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.

The quality of Chinese herbal medicine (CHM) directly impacts clinical efficacy and safety. Fingerprint technology is an internationally recognized method for evaluating the quality of CHM. However, the existing quality evaluation models based on fingerprint technology have blocked the ability to assess the internal quality of CHM and cannot comprehensively reflect the correlation between pharmacodynamic information and active constituents. Through mathematical methods, a connection between the "Spectrum" (fingerprint) and the "Effect" (pharmacodynamic data) was established to conduct a spectrum-effect relationship (SER) of CHM to unravel the active component information associated with the pharmacodynamic activity. Consequently, SER can efficiently address the limitations of the segmentation of chemical components and pharmacodynamic effect in CHM and further improve the quality evaluation of CHM. This review focuses on the recent research progress of SER in the field of CHM, including the establishment of fingerprint, the selection of data analysis methods, and their recent applications in the field of CHM. Various advanced fingerprint techniques are introduced, followed by the data analysis methods used in recent years are summarized. Finally, the applications of SER based on different research subjects are described in detail. In addition, the advantages of combining SER with other data are discussed through practical applications, and the research on SER is summarized and prospected. This review proves the validity and development potential of the SER and provides a reference for the development and application of quality evaluation methods for CHM.

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.

Background: Shilajit is a commonly used Tibetan medicine, and its water extract is mainly used for various heat-related syndrome, especially that of stomach, liver and kidney. Shilajit is found to exudate from rocks of cliff at an altitude of 2000–4000 m as a water-soluble mixture of black paste and animal feces of Trodocterus spp. or Ochotona spp. Because it is difficult to reach the exudation points so as to explain the its formation process, the source of Shilajit still remains unclear and controversial, which severely impedes its safety and efficacy in clinical application. Methods: In this work, a series of investigations as rock flakes identification, porosity determination, rock mineral analysis, scanning electron microscopy (SEM), and energy dispersive spectrometer (EDS) have been carried out to clarify the source of Shilajit, including the storage condition and exudation process of its organic matter, and to investigate the geological structure of the exudation points as well as physical and chemical characteristics of the mother rocks. Results: The Shilajit exudation points were mainly distributed on the steep cliffs, where there were cavities and sections that could not be eroded by rainwater. The fundamental structure of the exudation points was determined by the rock’s bedding planes, joints, fracture surfaces and faults, and developed into micro-topography later. The exudation points were distributed in the Triassic strata and scattered in the Early Mesozoic granitoids. The lithologic features were mainly slate, carbonaceous slate and sandy slate etc. The background rocks were characterized by intergranular pores, dissolved pore, joint and fracture development. Organic matter was widely distributed in these pores and fissures, which had condition for storage and exudation of organic matter. Conclusions: Shilajit mainly distributed on sunny steep slopes and cliffs with a slope of 60° or above at altitude of 2000–4000 m. The lithology character of the Shilajit exudation area were mainly various metamorphic rocks of sedimentary rocks that were rich in organic carbon. The organic matter in Shilajit was found to flow out naturally from rocks along pore, structural plane and even accumulate on the surface of rock as a result of storage environment change caused by rock tectonic action.