Incarvine C and its analogues inhibit the formation of cell cytoskeleton by targeting Rac1.

Ras-related C3 botulinum toxin substrate 1 (Rac1) has emerged as a key regulator in the treatment of cancer metastasis because of its involvement in the formation of cell plate pseudopods and effects on cell migration. In this study, we found that incarvine C, a natural product isolated from Incarvillea sinensis, and its seven analogues exhibited antitumour activity by inhibiting cell cytoskeleton formation, with moderate cytotoxicity. Accordingly, these compounds inhibited the cytoskeleton-mediated migration and invasion of MDA-MB-231 cells, with inhibition rates ranging from 37.30 % to 69.72 % and 51.27 % to 70.90 % in vitro, respectively. Moreover, they induced G2/M phase cell cycle arrest in MDA-MB-231 cells. A pull-down assay revealed that the interaction between Rac1 and its downstream effector protein PAK1 was inhibited by these compounds and that the compound Ano-6 exhibited substantial activity, with an inhibition rate of more than 90 %. Molecular docking showed that incarvine C and its analogues could bind to the nucleotide-binding pocket of Rac1, maintaining high levels of inactivated Rac1. As Ano-6 exhibited significant activity in vitro, its anti-cancer activity was tested in vivo. Four weeks of oral treatment with Ano-6 was well-tolerated in mice, and it induced a potential anti-tumour response in xenografts of MDA-MB-231 cells. Further studies demonstrated that Ano-6 was enriched in tumour tissues after 2 h of administration and induced an increase in the number of dead tumour cells. In summary, these findings not only reveal the mechanism of incarvine C but also provide a new molecular template for Rac1 inhibitors and identify a promising candidate for breast cancer treatment.

Quantitatively Recognizing Stimuli Intensity of Primary Taste Based on Surface Electromyography.

A novel approach to quantitatively recognize the intensity of primary taste stimuli was explored based on surface electromyography (sEMG). We captured sEMG samples under stimuli of primary taste with different intensities and quantitatively recognized preprocessed samples with Support Vector Machine (SVM). The feasibility of quantitatively recognizing the intensity of Sour, Bitter, and Salty was verified. The sEMG signals were acquired under the stimuli of citric acid (aq), sucrose (aq), magnesium chloride (aq), sodium chloride (aq), and sodium glutamate (aq) with different concentrations, for five types of primary tastes: Sour, Sweet, Bitter, Salty, and Umami, whose order was fixed in this article. The acquired signals were processed with a method called Quadratic Variation Reduction to remove baseline wandering, and an adaptive notch to remove power frequency interference. After extracting 330 features for each sample, an SVM regressor with five-fold cross-validation was performed and the model reached R2 scores of 0.7277, 0.1963, 0.7450, 0.7642, and 0.5055 for five types of primary tastes, respectively, which manifested the feasibilities of the quantitative recognitions of Sour, Bitter, and Salty. To explore the facial responses to taste stimuli, we summarized and compared the muscle activities under stimuli of different taste types and taste intensities. To further simplify the model, we explored the impact of feature dimensionalities and optimized the feature combination for each taste in a channel-wise manner, and the feature dimensionality was reduced from 330 to 210, 120, 210, 260, 170 for five types of primary tastes, respectively. Lastly, we analyzed the model performance on multiple subjects and the relation between the model's performance and the number of experiment subjects. This study can provide references for further research and applications on taste stimuli recognition with sEMG.

Discrimination between Alternative Herbal Medicines from Different Categories with the Electronic Nose.

As alternative herbal medicine gains soar in popularity around the world, it is necessary to apply a fast and convenient means for classifying and evaluating herbal medicines. In this work, an electronic nose system with seven classification algorithms is used to discriminate between 12 categories of herbal medicines. The results show that these herbal medicines can be successfully classified, with support vector machine (SVM) and linear discriminant analysis (LDA) outperforming other algorithms in terms of accuracy. When principal component analysis (PCA) is used to lower the number of dimensions, the time cost for classification can be reduced while the data is visualized. Afterwards, conformal predictions based on 1NN (1-Nearest Neighbor) and 3NN (3-Nearest Neighbor) (CP-1NN and CP-3NN) are introduced. CP-1NN and CP-3NN provide additional, yet significant and reliable, information by giving the confidence and credibility associated with each prediction without sacrificing of accuracy. This research provides insight into the construction of a herbal medicine flavor library and gives methods and reference for future works.

Preparation of the sphingolipid fraction from mycelia of Cordyceps sinensis and its immunosuppressive activity.

ETHNOPHARMACOLOGICAL RELEVANCE: Cordyceps sinensis (CS) is an herbal tonic in traditional Chinese medicine and is used to treat a wide range of disorders, including immune, kidney, respiratory, lung and cardiovascular diseases, in China. Most studies are focused mainly on nucleotides and polysaccharides from CS and consider them to be the main active ingredients, while other ingredients are often disregarded. Hundreds of sphingolipids have been identified from CS and showed inhibitory effects on mouse splenic lymphocytes. AIM OF THE STUDY: This study aimed to establish a method for preparing a fraction of sphingolipids from the mycelial powder of CS and evaluate its immunosuppressive activity. MATERIALS AND METHODS: Fraction of sphingolipids (Fr-SPLs) were prepared by silica gel chromatography and reversed-phase chromatography. Its components were identified and quantified by Quadrupole-Orbitrap UHPLC-MS/MS. PBMCs were prepared from human blood, and splenic lymphocytes, B cells, and T cells were prepared from mouse spleens. The inhibitory effect of Fr-SPLs on cell viability was evaluated by CCK-8 assay. PBMC apoptosis and the ratio of CD4+ T cells and CD8+ T cells were quantified by flow cytometry analysis. The expression of IL-2, IL-10, and TNF-α in PBMCs was detected by ELISA kits. RESULTS: A fraction containing 84.83% of sphingolipids (SPLs) was prepared from the mycelia of CS and named Fr-SPLs. 15 SPLs were identified from the Fr-SPLs. Fr-SPLs significantly inhibited the viability of human peripheral blood mononuclear cells (PBMCs) with an IC50 value of 9.82 µg/mL and promoted PBMC apoptosis in a dose-dependent manner. Moreover, Fr-SPLs inhibited the viability of mouse splenocytes, as well as that of B cells and T cells derived from splenocytes. Furthermore, Fr-SPLs reduced the production of IL-2, IL-10, and TNF-α in PBMCs. CONCLUSIONS: Fr-SPLs show immunosuppressive activity, and this study will be useful for preparing immunosuppressive components from CS and its mycelia for hyperimmune disease.

Isoborneol Attenuates Low-Density Lipoprotein Accumulation and Foam Cell Formation in Macrophages.

PURPOSE: Isoborneol has been used in the treatment of cardiovascular disease for several years in China. However, the mechanism is still unclear. The aim of this study was to identify the novel mechanism of isoborneol for its application in atherosclerotic disease. MATERIALS AND METHODS: The whole-genome gene expression profiles of MCF-7 cells treated with/or without isoborneol were detected by mRNA microarray analysis. The degree of similarity between the gene expression profiles was compared with the Connectivity Map (CMAP) database. An MTT assay was used to assess the toxicity of isoborneol on RAW 264.7 cells. Oil red O staining and a Dil-ox-LDL uptake assay in RAW 264.7 cells were also used to detect the accumulation of lipids in the macrophages and the uptake of oxidized low-density lipoprotein (ox-LDL). RESULTS: Isoborneol was proved to have mRNA expression profiles similar to that of ikarugamycin which can inhibit the uptake of ox-LDL. This process has proved to be an important cause of foam cell formation and early atherosclerotic lesions. It is speculated, therefore, that isoborneol may show similar activity to that shown by ikarugamycin. Subsequently, it was shown that RAW 264.7 cells reduced the absorption of ox-LDL and the accumulation of intracellular lipids after treatment with different concentrations of isoborneol. CONCLUSION: The results indicate that isoborneol inhibits macrophage consumption of ox-LDL, thereby preventing the accumulation of lipids in the macrophages. These results provide evidence for the application of isoborneol in atherosclerotic disease.

Silent Speech Decoding Using Spectrogram Features Based on Neuromuscular Activities.

Silent speech decoding is a novel application of the Brain-Computer Interface (BCI) based on articulatory neuromuscular activities, reducing difficulties in data acquirement and processing. In this paper, spatial features and decoders that can be used to recognize the neuromuscular signals are investigated. Surface electromyography (sEMG) data are recorded from human subjects in mimed speech situations. Specifically, we propose to utilize transfer learning and deep learning methods by transforming the sEMG data into spectrograms that contain abundant information in time and frequency domains and are regarded as channel-interactive. For transfer learning, a pre-trained model of Xception on the large image dataset is used for feature generation. Three deep learning methods, Multi-Layer Perception, Convolutional Neural Network and bidirectional Long Short-Term Memory, are then trained using the extracted features and evaluated for recognizing the articulatory muscles' movements in our word set. The proposed decoders successfully recognized the silent speech and bidirectional Long Short-Term Memory achieved the best accuracy of 90%, outperforming the other two algorithms. Experimental results demonstrate the validity of spectrogram features and deep learning algorithms.