Brain tumor is one of the most dreadful natures of cancer and caused a huge number of deaths among kids and adults from the past few years. According to WHO standard, the 700,000 humans are being with a brain tumor and around 86,000 are diagnosed since 2019. While the total number of deaths due to brain tumors is 16,830 since 2019 and the average survival rate is 35%. Therefore, automated techniques are needed to grade brain tumors precisely from MRI scans. In this work, a new deep learning-based method is proposed for microscopic brain tumor detection and tumor type classification. A 3D convolutional neural network (CNN) architecture is designed at the first step to extract brain tumor and extracted tumors are passed to a pretrained CNN model for feature extraction. The extracted features are transferred to the correlation-based selection method and as the output, the best features are selected. These selected features are validated through feed-forward neural network for final classification. Three BraTS datasets 2015, 2017, and 2018 are utilized for experiments, validation, and accomplished an accuracy of 98.32, 96.97, and 92.67%, respectively. A comparison with existing techniques shows the proposed design yields comparable accuracy.

Background:Diabetes mellitus is a metabolic disorder characterized by chronic hyperglycemia. Plant extracts and their products are being used as an alternative system of medicine for the treatment of diabetes. Aloe vera has been traditionally used to treat several diseases and it exhibits antioxidant, anti-inflammatory, and wound-healing effects. Streptozotocin (STZ)-induced Wistar diabetic rats were used in this study to understand the potential protective effect of A. vera extract on the pancreatic islets.Objective:The aim of the present study was to evaluate the A. vera extract on improvement of insulin secretion and pancreatic β-cell function by morphometric analysis of pancreatic islets in STZ-induced diabetic Wistar rats.Materials and Methods:After acclimatization, male Wistar rats, maintained as per the Committee for the Purpose of Control and Supervision of Experiments on Animals guidelines, were randomly divided into four groups of six rats each. Fasting plasma glucose and insulin levels were assessed. The effect of A. vera extract in STZ-induced diabetic rats on the pancreatic islets by morphometric analysis was evaluated.Results:Oral administration of A. vera extract (300 mg/kg) daily to diabetic rats for 3 weeks showed restoration of blood glucose levels to normal levels with a concomitant increase in insulin levels upon feeding with A. vera extract in STZ-induced diabetic rats. Morphometric analysis of pancreatic sections revealed quantitative and qualitative gain in terms of number, diameter, volume, and area of the pancreatic islets of diabetic rats treated with A. vera extract when compared to the untreated diabetic rats.Conclusion:A. vera extract exerts antidiabetic effects by improving insulin secretion and pancreatic β-cell function by restoring pancreatic islet mass in STZ-induced diabetic Wistar rats.SUMMARY Fasting plasma glucose (FPG) and insulin levels were restored to normal levels in diabetic rats treated with Aloe vera extractIslets of pancreas were qualitatively and quantitatively restored to normalcy leading to restoration of FPG and insulin levels of diabetic rats treated with Aloe vera extractMorphometric analysis of pancreatic sections revealed quantitative and qualitative gain in terms of number, diameter, volume, and area of the pancreatic islets of diabetic rats treated with Aloe vera extract when compared to the untreated diabetic rats. Abbreviations Used: A. vera, FPG: Fasting plasma glucose, STZ: Streptozotocin, BW: Body weight

A brain tumor is an uncontrolled development of brain cells in brain cancer if not detected at an early stage. Early brain tumor diagnosis plays a crucial role in treatment planning and patients' survival rate. There are distinct forms, properties, and therapies of brain tumors. Therefore, manual brain tumor detection is complicated, time-consuming, and vulnerable to error. Hence, automated computer-assisted diagnosis at high precision is currently in demand. This article presents segmentation through Unet architecture with ResNet50 as a backbone on the Figshare data set and achieved a level of 0.9504 of the intersection over union (IoU). The preprocessing and data augmentation concept were introduced to enhance the classification rate. The multi-classification of brain tumors is performed using evolutionary algorithms and reinforcement learning through transfer learning. Other deep learning methods such as ResNet50, DenseNet201, MobileNet V2, and InceptionV3 are also applied. Results thus obtained exhibited that the proposed research framework performed better than reported in state of the art. Different CNN, models applied for tumor classification such as MobileNet V2, Inception V3, ResNet50, Den-seNet201, NASNet and attained accuracy 91.8, 92.8, 92.9, 93.1, 99.6%, respectively. However, NASNet exhibited the highest accuracy.Two processes of transfer learning: freeze and fine-tune, are performed to extract significant features from MRI slices. Brain tumor multi-classification is performed using transfer learning, ResNet50-UNet, and NASNet architecture.

This study aims to provide estimates, trends and projections of vision loss burden in Pakistan from 1990 to 2025. Global Burden of Diseases, Injuries, and Risk Factors Study (GBD 2017) was used to observe the vision loss burden in terms of prevalence and Years Lived with Disability (YLDs). As of 2017, out of 207.7 million people in Pakistan, an estimated 1.12 million (95% Uncertainty Interval [UI] 1.07–1.19) were blind (Visual Acuity [VA] <3/60), 1.09 million [0.93–1.24] people had severe vision loss (3/60≤VA<6/60) and 6.79 million [6.00–7.74] people had moderate vision loss (6/60≤VA<6/18). Presbyopia was found to be the most common ocular condition that affected an estimated 12.64 million [11.94–13.41] people (crude prevalence 6.08% [5.75–6.45]; 61% female). In terms of age-standardized YLDs rate, Pakistan is ranked fourth among other South Asian countries and twenty-first among other 42 low-middle income countries (classified by World Bank), with 552.98 YLDs [392.98–752.95] per 100,000. Compared with 1990, all-age YLDs count of blindness and vision impairment increased by 55% in 2017, which is the tenth highest increase among major health loss causes (such as dietary iron deficiency, headache disorders, low back pain etc.) in Pakistan. Moreover, our statistics show an increase in vision loss burden by 2025 for which Pakistan needs to make more efforts to encounter the growing burden of eye diseases.

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The objective of this study was to examine the impact of workplace bullying on self-esteem, including the mediating effect of internalized stigma and the moderating effect of spirituality, among hepatitis C virus patients. Data were collected from 228 employed hepatitis C virus patients who had been admitted to Gastroenterology and Hepatology wards in Pakistani hospitals. We found support for the hypothesis that workplace bullying is associated with low self-esteem via internalized stigma. In addition, spirituality moderated the association such that participants with greater spirituality were buffered from the impact of stigma on self-esteem.

Nowadays food borne illness is most common in people due to their epidemic nature. These diseases affect the human digestive system through bacteria, viruses and parasites. The agents of illness are transmitted in our body through various types of food items, water and uncooked. Pathogens show drastic changes in immunosuppressant people. This review gives general insights to harmful microbial life. Pakistan is a developed country and because of its improper food management, a lot of gastrointestinal problems are noted in many patients. Bacteria are most common agents to spread diarrhoea, villi infection, constipation and dysenteric disease in human and induce the rejection of organ transplant. Enhancement of their lifestyle, properly cooked food should be used and to overcome the outbreak of the diseases.

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