Angelicin improves osteoporosis in ovariectomized rats by reducing ROS production in osteoclasts through regulation of the KAT6A/Nrf2 signalling pathway.

BACKGROUND: Angelicin, which is found in Psoralea, can help prevent osteoporosis by stopping osteoclast formation, although the precise mechanism remains unclear. METHODS: We evaluated the effect of angelicin on the oxidative stress level of osteoclasts using ovariectomized osteoporosis model rats and RAW264.7 cells. Changes in the bone mass of the femur were investigated using H&E staining and micro-CT. ROS content was investigated by DHE fluorescence labelling. Osteoclast-related genes and proteins were examined for expression using Western blotting, immunohistochemistry, tartrate-resistant acid phosphatase staining, and real-time quantitative PCR. The influence of angelicin on osteoclast development was also evaluated using the MTT assay, double luciferin assay, chromatin immunoprecipitation, immunoprecipitation and KAT6A siRNA transfection. RESULTS: Rats treated with angelicin had considerably higher bone mineral density and fewer osteoclasts. Angelicin prevented RAW264.7 cells from differentiating into osteoclasts in vitro when stimulated by RANKL. Experiments revealed reduced ROS levels and significantly upregulated intracellular KAT6A, HO-1, and Nrf2 following angelicin treatment. The expression of genes unique to osteoclasts, such as MMP9 and NFATc1, was also downregulated. Finally, KAT6A siRNA transfection increased intracellular ROS levels while decreasing KAT6A, Nrf2, and HO-1 protein expression in osteoclasts. However, in the absence of KAT6A siRNA transfection, angelicin greatly counteracted this effect in osteoclasts. CONCLUSIONS: Angelicin increased the expression of KAT6A. This enhanced KAT6A expression helps to activate the Nrf2/HO-1 antioxidant stress system and decrease ROS levels in osteoclasts, thus inhibiting oxidative stress levels and osteoclast formation.

Regulation of short-chain fatty acids in the immune system.

A growing body of research suggests that short-chain fatty acids (SCFAs), metabolites produced by intestinal symbiotic bacteria that ferment dietary fibers (DFs), play a crucial role in the health status of symbiotes. SCFAs act on a variety of cell types to regulate important biological processes, including host metabolism, intestinal function, and immune function. SCFAs also affect the function and fate of immune cells. This finding provides a new concept in immune metabolism and a better understanding of the regulatory role of SCFAs in the immune system, which impacts the prevention and treatment of disease. The mechanism by which SCFAs induce or regulate the immune response is becoming increasingly clear. This review summarizes the different mechanisms through which SCFAs act in cells. According to the latest research, the regulatory role of SCFAs in the innate immune system, including in NLRP3 inflammasomes, receptors of TLR family members, neutrophils, macrophages, natural killer cells, eosinophils, basophils and innate lymphocyte subsets, is emphasized. The regulatory role of SCFAs in the adaptive immune system, including in T-cell subsets, B cells, and plasma cells, is also highlighted. In addition, we discuss the role that SCFAs play in regulating allergic airway inflammation, colitis, and osteoporosis by influencing the immune system. These findings provide evidence for determining treatment options based on metabolic regulation.

[Qualitative and quantitative detection of minced pork quality by near infrared reflectance spectroscopy].

The present study is concerning qualitative and quantitative detection of minced pork quality based on FT-near infrared (FT-NIR) spectroscopy and achieving the rapid approach to detecting the minced pork quality. Firstly, FT-NIR spectroscopy combined with partial least squares (PLS) and least squares-support vector machine (LS-SVM) was used for minced pork quality prediction including discrimination of the different muscle type of pig and quantitative detection of the fat, protein and moisture content of pork. The result indicated that 100% recognition ratio for calibration and 96% recognition ratio for validation were achieved by PLSDA for 4 different muscle types of pig. These two methods for chemical composition detection both have good performances in predicting fat and moisture content, the correlation coefficient for calibration and validation was all more than 0.9, but the models for protein content prediction were of less well performances, the correlation coefficients for calibration and validation, RMSEC, RMSEP and RMSECV respectively were 0.722, 0.593, 1.595, 1.550 and 1.888, respectively. The LS-SVM method is more accurate in predicting each quality index than the PLSR method. The result shows that the prediction models for fat and moisture content based on LS-SVM have a better performance with high precision, good stability and adaptability and can be used to predict the fat and moisture content of minced pork rapidly, and provide a fast approach to discrimination of the different muscle type of pig.

[Prediction of minced pork quality attributes using visible and near infrared reflectance spectroscopy].

The objective of the present study was to estimate minced pork meat quality using visible and near infrared (Vis-NIR) spectroscopy. Two hundred twenty five carcasses samples from longissimus dorsi muscle were scanned over the Vis-NIR spectral range from 350 to 1 015 nm and analysed for intramuscular fat (IMF), protein and moisture according to the official methods. Wavelet transform was employed to eliminate the spectra noise. Partial least square regression (PLSR) and support vector machine (SVM) were used to develop Vis-NIR spectroscopy models for chemical composition detection. According to calibration statistics, the best model to predict intramuscular fat content was developed by SVM with the denoised spectra, the correlation coefficient was 0.889 for calibration and 0.888 for validation. For protein and moisture, the best model was achieved with the PLS method with the correlation coefficient of 0.869 and 0.881 for protein calibration and validation sets and 0.877 and 0.848 for moisture calibration and validation sets, respectively. And all the ratios of standard deviation of validation set to root mean square error of prediction (RPD) were not more than 3.0. Results indicated that it was possible to predict chemical composition in minced pork meat. As a fast predictor of meat quality using Vis-NIR spectroscopy, it is necessary to improve the precision and the robustness of the model for practice.

On-line prediction of fresh pork quality using visible/near-infrared reflectance spectroscopy.

Visible/near-infrared (Vis/NIR) spectroscopy was tested to predict the quality attributes of fresh pork (content of intramuscular fat, protein and water, pH and shear force value) on-line. Vis/NIR spectra (350-1100 nm) were obtained from 211 samples using a prototype. Partial least-squares regression (PLSR) models were developed by external validation with wavelet de-noising and several pre-processing methods. The 6th order Daubechies wavelet with 6 decomposition levels (db6-6) showed high de-noising ability with good information preservation. The first derivative of db6-6 de-noised spectra combined with multiplicative scatter correction yielded the prediction models with the highest coefficient of determination (R(2)) for all traits in both calibration and validation periods, which were all above 0.757 except for the prediction of shear force value. The results indicate that Vis/NIR spectroscopy is a promising technique to roughly predict the quality attributes of intact fresh pork on-line.

[Online determination of pH in fresh pork by visible/near-infrared spectroscopy].

The present research was focused on determination of the pH value online by visible and near-infrared spectroscopy. In the part of data gathering, fresh pork longissimus dorsi was moving at the constant velocity of 0.25 m x s(-1) on the conveyor belt, and the visible and near-infrared diffuse reflectance spectrum (350-1 000 nm) was captured. In the part of data processing, band of 510-980 nm of the spectra was chosen to calibrate reflex distance, then to set up online detection model of pH value in fresh pork by partial least squares regression (PLSR). Kennard-stone algorithm was applied to divide the samples to the calibration set and validation set. The performances of several PLSR models employing various preprocessing methods including multiple scatter correction, derivative and both of them combined were compared. Further, the best performance model was optimized by interval PLSR to decrease the modeling variables of wavelength. The results indicated that the PLSR model based on preprocessing of multiple scatter correction (MSC) combined with first derivative gave the best performance with 0.905 of the correlation coefficient for validation set and 0.051 of the root of mean square errors for validation set. For the best PLSR model performance, the correlation coefficient of validation set increased to 0.926 and the root of mean square errors for validation set to 0.045 in the optimization interval PLSR model. However, only half of variables were used. The research demonstrates that using visible and near-infrared spectroscopy to determine fresh pork pH online is feasible.