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Cissus quadrangularis (CQ) is known for its use as a folk medicine for pharmacological properties such as antimicrobial, antiulcer, anti-inflammatory, antitumor, antihemorrhagic, antiallergic, antidiabetic, and antioxidant properties. It is known for its significant role in fracture healing and bone regeneration. To date, it is safe without any adverse effects when used within normal limits. It contains phytoconstituents such as carbohydrates, phytosterols, flavonoids, triterpenoids, glycosides, tannins, saponins, proteins, Vitamin C, alkaloids, and calcium. The alveolar bone is the tooth-supporting apparatus comprising the alveolar bone proper, cortical bone, crestal alveolar bone, and trabecular bone. Periodontal disease is a polymicrobial host immune-mediated inflammatory disease that destroys supporting structures of the teeth. Periodontal regeneration regenerates the lost periodontal structures. It can be using bone grafts. Periodontal tissue engineering is surging in regenerative medicine. It is a triad that includes cells, scaffolds, and growth factors that help to form newly aligned functional periodontal structures. Plant medicine is an alternative medicine that overcomes the side effects of modern medicine. Plant extracts can be used as bioactive scaffolds for regeneration. CQ can differentiate human-derived mesenchymal stem cells into osteoblast and regenerate bone. This review elaborates on the use of CQ as a bioactive compound for periodontal regeneration. [ABSTRACT FROM AUTHOR]

In order to reduce the number of parameters in the Chinese herbal medicine recognition model while maintaining accuracy, this paper takes 20 classes of Chinese herbs as the research object and proposes a recognition network based on knowledge distillation and cross-attention – ShuffleCANet (ShuffleNet and Cross-Attention). Firstly, transfer learning was used for experiments on 20 classic networks, and DenseNet and RegNet were selected as dual teacher models. Then, considering the parameter count and recognition accuracy, ShuffleNet was determined as the student model, and a new cross-attention mechanism was proposed. This cross-attention model replaces Conv5 in ShuffleNet to achieve the goal of lightweight design while maintaining accuracy. Finally, experiments on the public dataset NB-TCM-CHM showed that the accuracy (ACC) and F1_score of the proposed ShuffleCANet model reached 98.8%, with only 128.66M model parameters. Compared with the baseline model ShuffleNet, the parameters are reduced by nearly 50%, but the accuracy is improved by about 1.3%, proving this method's effectiveness. [ABSTRACT FROM AUTHOR]

The use of traditional herbal drugs derived from natural sources is on the rise due totheirminimal side effects and numerous health benefits. However, a major limitation is thelackof standardized knowledge for identifying and mapping the quality of these herbal medicines. This article aims to provide practical insights into the application of artificial intelligence for quality-based commercialization of raw herbal drugs. It focuses onfeatureextraction methods, image processing techniques, and the preparation of herbal images forcompatibility with machine learning models. The article discusses commonly usedimageprocessing tools such as normalization, slicing, cropping, and augmentation to prepareimages for artificial intelligence-based models. It also provides an overviewof global herbal image databases and the models employed for herbal plant/drug identification. Readers will gain a comprehensive understanding of the potential application of various machinelearning models, including artificial neural networks and convolutional neural networks. The article delves into suitable validation parameters like true positive rates, accuracy, precision, and more for the development of artificial intelligence-based identificationandauthentication techniques for herbal drugs. This article offers valuable insights andaconclusive platform for the further exploration of artificial intelligence in the field of herbal drugs, paving the way for smarter identification and authentication methods. [ABSTRACT FROM AUTHOR]

Rubia cordifolia is a well-known plant used in oriental medicine plant, and is also serves as the primary traditional source of plant red dyestuffs. With the current depletion of natural resources of R. cordifolia, it is critical to conduct cultivation studies on the R. cordifolia. Here, we report on the dynamic growth characteristics and secondary metabolite accumulation of cultivated R. cordifolia, as well as the discovery of important genes involved in anthraquinone biosynthesis. The results showed that R. cordifolia grows better in sunny environments than in shaded environments, and its triennials better than its biennials, base on the biomass and the concentration of the primary components purpurin and mollugin. The dynamic accumulation of purpurin and mollugin content suggested that 30 June to 15 October is a fair window for harvesting R. cordifolia, and the possibility of a specific transition connection during the purpurin and mollugin biosynthesis process. Furthermore, we sequenced R. cordifolia using SMRT technology for the first time and obtained 45,925 full-length transcripts, 564 alternative splicing events, 3182 transcription factors, 6454 SSRs, and 6361 lncRNAs. We hypothesized an anthraquinone biosynthetic pathway and found 280 full-length transcripts that may be involved in anthraquinone biosynthesis in R. cordifolia. In addition, RT-qPCR was used to detect the relative expression levels of 12 candidate ungenes in the above- and underground parts of R. cordifolia. Above all, our findings have crucial implications for the field management of cultivation and harvesting of cultivated R. cordifolia, and also provide useful genetic information for clarifying the potential genes involved in anthraquinone biosynthesis.

To characterize pharmacokinetic profile of sennoside A in rats after intravenous and oral administration, a simple and sensitive liquid chromatography tandem mass spectrometry method was established and validated for quantitative determination of sennoside A in rat plasma. After prepared by protein precipitation with acetonitrile, sennoside A and internal standard were separated on a Waters ACQUITY HSS T3 (2.1 × 100 mm, 1.8 μm) column using acetonitrile and 5-mM ammonium acetate in water as mobile phase by gradient elution. The method showed excellent linearity over the range of 0.5-1000 ng/mL with acceptable intra- and inter-day precision, accuracy, matrix effect, and recovery. The stability assay indicated that sennoside A was stable in plasma during the sample collection, preparation, and analysis. Next, the method was applied to pharmacokinetic study of sennoside A in rats. After intravenous and intragastric administrated to rats, the concentrations of sennoside A in plasma at different time points were quantitated and the pharmacokinetic parameters were calculated by software of DAS 2.0. Pharmacokinetic parameters suggested that after oral administration, sennoside A was reached to the peak at 2.9-3.6 h with a C max value of 13.2-31.7 ng/mL. Sennoside A was eliminated slowly from the plasma with T 1/2 value between 15.4 and 18.3 h. The oral absolute bioavailability was among 0.9%-1.3%, which indicated low blood exposure level., (© 2024 John Wiley & Sons Ltd.)