ABSTRACT
α-Hederin is a monosaccharide pentacyclic triterpene saponin compound derived from the Chinese herb, Pulsatilla. It has garnered considerable attention for its anti-tumor, anti-inflammatory, and spasmolytic pharmacological activities. Given the rising incidence of cancer and the pronounced adverse reactions associated with chemotherapy drugs-which profoundly impact the quality of life for cancer patients-there is an immediate need for safe and effective antitumor agents. Traditional drugs and their anticancer effects have become a focal point of research in recent years. Studies indicate that α-Hederin can hinder tumor cell proliferation and impede the advancement of various cancers, including breast, lung, colorectal, and liver cancers. The principal mechanism behind its anti-tumor activity involves inhibiting tumor cell proliferation, facilitating tumor cell apoptosis, and arresting the cell cycle process. Current evidence suggests that α-Hederin can exert its anti-tumor properties through diverse mechanisms, positioning it as a promising agent in anti-tumor therapy. However, a comprehensive literature search revealed a gap in the comprehensive understanding of α-Hederin. This paper aims to review the available literature on the anti-tumor mechanisms of α-Hederin, hoping to provide valuable insights for the clinical treatment of malignant tumors and the innovation of novel anti-tumor medications.
Subject(s)
Antineoplastic Agents , Liver Neoplasms , Oleanolic Acid , Saponins , Humans , Cell Line, Tumor , Quality of Life , Saponins/pharmacology , Saponins/therapeutic use , Antineoplastic Agents/pharmacology , Antineoplastic Agents/therapeutic use , Oleanolic Acid/pharmacology , Oleanolic Acid/therapeutic useABSTRACT
Nanoplastics are widely distributed in aquatic environments, and nanoplastic pollution has become a global concern. However, few studies have evaluated the toxicity of nanoplastics to freshwater crustaceans. In this study, by adding different concentrations of nanoplastics to water, we explored the effects of nanoplastics on the survival, antioxidant activity, immune enzyme activity, and related gene expression levels in juvenile Macrobrachium nipponense. The results showed that the 96 -h half-lethal concentration of nanoplastics to juvenile shrimp was 396.391 mg/L. As the concentration of nanoplastics increased, the activities of antioxidant enzymes generally decreased, while the contents of hydrogen peroxide and lipid peroxidation products increased. The activities of non-specific immune enzymes first increased and then decreased with increasing nanoplastic concentration. The trends in the expressions of antioxidant-related genes were generally consistent with those in the activities of antioxidant enzymes. As the nanoplastic concentration increased, the expressions of immune-related genes generally increased at first and then decreased. These results indicate that low concentrations of nanoplastics (5 mg/L) may enhance the viability of juvenile shrimp, whereas high concentrations (10,20, 40 mg/L) have inhibitory and/or toxic effects. The findings provide basic information on the toxic effects of nanoplastics in juvenile shrimp.
Subject(s)
Palaemonidae , Animals , Antioxidants , Fresh Water , Gene Expression , Microplastics , Palaemonidae/geneticsABSTRACT
Common spatial pattern (CSP) is a very popular method for spatial filtering to extract the features from electroencephalogram (EEG) signals, but it may cause serious over-fitting issue. In this paper, after the extraction and recognition of feature, we present a new way in which the recognition results are fused to overcome the over-fitting and improve recognition accuracy. And then a new framework for EEG recognition is proposed by using CSP to extract features from EEG signals, using linear discriminant analysis (LDA) classifiers to identify the user's mental state from such features, and using Choquet fuzzy integral to fuse classifiers results. Brain-computer interface (BCI) competition 2005 data sets IVa was used to validate the framework. The results demonstrated that it effective ly improved recognition and to some extent overcome the over-fitting problem of CSP. It showed the effectiveness of this framework for dealing with EEG.