dough, consequently better preserving the yeast viability and dough structure. WBAP alleviated the depolymerization of glutenin macropolymers, and was conductive in developing more homogeneous and less distorted gluten network structures in frozen dough. The current study can provide a theoretical basis for the exploitation of WBAP as a technofunctional improver for frozen dough. [Display omitted] • Wheat bran antifreeze polysaccharides (WBAP) with varied structures was isolated. • WBAP possessed thermal hysteresis activity and inhibited ice recrystallization. • The quality of frozen dough steamed bread was significantly improved by WBAP. • WBAP preserved yeast viability and dough structure by affecting ice growth and shape. • WBAP alleviated depolymerization of glutenin macropolymers in frozen dough. [ABSTRACT FROM AUTHOR]

Introduction: Optical coherence tomography (OCT) is an intravascular imaging modality which is widely used clinically for coronary plaque characterization. It has the ability to measure the fibrous cap thickness and to detect lipid content. Therefore, it can be used to identify in vivo thin-cap fibroatheroma(TCFA) and to detect plaque vulnerability. However, it takes much time for a doctor to learn to understand OCT images. We proposed a novel method of detecting vulnerable coronary plaques in OCT images of coronary artery disease (CAD) patients with deep learning. Methods and Results: The procedure includes: 1) preprocessing of the original images including coordinates transformation, image denoising and data augmentation; 2) deep learning network training on a dataset of 2000 images including classification, object detection and segmentation; 3) postprocessing of the detected images including image filtering and expert knowledge based adjustment. The model was tested on a separate series of 300 coronary OCT images. The results indicate that applying the image detecting method provided by this paper, the accuracy rate was 85.0% while the sensitivity (recall rate) was 93%. Conclusions: Our artificial intelligence method can help improve clinical decision-making and reduce human medical errors of Cardiologists, thus help provide a more individualized treatment for each CAD patients. [ABSTRACT FROM AUTHOR]