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The prevention and treatment of tumor metastasis can significantly improve the survival of patients with solid tumors. However, there is still a lack of effective drugs for the prevention and treatment of metastasis. The main reason is that the existing intervention and therapeutic drugs are difficult to achieve precise prevention and treatment of metastasis. Due to disseminated tumor cells (DTCs) already exist in the metastatic target organs of early postoperative patients, they are difficult to be detected with existing imaging techniques, and there is a lack of effective intervention drugs and efficacy evaluation systems. When DTCs grow to be detectable by imaging, the patient is already in the advanced stage of cancer, which has become a bottleneck restricting the breakthroughs in metastasis prevention and treatment. This paper reviews the dormancy and survival mechanism of DTCs in metastatic target organs and its intervention strategies, in order to promote the curative effect of metastasis prevention and treatment.

Introduction: Prunellae Spica (PS), derived from the dried fruit spikes of Prunella vulgaris L., is a traditional Chinese medicinal herb. Our previous studies found that PVE30, a water‐extracting ethanol‐precipitating "glycoprotein" macromolecule of PS, was a potential anti‐herpes simplex virus (HSV) candidate. However, due to the complex structure and diverse bioactivity of the "glycoprotein", ensuring its quality consistency across different batches of PVE30 becomes particularly challenging. This poses a significant hurdle for new drug development based on PVE30. Objective: Our study aimed to integrate multi‐index determination coupled with hierarchical cluster analysis (HCA) to holistically profile the quality consistency of "glycoprotein" in PVE30. Methods: High‐performance gel permeation chromatography with refractive index detector (HPGPC‐RID) was used to characterise the molecular weight (Mw) distribution, HPLC‐PDA was used to quantitatively analyse the composed monosaccharides and amino acids, and UV‐VIS was used to quantify the contents of polysaccharides and proteins. Qualitative and quantitative consistency was analysed for each single index in 16 batches of PVE30, and a 16 × 38 data matrix, coupled with HCA, was used to evaluate the holistic quality consistency of PVE30. Results: The newly developed and validated methods were exclusive, linear, precise, accurate, and stable enough to quantify multi‐indexes in PVE30. Single‐index analysis revealed that 16 batches of PVE30 were qualitatively consistent in Mw distribution, polysaccharides and proteins, and the composition of composed monosaccharides and amino acids but quantitatively inconsistent in the relative contents of some "glycoprotein" macromolecules, as well as the composed monosaccharides/amino acids. HCA showed that the holistic quality of PVE30 was inconsistent, the inconsistency was uncorrelated with the regions where PS was commercially collected, and the contents of 17 amino acids and 2 monosaccharides contributed most to the holistic quality inconsistency. Conclusion: Multi‐index determination coupled with HCA was successful in evaluating the quality consistency of PVE30, and the significant difference in quantitative indices was not caused by the origin of PS. The cultivating basis should be confirmed for PVE30‐based new drug development. Multi‐index determination coupled with HCA was successful in evaluating the quality consistency of PVE30, and the significant difference in quantitative indices was not caused by the origin of Prunellae Spica (PS). The contents of 17 amino acids and 2 monosaccharides (D‐Xyl and Ara) contributed most to the holistic quality inconsistency. [ABSTRACT FROM AUTHOR]

Mountain‐cultivated ginseng is typically harvested after 10 years, while ginseng aged over 15 years is considered wild ginseng. This study aims to differentiate mountain‐cultivated ginseng by age, as the fraudulent practice of selling low‐aged cultivated ginseng disguised as high‐aged one is damaging the market. In this study, LC‐MS analyzed 98 ginseng samples, and multivariate statistical analysis identified patterns between samples to select influential components. Machine learning models were developed to identify ginseng samples of different ages. The untargeted metabolomic analysis clearly divided samples aged 4‐20 years into three age groups. Twenty‐two potential age‐dependent biomarkers were discovered to differentiate the three sample groups. Three machine learning models were used to predict new samples, and the optimal model was selected. Some biomarkers could determine age phases according to the differentiation of mountain‐cultivated ginseng samples. These biomarkers were thoroughly analyzed for variation trends. The machine learning models established using the screened biomarkers successfully predicted the age group of new samples. [ABSTRACT FROM AUTHOR]