Therapeutic effects of tetrandrine in inflammatory diseases: a comprehensive review.

Inflammation can be triggered by any factor. The primary pathological manifestations can be summarized as the deterioration, exudation, and proliferation of local tissues, which can cause systemic damage in severe cases. Inflammatory lesions are primarily localized but may interact with body systems to cause provocative storms, parenchymal organ lesions, vascular and central nervous system necrosis, and other pathologic responses. Tetrandrine (TET) is a bisbenzylquinoline alkaloid extracted from the traditional Chinese herbal medicine Stephania tetrandra, which has been shown to have significant efficacy in inflammatory conditions such as rheumatoid arthritis, hepatitis, nephritis, etc., through NF-κB, MAPK, ERK, and STAT3 signaling pathways. TET can regulate the body's imbalanced metabolic pathways, reverse the inflammatory process, reduce other pathological damage caused by inflammation, and prevent the vicious cycle. More importantly, TET does not disrupt body's normal immune function while clearing the body's inflammatory state. Therefore, it is necessary to pay attention to its dosage and duration during treatment to avoid unexpected side effects caused by a long half-life. In summary, TET has a promising future in treating inflammatory diseases. The author reviews current therapeutic studies of TET in inflammatory conditions to provide some ideas for subsequent anti-inflammatory studies of TET.

Therapeutic effects and molecular mechanisms of natural products in thrombosis.

Thrombotic disorders, such as myocardial infarction and stroke, are the leading cause of death in the global population and have become a health problem worldwide. Drug therapy is one of the main antithrombotic strategies, but antithrombotic drugs are not completely safe, especially the risk of bleeding at therapeutic doses. Recently, natural products have received widespread interest due to their significant efficacy and high safety, and an increasing number of studies have demonstrated their antithrombotic activity. In this review, articles from databases, such as Web of Science, PubMed, and China National Knowledge Infrastructure, were filtered and the relevant information was extracted according to predefined criteria. As a result, more than 100 natural products with significant antithrombotic activity were identified, including flavonoids, phenylpropanoids, quinones, terpenoids, steroids, and alkaloids. These compounds exert antithrombotic effects by inhibiting platelet activation, suppressing the coagulation cascade, and promoting fibrinolysis. In addition, several natural products also inhibit thrombosis by regulating miRNA expression, anti-inflammatory, and other pathways. This review systematically summarizes the natural products with antithrombotic activity, including their therapeutic effects, mechanisms, and clinical applications, aiming to provide a reference for the development of new antithrombotic drugs.

Dual Drug-Loaded Nanoliposomes Encapsulating Curcumin and 5-Fluorouracil with Advanced Medicinal Applications: Self-Monitoring and Antitumor Therapy.

Due to the presence of physiological barriers, it is difficult to achieve the desired therapeutic efficacy of drugs; thus, it is necessary to develop an efficient drug delivery system that enables advanced functions such as self-monitoring. Curcumin (CUR) is a naturally functional polyphenol whose effectiveness is limited by poor solubility and low bioavailability, and its natural fluorescent properties are often overlooked. Therefore, we aimed to improve the antitumor activity and drug uptake monitoring by simultaneously delivering CUR and 5-Fluorouracil (5-FU) in the form of liposomes. In this study, dual drug-loaded liposomes (FC-DP-Lip) encapsulating CUR and 5-FU were prepared by the thin-film hydration method; their physicochemical properties were characterized; and their biosafety, drug uptake distribution in vivo, and tumor cell toxicity were evaluated. The results showed that the nanoliposome FC-DP-Lip showed good morphology, stability, and drug encapsulation efficiency. It showed good biocompatibility, with no side effects on zebrafish embryonic development. In vivo uptake in zebrafish showed that FC-DP-Lip has a long circulation time and presents gastrointestinal accumulation. In addition, FC-DP-Lip was cytotoxic against a variety of cancer cells. This work showed that FC-DP-Lip nanoliposomes can enhance the toxicity of 5-FU to cancer cells, demonstrating safety and efficiency, and enabling real-time self-monitoring functions.

Nano-Liposomes Double Loaded with Curcumin and Tetrandrine: Preparation, Characterization, Hepatotoxicity and Anti-Tumor Effects.

(1) Background: Curcumin (CUR) and tetrandrine (TET) are natural compounds with various bioactivities, but have problems with low solubility, stability, and absorption rate, resulting in low bioavailability, and limited applications in food, medicine, and other fields. It is very important to improve the solubility while maintaining the high activity of drugs. Liposomes are micro-vesicles synthesized from cholesterol and lecithin. With high biocompatibility and biodegradability, liposomes can significantly improve drug solubility, efficacy, and bioavailability. (2) Methods: In this work, CUR and TET were encapsulated with nano-liposomes and g DSPE-MPEG 2000 (DP)was added as a stabilizer to achieve better physicochemical properties, biosafety, and anti-tumor effects. (3) Results: The nano-liposome (CT-DP-Lip) showed stable particle size (under 100 nm) under different conditions, high solubility, drug encapsulation efficiency (EE), loading capacity (LC), release rate in vitro, and stability. In addition, in vivo studies demonstrated CT-DP-Lip had no significant toxicity on zebrafish. Tumor cytotoxicity test showed that CT-DP-Lip had a strong inhibitory effect on a variety of cancer cells. (4) Conclusions: This work showed that nano-liposomes can significantly improve the physical and chemical properties of CUR and TET and make them safer and more efficient.

[Review on identity feature extraction methods based on electroencephalogram signals].

Biometrics plays an important role in information society. As a new type of biometrics, electroencephalogram (EEG) signals have special advantages in terms of versatility, durability, and safety. At present, the researches on individual identification approaches based on EEG signals draw lots of attention. Identity feature extraction is an important step to achieve good identification performance. How to combine the characteristics of EEG data to better extract the difference information in EEG signals is a research hotspots in the field of identity identification based on EEG in recent years. This article reviewed the commonly used identity feature extraction methods based on EEG signals, including single-channel features, inter-channel features, deep learning methods and spatial filter-based feature extraction methods, etc. and explained the basic principles application methods and related achievements of various feature extraction methods. Finally, we summarized the current problems and forecast the development trend.

The Time-Robustness Analysis of Individual Identification Based on Resting-State EEG.

An ongoing interest towards identification based on biosignals, such as electroencephalogram (EEG), magnetic resonance imaging (MRI), is growing in the past decades. Previous studies indicated that the inherent information about brain activity may be used to identify individual during resting-state of eyes open (REO) and eyes closed (REC). Electroencephalographic (EEG) records the data from the scalp, and it is believed that the noisy EEG signals can influence the accuracies of one experiment causing unreliable results. Therefore, the stability and time-robustness of inter-individual features can be investigated for the purpose of individual identification. In this work, we conducted three experiments with the time interval of at least 2 weeks, and used different types of measures (Power Spectral Density, Cross Spectrum, Channel Coherence and Phase Lags) to extract the individual features. The Pearson Correlation Coefficient (PCC) is calculated to measure the level of linear correlation for intra-individual, and Support Vector Machine (SVM) is used to obtain the related classification accuracy. Results show that the classification accuracies of four features were 85-100% for intra-experiment dataset, and were 80-100% for fusion experiments dataset. For inter-experiments classification of REO features, the optimized frequency range is 13-40 Hz for three features, Power Spectral Density, Channel Coherence and Cross Spectrum. For inter-experiments classification of REC, the optimized frequency range is 8-40 Hz for three features, Power Spectral Density, Channel Coherence and Cross Spectrum. The classification results of Phase Lags are much lower than the other three features. These results show the time-robustness of EEG, which can further use for individual identification system.

Weighted Subject-Semi-Independent ERP-based Brain-Computer Interface.

Subject-independent brain-computer interfaces (SI-BCIs) which require no calibration process, are increasingly affect researchers in BCI field. The efficiencies (accuracies), however, were not satisfying till now. In this paper, we proposed a weighted subject-semi-independent classification method (WSSICM) for ERP based BCI system in which a few blocks data of target subject were used. 47 participants were attended in this study. We compared the accuracies of proposed method with traditional subject-specific classification method（SSCM） which used 15 blocks data of target subject. The averaged accuracies were 95.2% for the WSSICM at 5 blocks and 95.7% for the SSCM at 15 blocks. The accuracies of two method did not show significant difference (p-value=0.652). The method we proposed in this paper which could reduce the calibration time can be used for future BCI systems.