Several tests were performed to check the in vitro and in vivo biocompatibility of the membrane. A highly connected homogeneous network was obtained due to the appropriate orientation of a hard segment and soft segment in the synthesized membrane. Mechanical property analysis indicates the membrane has a strength of 5.15MPa and strain 124%. The membrane showed high hemocompatibility, no cytotoxicity on peripheral blood mononuclear cell, and susceptible to degradation in simulated body fluid solution. Antimicrobial activity assessment has shown promising results against clinically significant bacteria. Primary hypopharyngeal cell growth on the PU membrane revealed the cytocompatibility and subcutaneous implantation on the back of Wistar rats were given in vivo biocompatibility of the membrane. Therefore, the synthesized material can be considered as a potential candidate for a hypopharyngeal tissue engineering application.
Diagnosis of ascending thoracic aortic aneurysm (ATAA) is based on the measurement of the maximum aortic diameter, but size is not a good predictor of the risk of adverse events. There is growing interest in the development of novel image-derived risk strategies to improve patient risk management towards a highly individualized level. In this study, the feasibility and efficacy of deep learning for the automatic segmentation of ATAAs was investigated using UNet, ENet, and ERFNet techniques. Specifically, CT angiography done on 72 patients with ATAAs and different valve morphology (i.e., tricuspid aortic valve, TAV, and bicuspid aortic valve, BAV) were semi-automatically segmented with Mimics software (Materialize NV, Leuven, Belgium), and then used for training of the tested deep learning models. The segmentation performance in terms of accuracy and time inference were compared using several parameters. All deep learning models reported a dice score higher than 88%, suggesting a good agreement between predicted and manual ATAA segmentation. We found that the ENet and UNet are more accurate than ERFNet, with the ENet much faster than UNet. This study demonstrated that deep learning models can rapidly segment and quantify the 3D geometry of ATAAs with high accuracy, thereby facilitating the expansion into clinical workflow of personalized approach to the management of patients with ATAAs.Precise delineation of the ischemic lesion from unimodal Magnetic Resonance Imaging (MRI) is a challenging task due to the subtle intensity difference between the lesion and normal tissues. Hence, multispectral MRI modalities are used for characterizing the properties of brain tissues. Traditional lesion detection methods rely on extracting significant hand-engineered features to differentiate normal and abnormal brain tissues. But the identification of those discriminating features is quite complex, as the degree of differentiation varies according to each modality. This can be addressed well by Convolutional Neural Networks (CNN) which supports automatic feature extraction. It is capable of learning the global features from images effectively for image classification. But it loses the context of local information among the pixels that need to be retained for segmentation. Also, it must provide more emphasis on the features of the lesion region for precise reconstruction. The major contribution of this work is the integration of attention mechanism with a Fully Convolutional Network (FCN) to segment ischemic lesion. This attention model is applied to learn and concentrate only on salient features of the lesion region by suppressing the details of other regions. https://www.selleckchem.com/products/oxythiamine-chloride-hydrochloride.html Hence the proposed FCN with attention mechanism was able to segment ischemic lesion of varying size and shape. To study the effectiveness of attention mechanism, various experiments were carried out on ISLES 2015 dataset and a mean dice coefficient of 0.7535 was obtained. Experimental results indicate that there is an improvement of 5% compared to the existing works.
Social distancing by working-from-home is an effective measure to decrease the spread of COVID-19. However, this new work pattern could also affect the well-being of workers. Therefore, the aim of the study was to study the magnitude of occupational health problems and lifestyle changes among workers who have only recently started working from home.
A cross-sectional study was conducted using online self-administered questionnaires during the coronavirus disease 2019 pandemic in the Bangkok metropolitan area, Thailand. The participants were from any organization that allowed working from home. The demographic dataincluding the analysis of the characteristics of working from home, the occurrence of occupational health problems, and the lifestyle changes caused by working from home were analyzed.
A total of 869 workers were included as study participants. The highest prevalence of physical health problems among all workers was identified to be weight gain at a rate of 40.97% (95% confidence interval=37.69-44.24), and the highest prevalence of psychosocial problems was identified to be cabin fever at a rate of 31.28% (95% confidence interval=26.66-35.90%) among full-time working-from-home workers. The health effects that were significantly related to the intensity of working from home (
for trends <0.05), either positively or negatively, included body weight changes, ergonomic problems, indoor environmental problems, and psychosocial problems. Meanwhile, the lifestyle changes related to work intensity included eating pattern, sleep habits, and exercise.
Working from home can affect workers' well-being in various aspects. Hence, occupational health providers must prepare for risk prevention and health promotion in this "new normal" working life pattern and for future pandemics.
Working from home can affect workers' well-being in various aspects. Hence, occupational health providers must prepare for risk prevention and health promotion in this "new normal" working life pattern and for future pandemics.Date palm (Phoenix dactylifera L.) inflorescence rot caused by Mauginiella scaettae poses a serious threat to date palm in Morocco. The present study aims to determine the antifungal activity of five plant extracts against M. scaettae, including Acacia cyanophylla, Cupressus atlantica, Eucalyptus torquata, Nerium oleander, and Schinus molle and link this effect to their content in phenolics and flavonoids, as well as their antioxidant properties. Plant extracts exhibited significant discrepancies regarding their antifungal activity (p less then 0.05). The extracts of E. torquata and C. atlantica had the strongest and dose-dependent manner inhibitory effect against mycelial growth and spore germination. E. torquata and S. molle caused the greatest sporulation reductions of about 88.05% and 36.11%, respectively. In addition, there were significant differences among the examined plant extracts with respect to their total polyphenols (14.52-76.68 mg GAE/g DW), flavonoids (8.75-57.78 g RE/100 g DW), and antioxidant properties as measured by TEAC (74.