Phytochemical composition, anti-microbial, anti-oxidant and anti-diabetic effects of Solanum elaeagnifolium Cav. leaves: in vitro and in silico assessments

The aim of this study was to screen the chemical components of Solanum elaeagnifolium leaves and assess their therapeutic attributes with regard to their antioxidant, antibacterial, and antidiabetic activities. The antidiabetic effects were explored to determine the α-amylase and α-glucosidase inhibitory potential of the leaf extract. To identify the active antidiabetic drugs from the extracts, the GC-MS-screened molecules were docked with diabetes-related proteins using the glide module in the Schrodinger Tool. In addition, molecular dynamics (MD) simulations were performed for 100 ns to evaluate the binding stability of the docked complex using the Desmond module. The ethyl acetate had a significant total phenolic content (TPC), with a value of 79.04 ± 0.98 mg/g GAE. The ethanol extract was tested for its minimum inhibitory concentration (MIC) for its bacteriostatic properties. It suppressed the growth of B. subtilis, E. coli, P. vulgaris, R. equi and S. epidermis at a dosage of 118.75 µg/mL. Moreover, the IC50 values of the ethanol extract were determined to be 17.78 ± 2.38 in the α-amylase and and 27.90 ± 5.02 µg/mL in α-glucosidase. The in-silico investigation revealed that cyclolaudenol achieved docking scores of -7.94 kcal/mol for α-amylase. Likewise, the α-tocopherol achieved the docking scores of -7.41 kcal/mol for glycogen phosphorylase B and -7.21 kcal/mol for phosphorylase kinase. In the MD simulations, the cyclolaudenol and α-tocopherol complexes exhibited consistently stable affinities with diabetic proteins throughout the trajectory. Based on these findings, we conclude that this plant could be a good source for the development of novel antioxidant, antibacterial, and antidiabetic agents.Communicated by Ramaswamy H. Sarma.

Oropharyngeal Exercises on Snoring, Daytime Sleepiness and Anthropometric Measurement Among Adults with Obstructive Sleep Apnea

Introduction: Obstructive sleep apnea (OSA) is the most common sleep-related breathing disorder. Untreated OSA is associated with potential long-term adverse health consequences. Oropharyngeal exercise is one of the treatment modalities to manage the symptoms of OSA. Hence, the current study was conducted to evaluate the effectiveness of oropharyngeal exercises on snoring, daytime sleepiness, and the selected anthropometric measurement among adults with obstructive sleep apnea. Methods: Quasi-experimental research design was adopted to conduct the study with 60 samples that met the inclusion criteria at Saveetha Medical College and Hospital. Samples were selected by convenience sampling technique and were assigned to the experimental group (n=30) and the control group (n=30). Samples were determined using the Berlin questionnaire, and the pre-test assessment was done by using the snoring sleepiness scale (SSS), Epworth sleepiness scale (ESS), and anthropometric measurements for both groups. The experimental group received the oropharyngeal exercise twice daily for 15 days. The control group received the day-to-day practices. The post-test assessment was done using the same tool at the end of 15 days for both groups. Results: There was a statistically significant change in snoring, daytime sleepiness, BMI, and neck and waist circumference between pre and post-intervention within the experimental group and post-intervention between the experimental and control group at the level of P

Classification of Fundus Images Using Convolutional Neural Networks

Diabetes causes damage to the retinal blood vessel networks, resulting in Diabetic Retinopathy (DR). This is a serious vision-threatening condition for most diabetics. Color fundus photographs are utilized to diagnose DR, which necessitates the employment of qualified clinicians to detect the presence of lesions. It is difficult to identify DR in an automated method. Feature extraction is quite important in terms of automated sickness detection. Convolutional Neural Network (CNN) exceeds previous handcrafted feature-based image classification algorithms in terms of picture classification efficiency in the current environment. In order to improve classification accuracy, this work presents the CNN structure for extracting attributes from retinal fundus images. The output properties of CNN are given as input to different machine learning classifiers in this recommended strategy. This approach is evaluating using pictures from the EYEPACS datasets using Decision stump, J48 and Random Forest classifiers. To determine the effectiveness of a classifier, its accuracy, false positive rate (FPR), True positive Rate (TPR), precision, recall, F-measure, and Kappa-score are illustrated. The recommended feature extraction strategy paired with the Random forest classifier outperforms all other classifiers on the EYEPACS datasets, with average accuracy and Kappa-score (k-score) of 99% and 0.98 respectively.

Brain Tumor Detection with Biologically Inspired Watershed Segmentation and Classification Based on Feed-Forward Neural Network (FNN)

Image segmentation plays a very vital role in gathering information by dividing the images into various segments to achieve the meaningful information, whereas the image segmentation gives importance in the area of medical imaging to analyze and process the anatomical structures of various internal organs of the body with high resolution images that are captured during medical examination. Medical experts will go through the reports which give the various reasons for the existence of the disease. Brain which is considered the important part of the body so the detection and the segmentation of brain tumors will be considered as the major task of the medical field whereas they are using the high resolution images in the form of MRI reports. The MRI images are considered as the vital source for the identification of tumors in the brain. The accuracy of the segmentation and identification of the tumor depends upon the experience of the radiologist and also it is time consuming task. Therefore the watershed segmentation is performed for the extraction of the tumor region and the features are extracted for the classification, whereas the classification is carried out by the Feed-Forward Neural Network (FNN). The experimental results are evaluated based on the performance and the quality analysis, Furthermore the results give the accuracy of 91.2% in the training model and 71.8% as the testing during the classification process.

ECM-CSD: An Efficient Classification Model for Cancer Stage Diagnosis in CT Lung Images Using FCM and SVM Techniques

As is eminent, lung cancer is one of the death frightening syndromes among people in present cases. The earlier diagnosis and treatment of lung cancer can increase the endurance rate of the affected people. But, the structure of the cancer cell makes the diagnosis process more challenging, in which the most of the cells are superimposed. By adopting the efficient image processing techniques, the diagnosis process can be made effective, earlier and accurate, where the time aspect is extremely decisive. With those considerations, the main objective of this work is to propose a region based Fuzzy C-Means Clustering (FCM) technique for segmenting the lung cancer region and the Support Vector Machine (SVM) based classification for diagnosing the cancer stage, which helps in clinical practice in significant way to increase the morality rate. Moreover, the proposed ECM-CSD (Efficient Classification Model for Cancer Stage Diagnosis) uses Computed Tomography (CT) lung images for processing, since it poses higher imaging sensitivity, resolution with good isotopic acquisition in lung nodule identification. With those images, the pre-processing has been made with Gaussian Filter for smoothing and Gabor Filter for enhancement. Following, based on the extracted image features, the effective segmentation of lung nodules is performed using the FCM based clustering. And, the stages of cancer are identified based on the SVM classification technique. Further, the model is analyzed with MATLAB tool by incorporating the LIDC-IDRI lung CT images clinical dataset. The comparative experiments show the efficiency of the proposed model in terms of the performance evaluation factors like increased accuracy and reduced error rate.

Genetics of otosclerosis

Otosclerosis is an early-middle adult life genetic disease affecting bone remodelling in the ear. Current knowledge of otosclerosis as an inherited disease dates to the mid-19th century, and we report here an attempt to understand the genetics of otosclerosis and detect its heterogeneity. The analysis was conducted on 151 otosclerotic families. The results of our study indicate that while heredity plays an important role in the manifestation of the disease a substantial portion of otosclerotic cases could arise due to non-genetic causes.