Sage and peppermint leaves and flowers possess antioxidant, anti-inflammatory, and metabolic-regulating properties. This study compared the phenolic profiles confirmed using LC-MS/MS analysis and the biological activities of traditionally prepared sage (SJ) and peppermint juices (PJ), evaluating their ex vivo antioxidant enzyme activity and antidiabetic potential in experimental mice. Quinic acid (2571.86 ± 1.15 µg/g dry weight (d.w.)) and apigenin-7-O-β-glucoside (324.36 ± 1.15 µg/g d.w.) were the predominant phenolic compounds in SJ, while PJ contained caffeic acid (16.96 ± 0.12 µg/g d.w.) and quinic acid (184.27 ± 0.45 µg/g d.w.). The administration of SJ and PJ for ten days significantly reduced the blood glucose levels of the mice. Compared to the control group, which showed an increase from 15.89 ± 3.45 to 29.54 ± 8.94 mmoL/L, SJ-20 mg/kg body weight (BW)-treated mice exhibited a more moderate rise (16.25 ± 7.33 to 21.50 ± 10.38 mmoL/L). Juice administration also enhanced antioxidant enzyme activity, with PJ-20 mg/kg BW significantly increasing superoxide dismutase (SOD) activity (30.15 ± 3.99 U/mg proteins) compared to the control (18.83 ± 2.04 U/mg proteins). Additionally, catalase (CAT) activity was elevated, indicating enhanced oxidative stress defense mechanisms. Furthermore, treatment with SJ-20 mg/kg BW reduced aspartate aminotransferase (AST) (7025.8 ± 1038.8 to 2782 ± 426.9 IU/L) and alanine aminotransferase (ALT) levels (10679 ± 1409.2 to 5336.5 ± 801.5 IU/L), suggesting hepatoprotective effects. These results support the traditional use of sage and peppermint juices as functional beverages with antioxidant, antidiabetic, and hepatoprotective properties, warranting further clinical investigation.

Alchemilla alpina L. (Rosaceae), belongs to a genus well recognized in traditional medicine for treating gynecological disorders and hormonal imbalance; however, the specific bioactivity of A. alpina itself remains poorly characterized. This study aimed to elucidate the phenolic composition and the biological potential of the methanolic (MeOH) extract of A. alpina. LC–MS/MS analysis identified 39 phenolic compounds, with rutin, catechin, kaempferol-3-O-glucoside, and caffeic acid being the dominant constituents. The extract exhibited high total phenolic and flavonoid contents, consistent with strong antioxidant capacities. It demonstrated notable α-glucosidase and acetylcholinesterase inhibitory activities, indicating its potential relevance for metabolic and neurodegenerative disorders. The extract effectively reduced AAPH-induced ROS levels in MRC-5 fibroblasts, indicating cytoprotective and antioxidative effects. The cytotoxicity toward cervical cancer cells HeLa and ovarian cancer cells A2780 was moderate and concentration dependent. A yeast-based fluorescent screen revealed a strong and selective binding affinity toward estrogen receptor α (ERα) and selective inhibition of human recombinant AKR1C3 (59.5%), without affecting AKR1C4. Additionally, high COX-1/COX-2 inhibition (>70%) supported its anti-inflammatory potential. Collectively, these findings provide the first integrated evidence of A. alpina’s phenolic richness and multifunctional bioactivity, scientifically supporting its potential in managing hormone-dependent and oxidative stress-related disorders.

This study examines the compositional differentiation of two Cucurbita species, C. maxima Duchesne and C. moschata Duchesne, to identify chemical markers relevant for their nutritional and functional potential. Multivariate statistical analysis, including principal component analysis (PCA), was applied to chromatographic, chemical, and antioxidant descriptors to visualize patterns of variability among samples. Classification artificial neural network (cANN) models were used to explore the potential of machine learning for sample differentiation, using integrated lipidomic, carotenoid, phenolic, and liquid chromatographic datasets, providing a multidimensional biochemical characterization of Cucurbita samples, achieving good classification within the analyzed dataset, reflecting the model’s capacity to describe the available data. The integration of chemometric and ANN approaches provides a framework for the compositional profiling and quality assessment of Cucurbita species, offering insights into their sustainable valorization as sources of bioactive compounds for food and nutraceutical applications while acknowledging the need for further validation on larger datasets.

Despite the well-established bioactivity of Salvia verticillata L. aerial parts, their roots represent an unexplored plant matrix. In this study, root extracts obtained by ethnopharmacological (EE), maceration (ME), and ultrasound-assisted (UE) extraction were examined. Ultrasound-assisted extraction was optimized using response surface methodology (RSM, CCD) to maximize total phenolic content (TPC) and rosmarinic acid (RA), and the optimized extract was compared with EE and ME. All extracts exhibited high levels of total phenolics, flavonoids, and phenolic acids, while RA was the dominant compound according to HPLC analysis (96.67–137.47 mg g⁻¹ d.e.). UE yielded the highest TPC and exhibited the strongest antioxidant activity in most of the applied assays. All extracts exhibited concentration-dependent tyrosinase inhibitory activity, with UE and EE showing the strongest effects. Moderate antibacterial activity, particularly against Gram-positive strains, and limited antifungal effects were observed. Strong COX-1 inhibition (>70% at 50 μg/mL) confirmed the anti-inflammatory potential of the extracts, whereas slight COX-2 inhibition was observed. UE exhibited moderate cytotoxic activity against A431, SVT2, and HaCaT cells. These findings identify S. verticillata roots as a promising source of rosmarinic acid-rich extracts and point out the importance of the extraction procedure to tailor the biological activity of the extract.