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Background: The causal relationship between chronic venous insufficiency (CVI) and cardiovascular diseases (CVDs) has yet to be elucidated. Herein, we implement Mendelian randomization (MR) analysis to investigate the causal association. Methods: A two-sample MR approach using genetic data from FinnGen and genome-wide association studies (GWAS) Catalog was applied to investigate the causal relationship between CVI and CVDs. This study assessed 77 single nucleotide polymorphisms (SNPs) as instrumental variables, employing random-effect inverse-variance-weighted MR, weighted median, Egger regression, Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO), and Robust Adjusted Profile Score (RAPS) methods. Multivariable MR (MVMR) considered confounding factors. Results: Genetically predicted CVI was associated with reduced heart failure risk (odds ratio (OR) = 0.96, 95% confidence interval (95% CI): 0.93–0.99, p = 0.025) and increased atrial fibrillation risk (OR = 1.06, 95% CI: 1.03–1.09, p = 0.0002). MVMR, adjusting for venous thromboembolism (VTE), lower limb ulceration, obesity, smoking, and alcohol, attenuated these associations. No significant links were found with hypertension, aortic aneurysm, coronary artery disease, myocardial infarction, valvular heart disease, or stroke. Conclusions: This MR study supports an association between CVI and CVDs, which may imply CVI should be monitored during the treatment of heart failure and atrial fibrillation. [ABSTRACT FROM AUTHOR]

Research on the acoustic performance of an anechoic coating composed of cavities in a viscoelastic material has recently become an area of great interest. Traditional forward research methods are unable to manipulate sound waves accurately and effectively, are difficult to analyse, have time-consuming solution processes, and have large optimization search spaces. To address these issues, this paper proposes a deep learning-based inverse research method to efficiently invert the material parameters of Alberich-type sound absorption coatings and rapidly predict their acoustic performance. First, an autoencoder (AE) model is pretrained to reconstruct the viscoelastic material parameters of an Alberich-type sound absorption coating, the material parameters are extracted into the latent feature space by the encoder, and the decoder model is saved. The internal relationship between the reflection coefficient and latent feature space is trained to establish a multilayer perceptron (MLP). Then, the reflection coefficients in the test set are input to the trained MLP and decoder models to automatically invert the material parameters. The accuracy of the inversion result is 95.08%. Finally, a predictive model is trained to rapidly predict the acoustic performance of the inverted material parameters. The speed of a single test target is 80 times faster than that of the finite element method (FEM). Furthermore, sound absorber material parameters with the best sound absorption performance and a three-band sound absorber are inverted, and their actual sound absorption performance is predicted by the proposed method. The proposed deep learning-based inversion research method provides a solution for low-frequency, wide-band, strong attenuation, and precisely controlled sound waves. It achieves an efficient inversion of material parameters and the rapid forecasting of acoustic performance. The training model can be used for a sound absorbing coating composed of irregular cavities in a viscoelastic material and predict its acoustic performance by only modifying the dataset. [ABSTRACT FROM AUTHOR]

A cavity viscoelastic structure has a good sound absorption performance and is often used as a reflective baffle or sound absorption cover in underwater acoustic structures. The acoustic performance field has become a key research direction worldwide. Because of the time-consuming shortcomings of the traditional numerical analysis method and the high cost of the experimental method for measuring the reflection coefficient to evaluate the acoustic performance of coatings, this innovative study predicted the reflection coefficient of a viscoelastic coating containing a cylindrical cavity based on an artificial neural network (ANN). First, themapping relationship between the input characteristics and reflection coefficient was analysed. When the elastic modulus and loss factor value were smaller, the characteristics of the reflection coefficient curve were more complicated. These key parameters affected the acoustic performance of the viscoelastic coating. Second, a dataset of the acoustic performance of the viscoelastic coating containing a cylindrical cavity was generated based on the finite elementmethod (FEM), which avoided a large number of repeated experiments. The minmax normalization method was used to preprocess the input characteristics of the viscoelastic coating, and the reflection coefficient was used as the dataset label. The grid search method was used to fine-tune the ANNparameters, and the prediction error was studied based on a 10-fold cross-validation. Finally, the error distributions were analysed. The average root means square error (RMSE) and the mean absolute percentage error (MAPE) predicted by the improved ANN model were 0.298% and 1.711%, respectively, and the Pearson correlation coefficient (PCC) was 0.995, indicating that the improved ANN model accurately predicted the acoustic performance of the viscoelastic coating containing a cylindrical cavity. In practical engineering applications, by expanding the database of the material range, cavity size and backing of the coating, the reflection coefficient of more sound-absorbing layers was evaluated, which is useful for efficiently predicting the acoustic performance of coatings in a specific frequency range and has great application value. [ABSTRACT FROM AUTHOR]

The protective effects of dioscin, a natural steroidal saponin from some medicinal plants including Dioscorea nipponica Makino, against lipopolysaccharide (LPS)-induced acute liver and renal damages have been reported in our previous works. However, the actions of dioscin against LPS-induced acute lung injury (ALI) is still unknown. In the present study, we investigated the effects and mechanisms of dioscin against LPS-induced ALI in vitro and in vivo. The results showed that dioscin obviously inhibited cell proliferation and markedly decreased reactive oxidative species level in 16HBE cells treated by LPS. In addition, dioscin significantly protected LPS-induced histological changes, inhibited the infiltration of inflammatory cells, as well as decreased the levels of MDA, SOD, NO and iNOS in mice and rats (p < 0.05). Mechanistically, dioscin significantly decreased the protein levels of TLR4, MyD88, TRAF6, TKB1, TRAF3, phosphorylation levels of PI3K, Akt, IkBa, NF-kB, and the mRNA levels of IL-1β, IL-6, and TNF-α against oxidative stress and inflammation (p < 0.05). Dioscin significantly reduced the overexpression of TLR4, and obviously down-regulated the levels of MyD88, TRAF6, TKB1, TRAF3, p-PI3K, p-Akt, p-IkBα, and p-NF-kB. These findings provide new perspectives for the study of ALI. Dioscin has protective effects on LPS-induced ALI via adjusting TLR4/MyD88- mediated oxidative stress and inflammation, which should be a potent drug in the treatment of ALI. [ABSTRACT FROM AUTHOR]

In search of new antileukopenia agents, twenty dithiolopyrrolone derivatives were synthesized and evaluated for their leukocyte-increasing activities in normal mice. Among the synthesized compounds 4-23, compounds 5 and 6 showed significant leukocyte-increasing activity ( p < 0.01), and compounds 4, 9 and 16 had a moderate effect ( p < 0.05). Compound 5 also displayed stronger leukocyte-increasing activity than that of the positive recombinant human granulocyte colony stimulating factor (rhG-CSF). Above all, compound 5 would be a potential antileukopenia agent which deserved further research. [ABSTRACT FROM AUTHOR]

Fungi in the genus Malassezia are ubiquitous skin residents of humans and other warm-blooded animals. Malassezia are involved in disorders including dandruff and seborrheic dermatitis, which together affect >50% of humans. Despite the importance of Malassezia in common skin diseases, remarkably little is known at the molecular level. We describe the genome, secretory proteome, and expression of selected genes of Malassezia globosa. Further, we report a comparative survey of the genome and secretory proteome of Malassezia restricta, a close relative implicated in similar skin disorders. Adaptation to the skin environment and associated pathogenicity may be due to unique metabolic limitations and capabilities. For example, the lipid dependence of M. globosa can be explained by the apparent absence of a fatty acid synthase gene. The inability to synthesize fatty acids may be complemented by the presence of multiple secreted lipases to aid in harvesting host lipids. In addition, an abundance of genes encoding secreted hydrolases (e.g., lipases, phospholipases. aspartyl proteases, and acid sphingomyelinases) was found in the M. g!obosa genome. In contrast, the phylogenetically closely related plant pathogen Ustilago maydis encodes a different arsenal of extracellular hydrolases with more copies of glycosyl hydrolase genes. M. globosa shares a similar arsenal of extracellular hydrolases with the phylogenetically distant human pathogen, Candida albicans, which occupies a similar niche, indicating the importance of host-specific adaptation. The M. globosa genome sequence also revealed the presence of mating-type genes, providing an indication that Malassezia may be capable of sex. [ABSTRACT FROM AUTHOR]

We will show that ocean-reflected signals from the global positioning system (GPS) navigation satellite constellation can be detected from a low-earth orbiting satellite and that these signals show rough correlation with independent measurements of the sea winds. We will present waveforms of ocean-reflected GPS signals that have been detected using the experiment onboard the United Kingdom's Disaster Monitoring Constellation satellite and describe the processing methods used to obtain their delay and Doppler power distributions. The GPS bistatic radar experiment has made several raw data collections, and reflected GPS signals have been found on all attempts. The down linked data from an experiment has undergone extensive processing, and ocean-scattered signals have been mapped across a wide range of delay and Doppler space revealing characteristics which are known to be related to geophysical parameters such as surface roughness and wind speed. Here we will discuss the effects of integration time, reflection incidence angle and examine several delay-Doppler signal maps. The signals detected have been found to be in general agreement with an existing model (based on geometric optics) and with limited independent measurements of sea winds; a brief comparison is presented here. These results demonstrate that the concept of using bistatically reflected global navigation satellite systems signals from low earth orbit is a viable means of ocean remote sensing. [ABSTRACT FROM AUTHOR]