Osmanthus fragrans (scientific name: Osmanthus fragrans (Thunb.) Lour.) is a species of the Osmanthus genus in the family Oleaceae, and it has a long history of cultivation in China. O. fragrans is edible and is well known for conferring a natural fragrance to desserts. This flowering plant has long been cultivated for ornamental purposes. Most contemporary literature related to O. fragrans focuses on its edible value and new species discovery, but the functional use of O. fragrans is often neglected. O, fragrans has many properties that are beneficial to human health, and its roots, stems, leaves, flowers and fruits have medicinal value. These characteristics are recorded in the classics of traditional Chinese medicine. Studies on the metabolites and medicinal value of O. fragrans published in recent years were used in this study to evaluate the medicinal value of O. fragrans. Using keywords such as metabolites and Osmanthus fragrans, a systematic and nonexhaustive search of articles, papers and books related to the medicinal use of Osmanthus fragrans metabolites was conducted. Fifteen metabolites were identified through this literature search and classified into three categories according to their properties and structure: flavonoids, terpenes and phenolic acids. It was found that the pharmacological activities of these secondary metabolites mainly include antioxidant, anticancer, anti-inflammatory and antibacterial activities and that these metabolites can be used to treat many human diseases, such as cancer, skin diseases, cardiovascular diseases, and neurological diseases. Most of the reports that are currently available and concern the secondary metabolites of Osmanthus fragrans have limitations. Some reports introduce only the general classification of compounds in Osmanthus fragrans, and some reports introduce only a single compound. In contrast, the introduction section of this paper includes both the category and the functional value of each compound. While reviewing the data for this study, the authors found that the specific action sites of these compounds and their mechanisms of action in plants are relatively weak, and in the future, additional research should be conducted to investigate this topic further.

Low back pain (LBP) is a problem that raises medical, social and economic concerns. Accurate and timely assessment and diagnosis of LBP, especially non-specific LBP (NSLBP), can help clinicians develop effective interventions and treatments for LBP patients. In this study, we integrated the B-mode ultrasound image feature from multiple sites with the shear wave elastography (SWE) feature of NSLBP patients, and then employed a support vector machines (SVM) model to classify NSLBP patients with the Visual Analogue Scale (VAS) as the ground truth. A total of 52 subjects were recruited from the University of Hong Kong-Shenzhen Hospital, and an optimal feature set with the size of 48 was obtained after feature extraction and feature selection, among which the SWE elasticity feature had most contribution in the classification task. The five-fold cross-validation accuracy, precision and sensitivity were 0.85, 0.89 and 0.86, respectively, higher than the previously reported values of MRI. The experimental results preliminarily demonstrate that the proposed methods can be applied to the automatic classification of NSLBP and find out the important site and position of the muscle in the NSLBP classification task.