A Review of the Potential Benefits of Herbal Medicines, Small Molecules of Natural Sources, and Supplements for Health Promotion in Lupus Conditions

Using near-infrared hyperspectral imaging combined with machine learning to predict the components and the origin of Radix Paeoniae Rubra

The efficacy and safety of drugs are closely related to the geographical origin and quality of the raw materials. This study focuses on using near-infrared hyperspectral imaging (NIR-HSI) combined with machine learning algorithms to construct content prediction models and origin identification models to predict the components and origin of Radix Paeoniae Rubra (RPR). These models are quick, non-destructive, and accurate for assessing both component content and origin. Spectral data were preprocessed using multiple scattering correction (MSC), Savitzky-Golay smoothing (S-G), and standard normal variate (SNV). Content prediction models for paeoniflorin were developed using principal component regression (PCR), partial least squares regression (PLSR), and ridge regression (RR). Classification models for origin identification utilized support vector machine (SVM), K-nearest neighbor (KNN), and random forest (RF). The SNV-RR model achieved a determination coefficient of 0.8943, while the SNV-SVM model achieved an accuracy of 0.9790. Meanwhile, two feature selection methods were used to further simplify the prediction model while ensuring accuracy, in order to improve the detection efficiency in practical applications. This research demonstrates the feasibility of combining NIR-HSI with machine learning for quality analysis of RPR, providing a theoretical basis for promoting hyperspectral imaging technology in the food and pharmaceutical sectors.

Unveiling the Anti-Obesity Potential of Thunder God Vine: Network Pharmacology and Computational Insights into Celastrol-like Molecules

Obesity, characterized by abnormal or excessive fat accumulation, has become a chronic degenerative health condition that poses significant threats to overall well-being. Pharmacological intervention stands at the forefront of strategies to combat this issue. Recent studies, notably by Umut Ozcan's team, have uncovered the remarkable potential of Celastrol, a small-molecule compound derived from the traditional Chinese herb thunder god vine (Tripterygium wilfordii) as an anti-obesity agent. In this research, computational chemical analysis was employed, incorporating the "TriDimensional Hierarchical Fingerprint Clustering with Tanimoto Representative Selection (3DHFC-TRS)" algorithm to systematically explore 139 active small molecules from thunder god vine. These compounds were classified into six categories, with a particular focus on Category 1 molecules for their exceptional binding affinity to obesity-related targets, offering new avenues for therapeutic development. Using advanced molecular docking techniques and Cytoscape prediction models, six representative Celastrol-like molecules were identified, namely 3-Epikatonic Acid, Hederagenin, Triptonide, Triptotriterpenic Acid B, Triptotriterpenic Acid C, and Ursolic Acid. These compounds demonstrated superior binding affinity and specificity toward two key obesity targets, PPARG and PTGS2, suggesting their potential to regulate fat metabolism and mitigate inflammatory responses. To further substantiate these findings, molecular dynamics simulations and MM-PBSA free-energy calculations were applied to analyze the dynamic interactions between these small molecules and the enzymatic active sites of their targets. The results provide robust theoretical evidence that support the feasibility of these molecules as promising candidates for anti-obesity therapies. This study underscores the power of the 3DHFC-TRS algorithm in uncovering bioactive compounds from natural sources, such as thunder god vine, and highlights the therapeutic promise of PPARG and PTGS2 as novel obesity-related targets. Furthermore, it emphasizes the essential role of computational science in expediting drug discovery, paving the way for personalized and precision-based treatments for obesity and heralding a future of more effective healthcare solutions.

In Silico PASS Predictions and Exploration of Antioxidant and Anti-inflammatory Activity of Citrus Karna Raf. Fruit

Objective

Inflammation and oxidative stress are major factors in the development of many disorders. Natural antioxidants present in plants can interrupt, decrease, or reduce the oxidation of components sensitive to oxidative processes by scavenging free radicals and lowering oxidative stress. Most anti-inflammatory agents used in the management of inflammatory disorders diminish oxidative damage. The biological potential of Citrus karna Raf. remains undisclosed, despite its richness in several bioactive compounds.

Methods

The methanolic extract was evaluated for quantitative phytochemical analysis and antioxidative efficacy using 1,1-diphenyl-2-picrylhydrazyl radical and hydrogen peroxide scavenging activity. A carrageenan-induced paw edema model was employed to evaluate the anti-inflammatory potential as antioxidants exert anti-inflammatory effects. In silico prediction of activity spectra for substance predictions were performed to understand the possible mechanism of action of phytochemicals.

Results

Citrus karna methanolic extract (CKME) showed dose-dependent radical scavenging effects. The powerful scavenging activity of CKME could be due to the diverse polyphenolic compounds such as ascorbic acid, beta-carotene, and naringin. In addition, the percentage inhibition of paw edema and swelling was observed in CKME-treated rats and mice, which is the same as that of standard drug-treated groups. The Pa value of ascorbic acid, beta-bisabolene, linalool, and naringin is more than 0.7 which shows that these phytoconstituents might contribute to the anti-inflammatory action of extract samples such as CKME.

Conclusions

Our findings shows that CKME possess strong antioxidant and anti-inflammatory effects. The richness of plants in polyphenolics such as flavonoids might be a contributing factor for these potential effects.

A Review of the Ethnobotany, Biological Activity, and Phytochemistry of the Plants in the Gundelia Genus

A comprehensive literature search was conducted in PubMed, Cochrane Library, Web of Science, Scopus, the National Library of Medicine (NLM) catalog, and Google Scholar from January 1980 up until October 2023 on plants in the Gundelia genus. Gundelia L. (Asteraceae) has been treated as a monospecific genus with Gundelia tournefortii L. (1753: 814) in most recent floras with wide variation in corolla color, but nowadays, the genus consists of 17 species. The unripe inflorescences of these species, especially G. tournefortii L., are consumed in many ways. 'Akkoub' or 'akko' in Arabic, "Kangar" in Persian, and "Silifa" in Greek are the common names of G. tournefortii L., also known as tumble thistle in English. They have been used in traditional medicine to treat bronchitis, kidney stones, diarrhea, stomach pain, inflammation, liver and blood diseases, bacterial and fungal infections, and mumps. Based on recent studies, their extracts have exhibited hepatoprotective, hypolipidemic, antioxidant, anti-inflammatory, and antimicrobial effects. Moreover, a variety of phytochemicals, including terpenoids, sterols, and fatty acids, as well as vitamins and minerals, have been identified in this genus. This study reviewed the ethnobotany, phytochemicals, and biological activities of the plants in the Gundelia genus as functional foods and herbal remedies.