Search Results (4)

Over recent years, the intensity of forest fires has escalated, with wildfire-emitted pollutants exerting substantial impacts on the environment, ecosystems, and human well-being. This study developed a robust predictive framework to quantify wildfire-induced PM_2.5 emission factors (EFs) using seven shrub species—Corylus mandshurica, Eleutherococcus senticosus, Philadelphus schrenkii, Sorbaria sorbifolia, Syringa reticulata, Spiraea salicifolia, and Lonicera maackii. These species represent ecological cornerstones of Northeast Asian forests and hold global relevance as widely introduced or invasive taxa in North America and Europe. The novelty of this research lies in the integration of traditional statistical inference with machine learning to resolve the complex coupling between fuel traits and emissions. We conducted 1134 laboratory-controlled burns in the Liangshui National Nature Reserve, evaluating two continuous and three categorical variables. Initial screening via Analysis of Variance (ANOVA) and stepwise linear regression (Step-AIC) identified the primary drivers of emissions and revealed that interspecific differences among the seven shrubs did not significantly affect the EF (p = 0.0635). To ensure statistical rigor, a log-transformation was applied to the EF data to correct for right-skewness and heteroscedasticity inherent in raw observations. Linear Mixed-effects Models (LMMs) and Gradient Boosting Machines (GBMs) were subsequently employed to quantify factor effects and capture potential nonlinearities. The LMM results consistently identified burning type and plant part as the dominant determinants: smoldering combustion and leaf components exerted strong positive effects on PM_2.5 emissions compared to flaming and branch components. Fuel load was positively correlated with emissions, while moisture content showed a significant negative effect. Notably, the model identified a significant negative quadratic effect for moisture content, indicating a non-linear inhibitory trend as moisture increases. While interspecific differences among the seven shrubs did not significantly affect EFs suggesting that physical fuel traits exert a more consistent influence than species-specific genetic backgrounds, complex interactions were captured. These include a negative synergistic effect between leaves and smoldering, and a positive interaction between moisture content and leaves that significantly amplified emissions. This research bridges the gap between physical fuel traits and chemical smoke production, providing a high-resolution tool for refining global biomass burning emission inventories and assisting international forest management in similar temperate biomes. Full article

We previously found that the water extract of Eleutherococcus senticosus leaves (ES extract) enhanced cognitive function in normal mice. Our study also revealed that the water extract of rhizomes of Drynaria fortunei (DR extract) enhanced memory function in Alzheimer’s disease model mice. In addition, our previous experiments suggested that a combined treatment of ES and DR extracts synergistically improved memory and anti-stress response in mice. Although those two botanical extracts are expected to be beneficial for neuropsychological function, no clinical data has ever been reported. Therefore, we performed a placebo-controlled, randomized, double-blind study to evaluate cognitive enhancement and anti-stress effects by the intake of a combined extract in healthy volunteers. The intake period was 12 weeks. The Japanese version of the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) test was used for neurocognitive assessment. The combined treatment of ES and DR extracts significantly increased the figure recall subscore of RBANS (p = 0.045) in an intergroup comparison. Potentiation of language domain ((p = 0.040), semantic fluency (p = 0.021) and figure recall (p = 0.052) was shown by the extracts (in intragroup comparison). In anti-stress response, the anxiety/uncertainly score was improved by the extract in an intragroup comparison (p = 0.022). No adverse effects were observed. The combined treatment of ES and DR extracts appear to safely enhance a part of cognitive function in healthy adults. Full article

The pharmacological properties of Eleutherococcus senticosus leaf have not been clarified although it is taken as a food item. In this study, the effects of water extract of Eleutherococcus senticosus leaves on memory function were investigated in normal mice. Oral administration of the extract for 17 days significantly enhanced object recognition memory. Compounds absorbed in blood and the brain after oral administration of the leaf extract were detected by LC-MS/MS analyses. Primarily detected compounds in plasma and the cerebral cortex were ciwujianoside C3, eleutheroside M, ciwujianoside B, and ciwujianoside A1. Pure compounds except for ciwujianoside A1 were administered orally for 17 days to normal mice. Ciwujianoside C3, eleutheroside M, and ciwujianoside B significantly enhanced object recognition memory. These results demonstrated that oral administration of the leaf extract of E. senticosus enhances memory function, and that active ingredients in the extract, such as ciwujianoside C3, eleutheroside M, and ciwujianoside B, were able to penetrate and work in the brain. Those three compounds as well as the leaf extract had dendrite extension activity against primary cultured cortical neurons. The effect might relate to memory enhancement. Full article

Eleutherococcus senticosus Maxim. belongs to the Araliaceae family. Phytochemical studies reveal that E. senticosus leaves contain triterpene glycosides along with organic acid derivatives and flavonoid compounds. It is believed that E. senticosus is similar to ginseng because they come from same family and both contain triterpene saponins. E. senticosus leaves have been developed as a functional beverage called ci-wu-jia tea in recent years. Triterpene glycosides are difficult to identify by ultraviolet (UV) detection and contents of these compounds are low in E. senticosus leaves. In this study, a sensitive ultra-high performance liquid chromatographic (UHPLC) method combining UV and tandem mass spectrometry (MS/MS) was developed to characterize the triterpene glycosides from E. senticosus leaves and related commercial products. Fragmentation patterns of three sub-groups of triterpene glycosides in E. senticosus leaves were investigated. Additionally, fragmentation pathways and UV characteristics of organic acid derivatives and flavonoids were also characterized. A compound screening library, including 241 compounds reported in the literature, was created and used to confirm the compounds in the samples. In this study, a total of 24 samples, including 13 plant samples of E. senticosus and 11 ci-wu-jia tea products, were analyzed. Out of the 11 commercial products, three products were discovered to contain green tea (Camellia sinensis) that was considered to be an adulterant since it was not an ingredient on the labels. The developed UHPLC-UV-MS/MS analytical method combined with the UNIFI processing method can simultaneously characterize organic acid derivatives, flavonoids, and triterpene saponins from E. senticosus. It provides a simple and sensitive way to perform quality control of E. senticosus and related ci-wu-jia tea products. Full article