ABSTRACT
Insufficient calcium intake during growth is a global public health concern. The aim of this study was to investigate the effects of dietary menaquinone-7 (MK-7) on bone accrual in growing Sprague-Dawley rats under calcium restriction. Following 13 weeks of treatment, various bone quality parameters, including microarchitecture, were measured. Fecal and cecal samples were subjected to microbiome (16S rRNA gene sequencing) analyses, while metabolomics analysis of the cecum and humerus samples was analyzed based on UHPLC-Q/TOF-MS. We found that calcium deficiency diminished the richness of the microbiome and disrupted microbiome composition, accompanied by an elevation in the relative abundance of Parasutterella. Furthermore, calcium insufficiency escalated the level of isovaleric acid and modified the metabolic profiles. MK-7 supplementation significantly increased the cortical thickness, cortical bone area, and the calcium content of the femur. Apart from improving bone calcium deposition and diminishing bone resorption, the mechanisms underlying the beneficial effects of MK on bone quality also involve the modulation of the host's metabolic pathways and the composition of gut microbiota. The gut-bone axis holds promise as an efficacious target for ameliorating calcium deficiency in children's bone quality, and MK-7 is a promising dietary supplement from this perspective.
ABSTRACT
This study aims to explore the future development path of the college health education and health education's impact on students' sports exercise. Specifically, artificial intelligence (AI) algorithm is combined with intelligent robotics technology to acquire and analyze students' sports exercise behaviors. As a result, a new development model is formulated for college health education. First, it explores students' sports exercise and health education situation in Chinese higher institutions and uncovers the underlying problems. Then it puts forward the corresponding modification suggestions. Second, the AI algorithm and the Kinect sensor-mounted intelligent robot capture the human skeleton features to obtain smooth skeleton joint points data. At the same time, a visual perception human motion recognition (HMR) algorithm is established based on the Hidden Markov Model (HMM). Afterward, the proposed HMM-based HMR algorithm is used to recognize students' sports exercise motions by analyzing human motion skeleton images. The experimental outcomes suggest that the maximum reconstruction error of the HMR algorithm is 10 mm, and the compression ratio is between 5 and 10; the HMR rate is more than 96%. Compared with similar algorithms, the proposed visual perception HMR algorithm depends less on the number of training samples. It can achieve a high recognition rate given only a relatively few samples. Therefore, the proposed (AI + intelligent robot)-enabled HMM-based HMR algorithm can effectively identify the behavior characteristics of students in sports exercise. This study can provide a reference for exploring college students' health education development path.
