A Study of Strategies and Methods for the Application of Sports Nutrition in Fitness Training

Dietary habits are particularly important in fitness training, and a scientific fitness diet needs to be formulated according to the fitness user’s situation, fitness goals and exercise volume. In this paper, a multimodal personalized sports nutrition recommendation model incorporating users’ visual preferences is designed to address the important impact of visual features on the task of sports nutrition recommendation. The user’s visual preference is modeled using the Query-Key-Value attention mechanism, which extracts valuable visual information from their historical data and adds it to textual features. In the sports nutrition program generation part, guided by sports nutrition theory and based on the MMFV model, the sports nutrition program generation method was designed. Then, the multi-objective optimization problem of the sports nutrition scheme is planned and modeled, and a calorie-checking mechanism is added to the multi-objective particle swarm algorithm for the problem of calorie intake not meeting the fitness goal. According to the sports nutrition program recommendation algorithm to improve the three meals diets of the gym participants, through the analysis of relevant data to verify the scientificity of the recommended program. The average intake of protein, vitamin A, vitamin B1 and vitamin C of the fitness participants after the modification increased by 7.4%, 17.77%, 22.33% and 8.46%, respectively, compared with that before the modification. Scapular sebaceous thickness and abdominal sebaceous thickness were reduced by 2.2 mm and 1.47 mm, respectively, compared with the pre-modification period.TC, TG and LDL metabolism were in the normal range, which was significantly different from the pre-modification period (P<0.05)

Silver Doped Mesoporous Silica Nanoparticles Based Electrochemical Enzyme-Less Sensor for Determination of H₂O₂ Released from Live Cells

In this study, a silver doped mesoporous silica nanoparticles-based enzyme-less electrochemical sensor for the determination of hydrogen peroxide (H2O2) released from live cells was constructed for the first time. The presented electrochemical sensor exhibited fast response (2 s) towards the reduction of H2O2 concentration variation at an optimized potential of −0.5 V with high selectivity over biological interferents such as uric acid, ascorbic acid, and glucose. In addition, a wide linear range (4 μM to 10 mM) with a low detection limit (LOD) of 3 μM was obtained. Furthermore, the Ag-mSiO2 nanoparticles/glass carbon electrode (Ag-mSiO2 NPs/GCE) based enzyme-less sensor showed good electrocatalytic performance, as well as good reproducibility, and long-term stability, which provided a successful way to in situ determine H2O2 released from live cells. It may also be promising to monitor the effect of reactive oxygen species (ROS) production in bacteria against oxidants and antibiotics

Comparison on the Surface Structure Properties along with Fe(II) and Mn(II) Removal Characteristics of Rice Husk Ash, Inactive Saccharomyces cerevisiae Powder, and Rice Husk

This study selected solid wastes, such as rice husk ash (RHA), inactive Saccharomyces cerevisiae powder (ISP), and rice husk (RH), as the potential adsorbents for the removal of Fe(II) and Mn(II) in aqueous solution. The structural characteristics, functional groups, and elemental compositions were determined by scanning electron microscope (SEM) and Fourier translation infrared spectrum (FT-IR) analyses, respectively. Then the influence on the Fe(II) and Mn(II) removing efficiency by the factors, such as pH, adsorbent dosage, initial Fe(II) and Mn(II) concentration, and contact time, was investigated by the static batch test. The adsorption isotherm study results show that Langmuir equation can better fit the Fe(II) and Mn(II) adsorption process by the three adsorbents. The maximum adsorption amounts for Fe(II) were 6.211 mg/g, 4.464 mg/g, and 4.049 mg/g by RHA, ISP, and RH and for Mn(II) were 3.016 mg/g, 2.229 mg/g, and 1.889 mg/g, respectively. The adsorption kinetics results show that the pseudo-second-order kinetic model can better fit the Fe(II) and Mn(II) adsorption process. D-R model and thermodynamic parameters hint that the adsorption processes of Fe(II) and Mn(II) on the three adsorbents took place physically and the processes were feasible, spontaneous, and exothermic

Circulating small extracellular vesicles microRNAs plus CA-125 for treatment stratification in advanced ovarian cancer

Abstract Background No residual disease (R0 resection) after debulking surgery is the most critical independent prognostic factor for advanced ovarian cancer (AOC). There is an unmet clinical need for selecting primary or interval debulking surgery in AOC patients using existing prediction models. Methods RNA sequencing of circulating small extracellular vesicles (sEVs) was used to discover the differential expression microRNAs (DEMs) profile between any residual disease (R0, n = 17) and no residual disease (non-R0, n = 20) in AOC patients. We further analyzed plasma samples of AOC patients collected before surgery or neoadjuvant chemotherapy via TaqMan qRT-PCR. The combined risk model of residual disease was developed by logistic regression analysis based on the discovery-validation sets. Results Using a comprehensive plasma small extracellular vesicles (sEVs) microRNAs (miRNAs) profile in AOC, we identified and optimized a risk prediction model consisting of plasma sEVs-derived 4-miRNA and CA-125 with better performance in predicting R0 resection. Based on 360 clinical human samples, this model was constructed using least absolute shrinkage and selection operator (LASSO) and logistic regression analysis, and it has favorable calibration and discrimination ability (AUC:0.903; sensitivity:0.897; specificity:0.910; PPV:0.926; NPV:0.871). The quantitative evaluation of Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI) suggested that the additional predictive power of the combined model was significantly improved contrasted with CA-125 or 4-miRNA alone (NRI = 0.471, IDI = 0.538, p < 0.001; NRI = 0.122, IDI = 0.185, p < 0.01). Conclusion Overall, we established a reliable, non-invasive, and objective detection method composed of circulating tumor-derived sEVs 4-miRNA plus CA-125 to preoperatively anticipate the high-risk AOC patients of residual disease to optimize clinical therapy