Management of Hypertension and Diabetes in Obesity: Non-Pharmacological Measures

Obesity has become a global epidemic over the past few decades because of unhealthy dietary habits and reduced physical activity. Hypertension and diabetes are quite common among obese individuals and there is a linear relationship between the degree of obesity and these diseases. Lifestyle interventions like dietary modifications and regular exercise are still important and safe first-line measures for treatment. Recently, bariatric surgery has emerged as an important and very effective treatment option for obese individuals especially in those with comorbidities like hypertension and diabetes. Though there are few effective drugs for the management of obesity, their efficacy is only modest, and they should always be combined with lifestyle interventions for optimal benefit. In this paper we aim to outline the non-pharmacological measures for the management of hypertension and diabetes in obesity

Multidrug and extensively drug-resistant tuberculosis from a general practice perspective

Despite intensive efforts to eradicate the disease, tuberculosis continues to be a major threat to Indian society, with an estimated prevalence of 3.45 million cases in 2006. Emergence of multidrug-resistant tuberculosis has complicated eradication attempts in recent years. Incomplete and/inadequate treatment are the main causes for development of drug resistance. Directly observed therapy, short-course (DOTS) is the World Health Organization (WHO) strategy for worldwide eradication of tuberculosis, and our country achieved 100% coverage for DOTS through the Revised National Tuberculosis Control Program in 2006. For patients with multidrug-resistant tuberculosis, the WHO recommends a DOTS-Plus treatment strategy. Early detection and prompt treatment of multidrug-resistant tuberculosis is crucial to avoid spread of the disease and also because of the chances of development of potentially incurable extensively drug-resistant tuberculosis in these cases. This review discusses the epidemiologic, diagnostic, and therapeutic aspects of multidrug-resistant tuberculosis, and also outlines the role of primary care doctors in the management of this dangerous disease

A Cloud-based Deep Learning Framework for Remote Detection of Diabetic Foot Ulcers

This research proposes a mobile and cloud-based framework for the automatic detection of diabetic foot ulcers and conducts an investigation of its performance. The system uses a cross-platform mobile framework which enables the deployment of mobile apps to multiple platforms using a single TypeScript code base. A deep convolutional neural network was deployed to a cloud-based platform where the mobile app could send photographs of patient's feet for inference to detect the presence of diabetic foot ulcers. The functionality and usability of the system were tested in two clinical settings: Salford Royal NHS Foundation Trust and Lancashire Teaching Hospitals NHS Foundation Trust. The benefits of the system, such as the potential use of the app by patients to identify and monitor their condition are discussed

Diabetic foot ulcers segmentation challenge report: benchmark and analysis

Monitoring the healing progress of diabetic foot ulcers is a challenging process. Accurate segmentation of foot ulcers can help podiatrists to quantitatively measure the size of wound regions to assist prediction of healing status. The main challenge in this field is the lack of publicly available manual delineation, which can be time consuming and laborious. Recently, methods based on deep learning have shown excellent results in automatic segmentation of medical images, however, they require large-scale datasets for training, and there is limited consensus on which methods perform the best. The 2022 Diabetic Foot Ulcers segmentation challenge was held in conjunction with the 2022 International Conference on Medical Image Computing and Computer Assisted Intervention, which sought to address these issues and stimulate progress in this research domain. A training set of 2000 images exhibiting diabetic foot ulcers was released with corresponding segmentation ground truth masks. Of the 72 (approved) requests from 47 countries, 26 teams used this data to develop fully automated systems to predict the true segmentation masks on a test set of 2000 images, with the corresponding ground truth segmentation masks kept private. Predictions from participating teams were scored and ranked according to their average Dice similarity coefficient of the ground truth masks and prediction masks. The winning team achieved a Dice of 0.7287 for diabetic foot ulcer segmentation. This challenge has now entered a live leaderboard stage where it serves as a challenging benchmark for diabetic foot ulcer segmentation

Deep learning in diabetic foot ulcers detection: A comprehensive evaluation

There has been a substantial amount of research involving computer methods and technology for the detection and recognition of diabetic foot ulcers (DFUs), but there is a lack of systematic comparisons of state-of-the-art deep learning object detection frameworks applied to this problem. DFUC2020 provided participants with a comprehensive dataset consisting of 2,000 images for training and 2,000 images for testing. This paper summarizes the results of DFUC2020 by comparing the deep learning-based algorithms proposed by the winning teams: Faster R–CNN, three variants of Faster R–CNN and an ensemble method; YOLOv3; YOLOv5; EfficientDet; and a new Cascade Attention Network. For each deep learning method, we provide a detailed description of model architecture, parameter settings for training and additional stages including pre-processing, data augmentation and post-processing. We provide a comprehensive evaluation for each method. All the methods required a data augmentation stage to increase the number of images available for training and a post-processing stage to remove false positives. The best performance was obtained from Deformable Convolution, a variant of Faster R–CNN, with a mean average precision (mAP) of 0.6940 and an F1-Score of 0.7434. Finally, we demonstrate that the ensemble method based on different deep learning methods can enhance the F1-Score but not the mAP

Amiodarone-Induced Thyroid Dysfunction: A Clinical Update.

Amiodarone is one of the most commonly prescribed antiarrhythmic agents in clinical practice owing to its efficacy, even with high toxicity profile. The high iodine content and the prolonged biological half-life of the drug can result in thyroid dysfunction in a high proportion of patients treated with amiodarone even after cessation of amiodarone. Both hypothyroidism and hyperthyroidism are common side effects that mandate regular monitoring of patients with thyroid function tests. Amiodarone-induced hypothyroidism (AIH) is diagnosed and managed in the same way as a usual case of hypothyroidism. However, differential diagnosis and clinical management of amiodarone-induced thyrotoxicosis (AIT) subtypes can be challenging. With the aid of a case snippet, we update the current evidence for the diagnostic work up and management of patients with amiodarone-induced thyroid dysfunction in this article