Osteoporosis commonly occurs in the older people and severe patients, with the main reason of the imbalance of bone metabolism (the rate of bone resorption exceeding the rate of bone formation), resulting in a decrease in bone mineral density and destruction of bone microstructure and further leading to the increased risk of fragility fracture. Recent studies indicate that protein nutritional support is beneficial for attenuating osteoporosis and improving bone health. This review summarized the classical mechanisms of protein intervention for alleviating osteoporosis on both suppressing bone resorption and regulating bone formation related pathways (promoting osteoblasts generation and proliferation, enhancing calcium absorption, and increasing collagen and mineral deposition), as well as the potential novel mechanisms via activating autophagy of osteoblasts, altering bone related miRNA profiles, regulating muscle-bone axis, and modulating gut microbiota abundance. Protein nutritional intervention is expected to provide novel approaches for the prevention and adjuvant therapy of osteoporosis. GRAPHICAL ABTSRACT

With increasing research, the nutritional value of oats is gaining recognition. Fermentation is an emerging biotransformation pattern that changes the nutritional structure of whole grains. Currently, research on whole-grain fermentation is relatively focused on phenolic compounds and their antioxidants, but less attention has been given to avenanthramides (Avns) and the glycemic index (GI) in fermented oats. In this study, oats were subjected to solid-state fermentation (SSF) by Monascus purpureus for 21 days, and samples were taken at different time points. Changes in the contents of nutrients, phytochemicals, and antinutritional factors were analyzed using one-way ANOVA. Additionally, a simulated in vitro digestion experiment was conducted to assess the estimated glycemic index (eGI) of SSF oats. The results revealed that the nutritional structure of oats was changed by SSF, and the Avns content significantly increased, with 3.2 times more Avns in SSF oats than in unfermented oats, including 3.05, 3.09, 3.09, and 3.7 times more Avn A, Avn B, Avn C, and Avn D, respectively, and the eGI was reduced from 40.22 to 39.97 with increasing fermentation time. In general, SSF with Monascus purpureus has improved nutritional value, significantly increased the content of active ingredients, and reduced the level of eGI and two antinutritional factors (phytate and oxalate), which provides an effective way to improve the nutritional and digestive characteristics of oats.

Rheumatoid arthritis (RA) is a systemic autoimmune disease that commonly causes pain in joints and the progressive destruction of cartilage and bone, which significantly reduces the quality of life and increases the social burden. However, there is still no cure for RA, so it is highly important to explore additional adjuvant treatment methods. Studies have indicated that malnutrition, changes in intestinal microbiota, and changes in immune status caused by dietary imbalance are directly related to the onset of RA, indicating that dietary intervention may offer a simple, economical, and practical avenue to relieve RA. Therefore, in this review, we discuss the pathogenesis of RA and summarize the influence of different dietary patterns on RA. In particular, we pointed out that high-fat, high-sugar, and high-salt diets contribute to RA progression, whereas the Mediterranean diet (MD) is beneficial for preventing RA. Furthermore, the ingredients of food, such as dietary fiber, probiotics, and vitamins, help reduce the level of inflammation and relieve joint pain, which may play critical roles in the treatment of RA. Therefore, dietary intervention provides a potential effective approach for adjuvant therapy of RA.

Yu Y, et al. Nutr Res Pract. 2022 Oct;16(5):589-603. https://doi.org/10.4162/nrp.2022.16.5.589

Compound–target interaction (CTI) prediction plays a critical role in drug discovery and the functional study of food-derived bioactive compounds. However, traditional experimental methods for CTI identification are limited by high costs, long cycle times, and high false-positive rates, highlighting an urgent need for more efficient approaches. Machine learning (ML) has become a revolutionary tool to address these challenges. In this review, we focus on recent developments in ML-based CTI prediction. We first systematically outline the commonly used public databases and feature extraction methods for both compounds (molecular fingerprints) and proteins (sequence-derived features), followed by elaborating on four types of ML approaches, including classical supervised learning, matrix factorization, graph topology-based inference, and deep neural network frameworks. In particular, this review explores the emerging application of these computational approaches in identifying targets of food-derived bioactive compounds, underscoring its significant potential to advance functional food research. Moreover, we analyze key challenges, such as limited model interpretability, high data dependency, and insufficient multi-source information integration, and put forth future prospects to improve the prediction of food-derived CTIs, thereby facilitating their application in functional food research.