Free sugar intake is associated with reduced proportion of circulating invariant natural killer T cells among women experiencing overweight and obesity

BackgroundHigher prevalence of obesity has been observed among women compared to men, which can be explained partly by the higher consumption of sweets and physical inactivity. Obesity can alter immune cell infiltration, and therefore increase the susceptibility to develop chronic inflammation and metabolic disorders. In this study, we aimed to explore the association between free sugar intake and other unhealthy lifestyle habits in relation to the proportion of circulating iNKT cells among women with healthy weight and women experiencing overweight and obesity.MethodsA cross-sectional study was conducted on 51 Saudi women > 18 years, wherein their daily free sugar intake was assessed using the validated Food Frequency Questionnaire. Data on smoking status, physical activity, and supplement use were also collected. Anthropometric data including height, weight, waist circumference were objectively measured from each participants. The proportion of circulating iNKT cells was determined using flow cytometry.ResultsSmoking, physical activity, supplement use, and weight status were not associated with proportion of circulating iNKT cells. Significant association was found between proportion of circulating iNKT cells and total free sugar intake and free sugar intake coming from solid food sources only among women experiencing overweight and obesity (Beta: -0.10: Standard Error: 0.04 [95% Confidence Interval: -0.18 to -0.01], p= 0.034) and (Beta: -0.15: Standard Error: 0.05 [95% Confidence Interval: -0.25 to -0.05], p= 0.005), respectively.ConclusionExcessive free sugar consumption may alter iNKT cells and consequently increase the risk for chronic inflammation and metabolic disorders

A Deep Learning-Based Mobile Application for Monkeypox Detection

The recent outbreak of monkeypox has raised significant concerns in the field of public health, primarily because it has quickly spread to over 40 countries outside of Africa. Detecting monkeypox in its early stages can be quite challenging because its symptoms can resemble those of chickenpox and measles. However, there is hope that potential use of computer-assisted tools may be used to identify monkeypox cases rapidly and efficiently. A promising approach involves the use of technology, specifically deep learning methods, which have proven effective in automatically detecting skin lesions when sufficient training examples are available. To improve monkeypox diagnosis through mobile applications, we have employed a particular neural network called MobileNetV2, which falls under the category of Fully Connected Convolutional Neural Networks (FCCNN). It enables us to identify suspected monkeypox cases accurately compared to classical machine learning approaches. The proposed approach was evaluated using the recall, precision, F score, and accuracy. The experimental results show that our architecture achieves an accuracy of 0.99%, a Recall of 1.0%, an F-score of 0.98%, and a Precision of 0.95%. We believe that such experimental evaluation will contribute to the medical domain and many use cases