At present, the digitalization and intelligence level of dripping pills production process is low, and there is a lack of process monitoring methods, which makes it difficult to effectively control the quality of dripping pills. Therefore, this paper proposed an online monitoring method for the dripping process of dripping pills based on laser detection technology and multivariate data analysis (MVDA) technology. Firstly, the width data of the falling droplets during the dripping process of the dripping pills were collected by the laser detector at a high frequency. Secondly, based on the width data, the nodes were selected for each droplet and the features were extracted. Then, the principal component analysis (PCA) model was established based on the feature dataset under normal process conditions, and Hotelling's T² or DModX statistic was selected to determine whether the droplets in the dripping process were abnormal, and the abnormalities were classified and diagnosed by the principal component score map combined with K-nearest neighbor (KNN) algorithm. In this study, the feasibility of this method was investigated by taking the dripping process of Ginkgo biloba leaf dripping pills as an example. The results showed that the obtained model has good detection and diagnosis ability for abnormal valve opening, abnormal liquid temperature, and abnormal liquid volume. This method can provide some reference for the industrial production of dripping pills.

Analyze the changes in gene expression profiles during the process of Panax ginseng seed dormancy release, and screen for differential genes, providing a basis for analyzing the mechanism of P. ginseng seed dormancy release. Comparative transcriptome analysis was conducted by using RNA-Seq sequencing technology in P. ginseng seeds stored at different low temperature. A total of 80.97 Gb of Raw reads and 80.19 Gb of Clean reads were obtained from the transcriptome. Principal component analysis and correlation analysis showed that there were significant differences in gene expression patterns at different developmental stages. Upset results showed that 46 248 unigenes were co-expressed in four stages, and 414, 445, 400 and 389 unigenes were specifically expressed in 0, 8, 14 and 28 days, respectively. Gene Ontology functional annotation showed that the differentially expressed genes were mainly involved in nsaturated fatty acid biosynthetic process, nuclear body and oxidoreductase activity. Encyclopedia of Genes and Genomes metabolic pathway showed that differentially expressed genes were mainly involved in peroxisome, mitogen-activated protein kinase signaling pathway-plant, plant hormone signal transduction, ribosome, biosynthesis of unsaturated fatty acid, circadian rhythm-plant and other metabolic pathways. In the process of P. ginseng seed dormancy release, multiple biological processes, such as unsaturated fatty acid biosynthesis and plant hormone signal transduction, are required to coordinate regulation, which constitutes a complex dormancy release regulation network. Transcriptome analysis and differential gene screening of P. ginseng seeds at different sand storage time laid a foundation for the analysis of P. ginseng seed dormancy release mechanism and molecular breeding.

Cancer is the most important leading cause of death worldwide, with about 10 million deaths caused by cancer in 2020. In situ gel drug delivery systems have attracted much attention in the field of pharmacy and biotechnology due to their good histo-compatibility, excellent injectability, high drug delivery capacity, slow-release drug delivery, and less influence by the in vivo environment. Meanwhile, in situ gel can be combined with chemotherapy, photo-thermal therapy, chemokinetic therapy, immunotherapy and so on to deliver drugs into the tumor site in a less invasive way without surgical operation, forming a semi-solid gel reservoir in the tumor site to realize in situ tumor combined therapy. In this paper, the author summarized the research progress of anti-tumor in situ gel delivery system in the past 10 years, introduced its commonly used polymer materials, classification principles and specific application examples, and finally summarized and discussed the key issues, in order to provide reference for the development of new anti-tumor drug delivery system in the future.

To discover new structural hits, based on the important role of pyrazole ring and fragment of pyridinone carboxylic acid in drug design, novel title pyrazolo[3,4-b]pyridine-4-one-5-carboxylic acid derivatives (10a-10p) were designed and synthesized, the structures were confirmed by spectral data and elemental analyses. The antibacterial and antitumor activities were evaluated by the measured minimum inhibitory concentration (MIC) values against the tested four strains and half inhibitory concentration (IC₅₀) values against the tested four cancer cells, respectively. The results displayed markedly poor antibacterial activity and observably potent antitumor activity. In particularly, the title difluorophenyl (10d, 10e, 10f), pyridyl (10j), ethyl (10k) and cycloproyl (10l) compounds exhibited comparable activity against Capan-1 and A549 cells to that of the comparison doxorubicin. Thus, pyrazolo[3,4-b]pyridine-4-one-5-carboxylic acid derivatives as promising antitumor hits need to be developed.

Dihydrofolate reductase (DHFR) is a well-known key target in the treatment of tumors, bacterial infections, and parasitic infections; and it plays a critical role in the biosynthesis of cellular DNA. DHFR inhibitors interfere with one-carbon metabolism by inhibiting substrate binding to DHFR, thereby inhibiting cell proliferation. Research on DHFR inhibitors has continued since the 1940s. To date, a variety of DHFR inhibitors have come into the market, primarily used for anti-tumor, antibacterial, antiparasitic, and anti-inflammatory therapy. This review summarizes the research progress of DHFR inhibitors with antitumor or antibacterial effects in recent years based on the classification of single-target and dual-target and looks forward to the opportunities and challenges faced by the work in this field.

This study focuses on the microbial quality control of the Chinese herbal decoction pieces. In view of the shortcomings of traditional culture methods such as slow detection speed and inability to detect unculturable microorganisms, a new method based on ATP bioluminescence technology combined with statistical analysis methods was established to rapidly predict and quantitatively detect the total aerobic microbial count (TAMC) and total yeast and mold count (TYMC) contaminated Bupleurum chinense DC. decoction pieces. Based on the optimized ATP bioluminesence detection system, accurate detection of pure bacterial solution of Escherichia coli, Bacillus subtilis and Staphylococcus aureus can be achieved, with detection limits of 47.86, 89.13 and 1 862.09 CFU·mL^-1, respectively. The detection time was 6.5 h, and the detection cost was as low as 2 yuan/time. The upper and lower warning limits of TAMC were determined by the misjudgment rates of 10% and 20%, respectively. And the warning limit of TYMC was determined by the misjudgment rate of 20%. The proposed crossing method could quickly predict the amount of microbial contamination in Bupleurum chinense DC. decoction pieces. The constructed partial least squares regression (PLSR) model could accurately quantify the quantity of microbial contamination in Bupleurum chinense DC. decoction pieces. The optimal PLSR prediction model for TAMC had a correction coefficient (R²) of 0.826, a root mean square error of correction set (RMSE_E) of 0.468 and a root mean square error of cross-validation set (RMSE_CV) of 0.465. The R², RMSE_E and RMSE_CV in the prediction model of TYMC were 0.778, 0.543 and 0.541, respectively. The aim of this study is to establish a kind of rapid detection method and prediction models for the microbial limit of traditional Chinese medicine and Chinese herbal decoction pieces, and to provide a more convenient and sensitive detection technology for the microbial quality process control of traditional Chinese medicine products.

Artificial intelligence-aided drug discovery (AIDD) is a new version of computer-aided drug discovery (CADD). AIDD is featured in significantly promoting the performance of conventional CADD. AI markedly enhances the learning ability of CADD. In the 1960s, CADD was established from conventional QSAR approaches, which mainly used regression approaches to derive substructure-activity relationship for compounds with a common scaffold, and guide drug molecular design, figure out the binding features of drugs, and identify potential drug targets. Since the 1990s, structural biology has provided three-dimensional structures of drug targets, enabling drug discovery based on target structure (SBDD), fragment-based drug discovery (FBDD), and structure-based virtual screening (SBVS) with CADD approaches. In the past 30 years, many first in class (FIC) and best in class (BIC) drugs were discovered with CADD. Now, AIDD will further revolutionize CADD by reducing human interventions and mining big chemical and biological data. It is expected that AIDD will significantly enhance the abilities of CADD, virtual screening and drug target identification. This article tries to provide perspectives of CADD and AIDD in medicinal chemistry with case studies.

In the study, specific primers were designed based on the CO Ⅰ gene sequence of Polyrhachis dives. By optimizing the genomic DNA extraction method and amplification conditions, we established an efficient, specific, and accurate DNA molecular identification method for Polyrhachis dives. In this method, the length of the target fragment was 294-308 bp, and the other counterfeits had no target bands. In this paper, the specific identification method of the origin of Polyrhachis dives established can be used to identify the medicinal materials of Polyrhachis dives accurately.

An UPLC-Q-TOF/MS^E technology coupled with UNIFI database was used to develop a rapid, high coverage, accurate and efficient chemical composition qualitative method for Xuezhikang Capsule. A UNIFI database was established utilizing compound name, formula, structure, following automatic matching with high-resolution mass numbers, isotope distributions, mass deviations, fragment ion matching, and chromatographic retention features in UNIFI database to achieve the qualitative results of natural products in Xuezhikang Capsules. Combined with manual confirmation, 82 chemical components were identified in Xuezhikang Capsules, and the MS² fragmentation pathway of typical organic acids, flavonoids, monacrines, and monascus were analyzed to ensure accuracy of the LC-MS workflow. This study clarified the chemical substance basis of Xuezhikang Capsules by LC-MS technology, providing experimental data support for the identification of key quality attributes, quality control and consistency evaluation in the manufacturing process of Xuezhikang Capsules.

Xanthine oxidoreductase (XOR), the key enzyme catalyzing purine to produce uric acid, including two subtypes, xanthine dehydrogenase (XDH) and xanthine oxidase (XO), respectively, in vivo. Usually, XDH and XO can transform to each other. In this study, based on the principle that the subtype XO or XDH uses different electron acceptors, the methods for the measuring the activities of bovine milk XOR (pure enzyme) and its subtypes were established. The optimal concentrations of substrate xanthine (50 μmol·L^-1) and electron acceptor NAD⁺ (50 μmol·L^-1), pH value (7.80) were investigated. The ranges of the XOR, XO, XDH activity which could be determined were 0.97-17.5 U·L^-1, 1-9 U·L^-1, and 66-1 191 mU·L^-1, respectively. Furthermore, the methods for determining the activities of XOR and its subtypes in mouse liver were established. The preparation of liver samples, the optimal concentrations of xanthine (100 μmol·L^-1) and NAD⁺ (100 μmol·L^-1) were researched. And the activity ranges of XOR, XO and XDH in mouse liver which could be determined were 0.67-3.98, 0.19-1.08, and 0.52-3.55 U·gprot^-1, respectively. With the methods above, the effects of classic XOR inhibitor allopurinal (Allo) on XOR, XO and XDH from both milk and mouse liver were determined. All animal experiments have been approved by the Animal Experimental Center, Institute of Materia Medica, Chinese Academy of Medical Science and Peking Union Medical College (00003346). This study established new methods for the determination of XOR and its subtypes activity in pure enzyme system and in mouse liver, respectively, which were accurate and convenient. It laid the experimental foundation for exploring the different pathophysiological effects of XOR in the body and developing new XOR inhibitors.