The authenticity and quality of traditional Chinese medicine (TCM) directly impact clinical efficacy and safety. Quality assessment of traditional Chinese medicine (QATCM) is a global concern due to increased demand and shortage of resources. Recently, modern analytical technologies have been extensively investigated and utilized to analyze the chemical composition of TCM. However, a single analytical technique has some limitations, and judging the quality of TCM only from the characteristics of the components is not enough to reflect the overall view of TCM. Thus, the development of multi-source information fusion technology and machine learning (ML) has further improved QATCM. Data information from different analytical instruments can better understand the connection between herbal samples from multiple aspects. This review focuses on the use of data fusion (DF) and ML in QATCM, including chromatography, spectroscopy, and other electronic sensors. The common data structures and DF strategies are introduced, followed by ML methods, including fast-growing deep learning. Finally, DF strategies combined with ML methods are discussed and illustrated for research on applications such as source identification, species identification, and content prediction in TCM. This review demonstrates the validity and accuracy of QATCM-based DF and ML strategies and provides a reference for developing and applying QATCM methods.

Background: The identification and enumeration of medicinal plants at high elevations is an important part of accurate yield calculations. However, the current assessment of medicinal plant reserves continues to rely on field sampling surveys, which are cumbersome and time-consuming. Recently, unmanned aerial vehicle (UAV) remote sensing and deep learning (DL) have provided ultrahigh-resolution imagery and high-accuracy object recognition techniques, respectively, providing an excellent opportunity to improve the current manual surveying of plants. However, accurate segmentation of individual plants from drone images remains a significant challenge due to the large variation in size, geometry, and distribution of medicinal plants. Results: In this study, we proposed a new pipeline for wild medicinal plant detection and yield assessment based on UAV and DL that was specifically designed for detecting wild medicinal plants in an orthomosaic. We used a drone to collect panoramic images of Lamioplomis rotata Kudo (LR) in high-altitude areas. Then, we annotated and cropped these images into equally sized sub-images and used a DL model Mask R-CNN for object detection and segmentation of LR. Finally, on the basis of the segmentation results, we accurately counted the number and yield of LRs. The results showed that the Mask R-CNN model based on the ResNet-101 backbone network was superior to ResNet-50 in all evaluation indicators. The average identification precision of LR by Mask R-CNN based on the ResNet-101 backbone network was 89.34%, while that of ResNet-50 was 88.32%. The cross-validation results showed that the average accuracy of ResNet-101 was 78.73%, while that of ResNet-50 was 71.25%. According to the orthomosaic, the average number and yield of LR in the two sample sites were 19,376 plants and 57.93 kg and 19,129 plants and 73.5 kg respectively. Conclusions: The combination of DL and UAV remote sensing reveals significant promise in medicinal plant detection, counting, and yield prediction, which will benefit the monitoring of their populations for conservation assessment and management, among other applications.

Cancer is one of the leading causes of death worldwide, yet the drugs currently approved for cancer treatment are associated with significant side effects, making it urgent to develop alternative drugs with low side effects. Polysaccharides are natural polymers with ketone or aldehyde groups, which are widely found in plants and have various biological activities such as immunomodulation, antitumor and hypolipidemic. The lower plants have attracted much attention for their outstanding anticancer effects, and many studies have shown that medicinal lower plant polysaccharides (MLPPs) have antitumor activity against various cancers and are promising alternatives with potential development in the food and pharmaceutical fields. Therefore, this review describes the structure and mechanism of action of MLPPs with antitumor activity. In addition, the application of MLPPs in cancer treatment is discussed, and the future development of MLPPs is explored.

Lung diseases have become a major threat to human health worldwide. Despite advances in treatment and intervention in recent years, effective drugs are still lacking for many lung diseases. As a traditional natural medicine, Tibetan medicine has had a long history of medicinal use in ethnic minority areas, and from ancient times to the present, it has a good effect on the treatment of lung diseases and has attracted more and more attention. In this review, a total of 586 Tibetan medicines were compiled through literature research of 25 classical works on Tibetan medicine, drug standards, and some Chinese and English databases. Among them, 33 Tibetan medicines have been studied to show their effectiveness in treating lung diseases. To investigate the uses of these Tibetan medicines in greater depth, we have reviewed the ethnomedicinal, phytochemical and pharmacological properties of the four commonly used Tibetan medicines for lung diseases (rhodiola, gentian, sea buckthorn, liexiang dujuan) and the five most frequently used Tibetan medicines (safflower, licorice, sandalwood, costus, myrobalan). It is expected to provide some reference for the development of new drugs of lung diseases in the future.

Tibetan medicine Bang Jian refers to a range of botanical drugs within the Gentiana genus. It serves as a prominent traditional Tibetan botanical drug primarily found in the ethnic minority regions of the Qinghai-Tibet Plateau in China. Traditionally, the dried flowers of Bang Jian, known as “Longdanhua” have been employed in Tibetan medicine to address detoxification, pharyngeal relief, acute and chronic bronchitis, bronchiectasis, lung infections, pulmonary fibrosis, and throat disorders. Surprisingly, there has been no comprehensive review published to date on Tibetan medicine Bang Jian. This passage systematically presents and critically assesses recent advancements in botanical characterization, traditional applications, phytochemistry, pharmacology, and clinical uses of Bang Jian, aiming to provide a scientific foundation for its reasonable use and further exploration. To date, researchers have isolated and identified 92 structurally diverse compounds, with a predominant presence of iridoids, flavonoids, xanthones, and triterpenoids. The crude extracts and metabolites derived from Bang Jian have been found to exhibit a wide range of pharmacological effects, encompassing anti-inflammatory, anti-tumor, anti-bacterial, antiviral, antioxidant, hepatoprotective properties, and protect the respiratory system. Nevertheless, detailed data on the biological effects, metabolic activities, and mechanistic research concerning active monomer metabolites remain insufficient. Consequently, there is a pressing need for comprehensive and in-depth research to guide rational clinical drug usage and evaluate the medicinal attributes of Bang Jian.