Red ginseng has long been used as a traditional medicine in many East Asian countries including Korea. It is known to exhibit various pharmacological effects, including anti-oxidant, anti-cancer, anti-stress and anti-diabetes activities. To further explore its actions, the present study evaluated effects of Korean red ginseng (KRG) extract on neuronal injury induced by various types of insults using primary cultured rat cortical cells. KRG extract inhibited neuronal damage and generation of intracellular reactive oxygen species (ROS) induced by excitatory amino acids, such as glutamate and N-methyl-D-aspartate (NMDA), or by A β(25-35) . To elucidate possible mechanism(s) by which KRG extract exerts neuroprotective action, its effects on apoptosis and apoptosis-related signaling molecules in neurons were assessed. KRG extract markedly increased phosphorylation of Bad at Ser 112 and inhibited Bax expression and caspase 3 activity. It also inhibited DNA fragmentation induced by NMDA or A β(25-35) . These results indicate that KRG extract protects cultured neurons from excitotoxicity and A β(25-35) -induced toxicity through inhibition of ROS generation and apoptotic cell death. In addition, KRG extract inhibited β-secretase activity, implying that it may reduce A β peptide formation. Taken together, these findings suggest that KRG extract may be beneficial for the prevention and/or treatment of neurodegenerative disorders including Alzheimer's disease.
The Internet of Medical Things (IoMT) has revolutionized Ambient Assisted Living (AAL) by interconnecting smart medical devices. These devices generate a large amount of data without human intervention. Learning-based sophisticated models are required to extract meaningful information from this massive surge of data. In this context, Deep Neural Network (DNN) has been proven to be a powerful tool for disease detection. Pulmonary Embolism (PE) is considered the leading cause of death disease, with a death toll of 180,000 per year in the US alone. It appears due to a blood clot in pulmonary arteries, which blocks the blood supply to the lungs or a part of the lung. An early diagnosis and treatment of PE could reduce the mortality rate. Doctors and radiologists prefer Computed Tomography (CT) scans as a first-hand tool, which contain 200 to 300 images of a single study for diagnosis. Most of the time, it becomes difficult for a doctor and radiologist to maintain concentration going through all the scans and giving the correct diagnosis, resulting in a misdiagnosis or false diagnosis. Given this, there is a need for an automatic Computer-Aided Diagnosis (CAD) system to assist doctors and radiologists in decision-making. To develop such a system, in this paper, we proposed a deep learning framework based on DenseNet201 to classify PE into nine classes in CT scans. We utilized DenseNet201 as a feature extractor and customized fully connected decision-making layers. The model was trained on the Radiological Society of North America (RSNA)-Pulmonary Embolism Detection Challenge (2020) Kaggle dataset and achieved promising results of 88%, 88%, 89%, and 90% in terms of the accuracy, sensitivity, specificity, and Area Under the Curve (AUC), respectively.
Background and Objective: The unprecedented spread of infectious diseases, such as the COVID-19 pandemic, in psychiatric units has affected the self-efficacy, burnout, and job performances of psychiatric nurses. We conducted a survey to investigate the moderating effect of burnout on the relationship between the self-efficacy and job performances of psychiatric nurses. Materials and Methods: Validated and structured questionnaires were used to collect data from 186 nurses in psychiatric units for COVID-19. The data were analyzed using descriptive statistics, Pearson’s correlation coefficient, and a series of multiple linear regression analyses based on Baron and Kenny’s method using the SPSS 26.0 program. Results: Job performance was positively correlated with self-efficacy (r = 0.75, p < 0.001) but had no significant correlation with burnout (r = −0.11, p = 0.150). Self-efficacy was negatively correlated with burnout (r = −0.22, p = 0.002). Burnout among psychiatric nurses had significant moderating effects on self-efficacy and job performance (β = −0.11, p = 0.024). Conclusions: These findings indicate a need to prevent burnout and to enhance self-efficacy in psychiatric nurses to increase their job performances and serve as a basis for establishing strategies to deploy medical staff in the future.
Monkeypox disease is caused by a virus that causes lesions on the skin and has been observed on the African continent in the past years. The fatal consequences caused by virus infections after the COVID pandemic have caused fear and panic among the public. As a result of COVID reaching the pandemic dimension, the development and implementation of rapid detection methods have become important. In this context, our study aims to detect monkeypox disease in case of a possible pandemic through skin lesions with deep-learning methods in a fast and safe way. Deep-learning methods were supported with transfer learning tools and hyperparameter optimization was provided. In the CNN structure, a hybrid function learning model was developed by customizing the transfer learning model together with hyperparameters. Implemented on the custom model MobileNetV3-s, EfficientNetV2, ResNET50, Vgg19, DenseNet121, and Xception models. In our study, AUC, accuracy, recall, loss, and F1-score metrics were used for evaluation and comparison. The optimized hybrid MobileNetV3-s model achieved the best score, with an average F1-score of 0.98, AUC of 0.99, accuracy of 0.96, and recall of 0.97. In this study, convolutional neural networks were used in conjunction with optimization of hyperparameters and a customized hybrid function transfer learning model to achieve striking results when a custom CNN model was developed. The custom CNN model design we have proposed is proof of how successfully and quickly the deep learning methods can achieve results in classification and discrimination.
The Punjab region of Pakistan faced significant losses from flash flooding in 2010 and experienced a multiyear drought during 1998–2002. The current study illustrates the drought and flood conditions using the multi-satellite data products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) as well as the TRMM Multi-satellite Precipitation Analysis (TMPA) satellites with high-quality resolution in the region of Punjab during 2010–2014. To determine the drought and flood events, we used the Vegetation Temperature Condition Index (VTCI) drought monitoring approach combined with the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) to identify the warm and cold edges (WACE) in the provision of soil moisture as well as the VTCI imagery using the MODIS-Aqua data products. We assessed the 2010 flood effect on the four years (2011–2014) of drought conditions during winter wheat crop seasons. The obtained VTCI imagery and precipitation data were utilized to validate the drought and flood conditions in the year 2010 and the drought conditions in the years 2011–2014 during the winter-wheat-crop season. It is worth mentioning that over the four years (2011–2014) of the Julian day~D-041 year, the VTCI shows a stronger link with the accumulative precipitation anomaly (r = 0.77). It was found that for D-201 during the 2010 flood was the relationship was nonlinear, and in D-217, there was a negative relationship which revealed the flood timing, duration, and intensity. For D-281, a correlation (r = 0.97) was noted during fall 2010, which showed the drought and flood extreme conditions for the winter-wheat-crop season in the year 2010–2014. In regard to 2010, the Global Flood Monitoring System (GFMS) model employs the TRMM and TMPA data products to display the study region during the 2010 flood events and validate the VTCI results. This study’s spatial and temporal observations based on the observed results of the MODIS, TRMM, and TMPA satellites are in good agreement with dry and wet conditions as well as the flood runoff stream flow and flood intensity. It demonstrates the flood events with high intensity compared with the normality of flood with the complete establishment of flood events and weather extremes during the year of 2011–2014, thereby highlighting the natural hazards impacts. Our findings show that the winter wheat harvest was affected by the 2010 monsoon’s summer high rain and floods in the plain of Punjab (Pakistan).
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