Study on Extraction and Antioxidant Activity of Flavonoids from Hemerocallis fulva (Daylily) Leaves

Antioxidant Capacity of Free and Bound Phenolics from Olive Leaves: In Vitro and In Vivo Responses

Olive leaves are rich in phenolic compounds. This study explored the chemical profiles and contents of free phenolics (FPs) and bound phenolics (BPs) in olive leaves, and further investigated and compared the antioxidant properties of FPs and BPs using chemical assays, cellular antioxidant evaluation systems, and in vivo mouse models. The results showed that FPs and BPs have different phenolic profiles; 24 free and 14 bound phenolics were identified in FPs and BPs, respectively. Higher levels of phenolic acid (i.e., sinapinic acid, 4-coumaric acid, ferulic acid, and caffeic acid) and hydroxytyrosol were detected in the BPs, while flavonoids, triterpenoid acids, and iridoids were more concentrated in the free form. FPs showed a significantly higher total flavonoid content (TFC), total phenolic content (TPC), and chemical antioxidant properties than those of BPs (p < 0.05). Within the range of doses (20-250 μg/mL), both FPs and BPs protected HepG2 cells from H2O2-induced oxidative stress injury, and there was no significant difference in cellular antioxidant activity between FPs and BPs. The in vivo experiments suggested that FP and BP treatment inhibited malondialdehyde (MDA) levels in a D-galactose-induced oxidation model in mice, and significantly increased antioxidant enzyme activity of superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), catalase (CAT), and the total antioxidant capacity (T-AOC). Mechanistically, FPs and BPs exert their antioxidant activity in distinct ways; FPs ameliorated D-galactose-induced oxidative stress injury partly via the activation of nuclear factor erythroid-2-related factor 2 (Nrf2) signaling pathway, while the BP mechanisms need further study.

Synthesis, Antioxidant, and Antifungal Activities of β-Ionone Thiazolylhydrazone Derivatives and Their Application in Anti-Browning of Freshly Cut Potato

In order to develop a new type of antioxidants with high efficiency, a series of β-ionone thiazolylhydrazone derivatives were designed and synthesized from β-ionone, and their structures were characterized by 1H-NMR, 13C-NMR, FT-IR, and HR-MS. The antioxidant test in vitro indicated that most of the target compounds had high biological activity. Among them, compound 1k exhibited very strong DPPH (1,1-diphenyl-2-picrylhydrazyl radical)-scavenging activity with a half-maximal effective concentration (IC50) of 86.525 μM. Furthermore, in the ABTS (2,2-azinobis (3-ethylbenzothiazoline-6-sulfonate) diammonium salt)-scavenging experiment, compound 1m exhibited excellent activity with an IC50 of 65.408 μM. Their biological activities were significantly better than those of the positive control Trolox. These two compounds, which have good free-radical-scavenging activity in vitro, were used as representative compounds in the anti-browning experiment of fresh-cut potatoes. The results showed that 1k and 1m had the same anti-browning ability as kojic acid, and they were effective browning inhibitors. In addition, it is well known that microbial infection is one of the reasons for food oxidation. Therefore, we investigated the antifungal activity of 25 target compounds against eight plant fungi at a concentration of 125 mg/L. The results indicated that these compounds all have some antifungal activity and may become new potential fungicides. Notably, compound 1u showed the best inhibitory effect against Poria vaporaria, with an inhibition rate as high as 77.71%; it is expected to become the dominant structure for the development of new antifungal agents.

Daylily (Hemerocallis fulva Linn.) flowers improve sleep quality in human and reduce nitric oxide and interleukin-6 production in macrophages

The flowers of daylily (Hemerocallis fulva Linn.) have been used as vegetable and medicinal herb for thousands of years in Taiwan and eastern Asia. Daylily flowers have been demonstrated to exert several biomedical properties. In this study, we provided the evidences show that daylily flowers exert anti-inflammatory activity in vitro and improved the sleep quality in vivo. We demonstrated that adult volunteers received water extract of daylily flowers improved sleep quality, sleep efficiency and daytime functioning, while sleep latency was reduced, compared to the adult volunteers received water. In addition, we demonstrated that aqueous and ethanol extracts of daylily flowers inhibited nitric oxide and interleukin-6 production in lipopolysaccharide-activated macrophages. Furthermore, the quantitative high performance liquid chromatography-based analysis showed the rutin content of the aqueous extract, ethanolic extract, ethyl acetate fractions of ethanolic extract, and water fractions of ethanolic extract were 7.27, 23.30, 14.71, and 57.43 ppm, respectively. These results indicate that daylily flowers have the potential to be a nutraceutical for improving inflammatory-related diseases and sleep quality in the future.

Study on the Detection Method for Daylily Based on YOLOv5 under Complex Field Environments

Intelligent detection is vital for achieving the intelligent picking operation of daylily, but complex field environments pose challenges due to branch occlusion, overlapping plants, and uneven lighting. To address these challenges, this study selected an intelligent detection model based on YOLOv5s for daylily, the depth and width parameters of the YOLOv5s network were optimized, with Ghost, Transformer, and MobileNetv3 lightweight networks used to optimize the CSPDarknet backbone network of YOLOv5s, continuously improving the model's performance. The experimental results show that the original YOLOv5s model increased mean average precision (mAP) by 49%, 44%, and 24.9% compared to YOLOv4, SSD, and Faster R-CNN models, optimizing the depth and width parameters of the network increased the mAP of the original YOLOv5s model by 7.7%, and the YOLOv5s model with Transformer as the backbone network increased the mAP by 0.2% and the inference speed by 69% compared to the model after network parameter optimization. The optimized YOLOv5s model provided precision, recall rate, mAP, and inference speed of 81.4%, 74.4%, 78.1%, and 93 frames per second (FPS), which can achieve accurate and fast detection of daylily in complex field environments. The research results can provide data and experimental references for developing intelligent picking equipment for daylily.