Polygonatum sibiricum polysaccharides (PSP), the extract of Polygonatum sibiricum, are demonstrated to exhibit a wide range of pharmacological activities. A recent study reported that PSP alleviated the aging of the kidney and meninges. However, the effect of PSP on heart aging remains unclear. The present study is aimed at investigating the protection of PSP on D‐galactose‐ (D‐gal‐) induced heart aging. Results showed that irregularly arranged cardiac muscle fibers were observed in heart tissues of D‐gal‐treated mice, and the levels of cardiac troponin T (cTnT), creatine kinase (CK), p21, and p53 were increased after D‐gal treatment. D‐gal‐induced heart aging and injury can be attenuated by oral administration of PSP. Moreover, PSP also decreased reactive oxygen species (ROS) and malondialdehyde (MDA) and increased the level of superoxide dismutase (SOD) in the hearts of D‐gal‐treated mice. DNA damages and lipid peroxidation induced by oxidative stress were also inhibited by PSP as indicated by reduced levels of 8‐hydroxydeoxyguanosine (8‐OHdG) and 4‐hydroxy‐2‐nonenal (4‐HNE). Collectively, PSP attenuated D‐gal‐induced heart aging via inhibiting oxidative stress, suggesting that PSP might serve as a potential effective Chinese herbal active constituent for antiaging therapy.

In this study, a green and effective extraction method was proposed to extract two main compounds, ginsenosides and polysaccharides, from American ginseng by combining deep eutectic solvents (DESs) with aqueous two-phase systems. The factors of type of DESs, water content in DESs, the solid–liquid ratio, extraction temperature, and extraction time were studied in the solid–liquid extraction. Then, the aqueous two-phase system (DESs-ethylene oxide–propylene oxide (EOPO)) and salty solution exchange (EOPO-salty solution) was applied for the purification of polysaccharides. The content of the polysaccharides and ginsenosides were analyzed by the anthrone–sulfuric acid method and HPLC method, which showed that the extraction efficiency of deep eutectic solvents (DESs) was better than conventional methods. Moreover, the antioxidant activities of ginseng polysaccharides and their cytotoxicity were further assayed. The advantages of the current study are that, throughout the whole extraction process, we avoided the usage of an organic reagent. Furthermore, the separated green solvent DESs and EOPO could be recovered and reused for a next cycle. Thus, this study proposed a new, green and recyclable extraction method for extracting ginsenosides and polysaccharides from American ginseng.

Background . Continuous wavelet transform (CWT) based scalogram can be used for photoplethysmography (PPG) signal transformation to classify blood pressure (BP) with deep learning. We aimed to investigate the determinants that can improve the accuracy of BP classification based on PPG and deep learning and establish a better algorithm for the prediction. Methods . The dataset from PhysioNet was accessed to extract raw PPG signals for testing and its corresponding BPs as category labels. The BP category of normal or abnormal followed the criteria of the 2017 American College of Cardiology/American Heart Association (ACC/AHA) Hypertension Guidelines. The PPG signals were transformed into 224 ∗ 224 ∗ 3‐pixel scalogram via different CWTs and segment units. All of them are fed into different convolutional neural networks (CNN) for training and validation. The receiver‐operating characteristic and loss and accuracy curves were used to evaluate and compare the performance of different methods. Results . Both wavelet type and segment length could affect the accuracy, and Cgau1 wavelet and segment‐300 revealed the best performance (accuracy 90%) without obvious overfitting. This method performed better than previously reported MATLAB Morse wavelet transformed scalogram on both of our proposed CNN and CNN‐GoogLeNet. Conclusions . We have established a new algorithm with high accuracy to predict BP classification from PPG via matching of CWT type and segment length, which is a promising solution for rapid prediction of BP classification from real‐time processing of PPG signal on a wearable device.

Establishing a fast and effective extraction method for herbs is beneficial for the determination of their main compounds and estimating their quality. In this study, deep eutectic solvents (DESs) were optimized to simultaneously extract three main types of phenolic acids, i.e., regaloside B, regaloside C, and regaloside E, and polysaccharides from the bulbs of Lilium lancifolium Thunb. Based on the optimized extraction conditions, i.e., an extraction temperature of 50 °C, an extraction time of 40 min, a solid–liquid ratio of 1 : 25, and a ratio of water in the DES of 20%, the extracted amounts of regaloside B, regaloside C, and regaloside E reached 0.31 ± 0.06 mg g⁻¹, 0.29 ± 0.03 mg g⁻¹, and 3.04 ± 0.38 mg g⁻¹, respectively. The extraction efficiencies were higher than those obtained using conventional organic solvents. Next, the polysaccharide levels were measured and compared with those obtained using a conventional hot water extraction method, and equivalent extraction efficiencies were obtained with the conventional hot water extraction method. This study provides a new application of deep eutectic solvents (DESs) for simultaneously extracting phenolic acids and polysaccharides from the bulbs of L. lancifolium Thunb. Considering the biodegradability and pharmaceutical acceptability, DESs as a class of green solvents could have wide applications in the extraction of natural products.

Background: Pu-erh tea is a unique microbially fermented tea, which distinctive chemical constituents and activities are worthy of systematic study. Near infrared spectroscopy (NIR) coupled with suitable chemometrics approaches can rapidly and accurately quantitatively analyze multiple compounds in samples. Methods: In this study, an improved weighted partial least squares (PLS) algorithm combined with near infrared spectroscopy (NIR) was used to construct a fast calibration model for determining four main components, i.e., tea polyphenols, tea polysaccharide, total flavonoids, theanine content, and further determine the total antioxidant capacity of pu-erh tea. Results: The final correlation coefficients R square for tea polyphenols, tea polysaccharide, total flavonoids content, theanine content, and total antioxidant capacity were 0.8288, 0.8403, 0.8415, 0.8537 and 0.8682, respectively. Conclusions: The current study provided a comprehensive study of four main ingredients and activity of pu-erh tea, and demonstrated that NIR spectroscopy technology coupled with multivariate calibration analysis could be successfully applied to pu-erh tea quality assessment.