Phytochemical Profile and Biological Activities of Extracts Obtained from Young Shoots of Blackcurrant (Ribes nigrum L.), European Blueberry (Vaccinium myrtillus L.), and Mountain Cranberry (Vaccinium vitis-idaea L.)

Abstract

This study explores the bioactive potential of young shoots from blackcurrant, European blueberry, and mountain cranberry, widely employed in gemmotherapy and phytotherapy, as rich sources of antioxidants, antimicrobial agents, and anti-inflammatory components. The primary aims of this study were to enhance the extraction conditions for bioactive compounds from blackcurrant young shoots using Modde software for experimental design, to conduct a comprehensive phytochemical analysis of blackcurrant, European blueberry, and mountain cranberry young shoot extracts through LC–MS analysis, and to evaluate the in vitro biological activities of these optimized extracts. The experimental design comprised multiple variables: extraction techniques, solvent type, extraction time, apparent pH, and the solvent-to-vegetal product ratio. The responses included total phenolic content, total flavonoid content, condensed tannin content, and total antioxidant activity determined through the DPPH assay. Furthermore, the antioxidant potential of the extracts was validated through in vitro cell culture experiments, in addition to the cytotoxicity assessments conducted on both normal and cancer cell lines. Extracts obtained through Ultra-Turrax extraction using 70% acetone displayed high levels of polyphenolic compounds and enhanced antioxidant potential, regardless of young shoots origin. LC–MS analysis revealed the predominant occurrence of chlorogenic acid, hyperoside, and isoquercitrin in all examined samples. The optimized extracts also displayed significant biological potential when evaluated in vitro on cell lines. These results provide valuable insights into the potent bioactive components present in these young shoot extracts, paving the way for further exploration in therapeutic applications.

Selection of a Digitalis purpurea Cell Line with Improved Bioconversion Capacity of Hydroquinone into Arbutin

“…Like R 2 , a higher Q 2 value suggests better predictive ability as it is an Life 2024, 14, 84 9 of 17 indicator of how good the model is expected to perform on external or validation datasets. Q 2 should be close to R 2 (<0.3 difference between them), indicating that the model not only fits the experimental data well but also has good predictive power [36]. As indicated by the results given in Table 5, significant models (p < 0.05) were developed for all responses because there is a statistically significant difference between the percentage of variation that can be explained and the percentage of variation that cannot be modeled.…”