Cancer presents a significant and growing global health challenge in the 21st century, marked by a rising number of new diagnoses and cancer-related deaths reported annually. Glycyrrhetinic Acid (GA), a triterpenoid compound derived from the licorice plant, Glycyrrhiza glabra, G. inflata, and G. uralensis, has a long-standing history of use in traditional healing practices. In this comprehensive review, the evolving research on GA's pharmacological properties published between 2015 and 2024, with a specific focus on its potential as a cancer therapy, is critically analyzed. In preclinical studies, GA showed anti-tumor effects and modulated several cellular pathways involved in cancer growth. Despite this promising anti-tumor activity, GA's clinical use is still being evaluated because of poor solubility and low bioavailability. A clear understanding of GA's complex pharmacokinetics is necessary to optimize its clinical application. This work explores how GA may act against cancer, including its capacity to enhance chemotherapeutic treatments and its interactions in the tumor microenvironment. In addition, this paper critically reviews GA's therapeutic potential across different cancer types and discusses new formulation approaches, pointing to key directions for future clinical and translational studies. This review summarizes the existing evidence on GA and discusses its potential use as a treatment in oncology. Further research is needed to assess GA's effectiveness and safety in clinical studies. Such investigations can help translate laboratory results into practical use in clinical settings.
Introduction. The genus Salvia L. is characterized by significant species diversity, with many of its representatives being used in medicine worldwide. The most well-known pharmacopoeial plant is common sage (Salvia officinalis L.). However, its close relative, clary sage (Salvia sclarea L.), despite its long-standing use in folk medicine, food, and perfume industries, is not an officinal plant. Clary sage contains a rich complex of biologically active compounds, such as essential oil, flavonoids, coumarins, phenolic and organic acids, saponins, and polysaccharides. Extracts of this plant demonstrate antimicrobial, anti-inflammatory, immunomodulatory, and antioxidant activities. Therefore, phenolic compounds of clary sage are of interest for more detailed study as they reveal the plant's potential for use as antioxidant and anti-inflammatory agents. Aim – isolation and identification of phenolic compounds from the aerial part of S. sclarea. Material and methods. The aerial part of clary sage was collected during the flowering phase in the Republic of Uzbekistan and dried in air under shade. The ground raw material (900 g) was extracted with 96% ethanol at room temperature. The combined extract was concentrated under vacuum and subjected to liquid-liquid extraction. The hexane fraction, which showed the richest component composition according to preliminary HPLC analysis, was separated by column chromatography on Sephadex LH-20, eluting with 96% ethanol. The process was monitored, and subfractions were analyzed using TLC on Silica gel 60 F254 and analytical HPLC on a Shimadzu Prominence LC-20 chromatographic system with a Supelcosil LC18 column. The target subfractions were purified by preparative HPLC on a Knauer Smartline system with a Kromasil C18 column. The structure of the isolated compounds was established by NMR spectroscopy on a Bruker Avance III spectrometer (400 MHz). Results.Ten polyphenolic compounds were isolated from the aerial part of clary sage: three phenolic acids and seven flavonoids. Four of them – rosmarinic acid, caffeic acid, luteolin, apigenin – are already known for this species. Six derivatives: 3'-O-methylrosmarinic acid, 6-methoxy-7-O-methylluteolin, 6-methoxy-7-O-methylapigenin, 6-methoxy-4'-O-methylapigenin, 7-O-methylapigenin, and 6-methoxy-7,4'-O-dimethylapigenin – were identified for the first time in S. sclarea. Conclusion. The study results demonstrate the characteristic polyphenolic composition of the aerial part of S. sclarea. The identification of six new components for this species expands the knowledge of its chemical composition and opens prospects for further study of the biological activity of the isolated compounds
The article presents a systematic analysis of the biochemical and phytochemical composition of citrus fruits, from the point of view of biologically active substances, and substantiates the need for an integrated approach to assessing the quality of raw materials. The study results demonstrated an uneven distribution of citrus bioactive compounds across fractions, the importance of flavonoids and limonoids as profile markers, and the fact that ascorbic acid and organic acids are rapidly changing quality indicators. Furthermore, it was noted that pectin and fiber matrix components technologically determine the preservation and extraction of bioactive compounds. It is recommended to jointly interpret the set of markers for processing and optimize storage conditions.
This article provides a marketing analysis of the assortment of hepatoprotective drugs based on data from the 2023–2025 State Register of medicinal products, medical devices, and medical equipment approved for use in the Republic of Uzbekistan. During the study, using data from "Drug audit," the assortment range and market dynamics of hepatoprotective drugs produced by foreign and domestic pharmaceutical companies registered in the republic were examined. Additionally, the growth trends, competitive environment, and position in the pharmaceutical market of this group of drugs were assessed.
The sleep abnormalities are called sleep disorder which can disturb sleep of the person. Sleep apnea, insomnia, RL syndrome and circadian rhythm disease are the several category of sleep disorders. Early sleep disorders prediction can save person otherwise it may forms heart disease such as arrhythmia. Sleep disorders are predicted by Artificial Intelligence(AI) models in proposed work. AI models proposed for health care domain to classify health diseases. Transfer learning(TL), Machine learning(ML), Deep learning(DL) from the AI used to identify sleep abnormalities. Resnet50 model from TL, Vision Transformer(ViT) from DL and XGBoost from ML used to classify sleep diseases. ResNet50 model predicts sleep disorders with 97% accuracy over other models introduced such as XGBoost and Vision Transformer Proposed implementation shows highly accurate results over existing research methods. This research shows awareness on sleep abnormalities by AI models.
