Background: Vitamin-D deficiency is a pandemic that is being linked to various noncommunicable diseases. The present study is an attempt to study the demographic profile and the prevalence of comorbidities in association with the vitamin-D status of the Mumbai-based study population. The authors also attempt to understand the change in prevalence over the last decade Methodology: Fasting blood samples were collected from consenting asymptomatic adults visiting the hospital and were analyzed for the prevalence of vitamin-D deficiency and diabetes mellitus, and participants were clinically examined for the presence of hypertension (as defined by AHA guidelines) and obesity (as defined by body mass index of more than equal to 30) Results: It was found that 57% of participants were deficient, 25% had insufficient, and 18% had adequate vitamin-D levels. There were a greater number of younger ( P = 0.003) and upper-middle-class participants in the deficient group ( P = 0.043816). Prevalence of obesity, hypertension, and diabetes mellitus and the distribution of genders was comparable in the deficient and sufficient vitamin-D groups. However, diabetic vitamin-D-sufficient participants had better control of blood sugar compared to diabetic vitamin-D-deficient participants Conclusion: Although the prevalence of vitamin-D deficiency has slightly reduced compared to the previous decade, it is still highly prevalent. Diabetic vitamin-D-sufficient participants had better glycemic control compared to diabetic vitamin-D-deficient participants. Thus, it is highly recommended for primary care physicians to screen everyone for vitamin-D deficiency.

Primary diagnosis of brain tumors is crucial to improve treatment outcomes for patient survival. T1-weighted contrast-enhanced images of Magnetic Resonance Imaging (MRI) provide the most anatomically relevant images. But even with many advancements, day by day in the medical field, assessing tumor shape, size, segmentation, and classification is very difficult as manual segmentation of MRI images with high precision and accuracy is indeed a time-consuming and very challenging task. So newer digital methods like deep learning algorithms are used for tumor diagnosis which may lead to far better results. Deep learning algorithms have significantly upgraded the research in the artificial intelligence field and help in better understanding medical images and their further analysis. The work carried out in this paper presents a fully automatic brain tumor segmentation and classification model with encoder-decoder architecture that is an improvisation of traditional UNet architecture achieved by embedding three variants of ResNet like ResNet 50, ResNet 101, and ResNext 50 with proper hyperparameter tuning. Various data augmentation techniques were used to improve the model performance. The overall performance of the model was tested on a publicly available MRI image dataset containing three common types of tumors. The proposed model performed better in comparison to several other deep learning architectures regarding quality parameters including Dice Similarity Coefficient (DSC) and Mean Intersection over Union (Mean IoU) thereby enhancing the tumor analysis.

No abstract

No abstract

No abstract