The ovarian system comprises vital organs in females and is of great significance for the maintenance of reproductive potential and endocrine stability. Although complex pathogenesis undoubtedly contributes to ovarian aging, increasing attention is being paid to the extensive influence of oxidative stress. However, the role of oxidative stress in ovarian aging is yet to be fully elucidated. Exploring oxidative stress-related processes might be a promising strategy against ovarian aging. In this review, compelling evidence is shown that oxidative stress plays a role in the etiology of ovarian aging and promotes the development of other ovarian aging-related etiologies, including telomere shortening, mitochondrial dysfunction, apoptosis, and inflammation. In addition, some natural antioxidants such as quercetin, resveratrol, and curcumin have a protective role in the ovaries through multiple mechanisms. These findings raise the prospect of oxidative stress modulator-natural antioxidants as therapeutic interventions for delaying ovarian aging.

Altered gut microbiota and a damaged colon mucosal barrier have been implicated in the development of colon cancer. Astragalus mongholicus Bunge-Curcuma aromatica Salisb. (ACE) is a common herbal drug pair that widely used clinically to treat cancer. However, whether the anti-cancer effect of ACE is related to gut microbiota remains unclear yet. We standardized ACE and investigated the effects of ACE on tumour suppression and analyze the related mechanisms on gut microbiota in CT26 colon cancer-bearing mice in the present study. Firstly, four flavonoids (calycosin-7-glucoside, ononin, calycosin, formononetin) and three astragalosides (astragaloside A, astragaloside II, astragaloside I) riched in Astragalus mongholicus Bunge, three curcumins (bisdemethoxycurcumin, demethoxycurcumin, curcumin) and four essential oils (curdione, curzerene, germacrone and β-elemene) from Curcuma aromatica Salisb., in concentrations from 0.08 to 2.07 mg/g, were examined in ACE. Then the results in vivo studies indicated that ACE inhibited solid tumours, liver and spleen metastases of colon cancer while simultaneously reducing pathological tissue damage. Additionally, ACE regulated gut microbiota dysbiosis and the short chain fatty acid content in the gut, repaired intestinal barrier damage. ACE treatment suppressed the overgrowth of conditional pathogenic gut bacteria, including Escherichia-Shigella, Streptococcus and Enterococcus, while the probiotic gut microbiota like Lactobacillus, Roseburia, Prevotellaceae_UCG-001 and Mucispirillum were increased. More interestingly, the content level of SCFAs such as propionic acid and butyric acid was increased after ACE administration, which further mediates intestinal SDF-1/CXCR4 signalling pathway to repair the integrity of the intestinal barrier, decrease Cyclin D1 and C-myc expressions, eventually suppress the tumor the growth and metastasis of colon cancer. To sum up, the present study demonstrated that ACE could efficiently suppress colon cancer progression through gut microbiota modification, which may provide a new explanation of the mechanism of ACE against colon cancer.

Angelicae Sinensis Radix is a widely used traditional Chinese medicine and spice in China. The purpose of this study was to develop a methodology for geographical classification of Angelicae Sinensis Radix and determine the contents of ferulic acid and Z-ligustilide in the samples using near-infrared spectroscopy. A qualitative model was established to identify the geographical origin of Angelicae Sinensis Radix using Fourier transform near-infrared (FT-NIR) spectroscopy. Support vector machine (SVM) algorithms were used for the establishment of a qualitative model. The optimum SVM model had a recognition rate of 100% for the calibration set and 83.72% for the prediction set. In addition, a quantitative model was established to predict the content of ferulic acid and Z-ligustilide using FT-NIR. Partial least squares regression (PLSR) algorithms were used for the establishment of a quantitative model. Synergy interval-PLS (Si-PLS) was used to screen the characteristic spectral interval to obtain the best PLSR model. The coefficient of determination for calibration (R2C) for the best PLSR models established with the optimal spectral preprocessing method and selected important spectral regions for the quantitative determination of ferulic acid and Z-ligustilide was 0.9659 and 0.9611, respectively, while the coefficient of determination for prediction (R2P) was 0.9118 and 0.9206, respectively. The values of the ratio of prediction to deviation (RPD) of the two final optimized PLSR models were greater than 2. The results suggested that NIR spectroscopy combined with SVM and PLSR algorithms could be exploited in the discrimination of Angelicae Sinensis Radix from different geographical locations for quality assurance and monitoring. This study might serve as a reference for quality evaluation of agricultural, pharmaceutical, and food products.