Jabir Khan

Chronic non-communicable diseases are the major cause of death globally. Whole grains are recommended in dietary guidelines worldwide due to increasing evidence that their consumption can improve health beyond just providing energy and nutrients. Epidemiological studies have suggested that the incorporation of whole grains, as part of a healthy diet, plays a key role in reducing one’s risk for cardiovascular diseases (CVDs), obesity, type 2 diabetes (T2D) and cancer. Phenolic acids and dietary fibre are important components found in whole grains that are largely responsible for these health advantages. Both phenolic acids and dietary fibre, which are predominantly present in the bran layer, are abundant in whole-grain cereals and pseudo-cereals. Several studies indicate that whole grain dietary fibre and phenolic acids are linked to health regulation. The main focus of this study is two-fold. First, we provide an overview of phenolic acids and dietary fibres found in whole grains (wheat, barley, oats, rice and buckwheat). Second, we review existing literature on the linkages between the consumption of whole grains and the development of the following chronic non-communicable diseases: CVDs, obesity, T2D and cancer. Altogether, scientific evidence that the intake of whole grains reduces the risk of certain chronic non-communicable disease is encouraging but not convincing. Based on previous studies, the current review encourages further research to cover the gap between the emerging science of whole grains and human health.

Strategies to screen antihypertensive peptides with high throughput and rapid speed will doubtlessly contribute to the treatment of hypertension. Food-derived antihypertensive peptides can reduce blood pressure without side effects. In the present study, a novel model based on the eXtreme Gradient Boosting (XGBoost) algorithm was developed and compared with the dominating machine learning models. To further reflect on the reliability of the method in a real situation, the optimized XGBoost model was utilized to predict the antihypertensive degree of the k-mer peptides cutting from six key proteins in bovine milk, and the peptide–protein docking technology was introduced to verify the findings. The results showed that the XGBoost model achieved outstanding performance, with an accuracy of 86.50% and area under the receiver operating characteristic curve of 94.11%, which were better than the other models. Using the XGBoost model, the prediction of antihypertensive peptides derived from milk protein was consistent with the peptide–protein docking results, and was more efficient. Our results indicate that using the XGBoost algorithm as a novel auxiliary tool is feasible to screen for antihypertensive peptides derived from food, with high throughput and high efficiency.