IFI16 Positively Regulates RIG-I-Mediated Type I Interferon Production in a STING-Independent Manner.

Previous studies have shown that interferon gene-stimulating protein (STING) is essential for IFN-γ-inducible protein 16 (IFI16) as the DNA sensor and RNA sensor to induce transcription of type I interferon (IFN-I) and is essential for IFI16 to synergize with DNA sensor GMP-AMP (cGAMP) synthase (cGAS) in induction of IFN-I transcription. While other and our previous studies have shown that IFI16 enhanced retinoic acid-inducible gene I (RIG-I)-, which was an RNA sensor, and mitochondrial antiviral signaling (MAVS)-, which was the adaptor protein of RIG-I, induced production of IFN-I, so we wonder whether IFI16 regulates the signal pathway of RNA-RIG-I-MAVS-IFN-I in a STING-dependent manner. We used HEK 293T cells, which did not express endogenous STING and were unable to mount an innate immune response upon DNA transfection and found that IFI16 could enhance RIG-I- and MAVS-mediated induction of IFN-I in a STING-independent way. Furthermore, we found that upregulation of the expression of NF-kappa-B essential modulator (NEMO) by IFI16 was not the mechanism that IFI16 regulated the induction of IFN-I. In conclusion, we found that IFI16 regulated the signal pathway of RNA-RIG-I-MAVS-IFN-I in a STING-independent manner.

Efficient Near-Infrared Spectrum Detection in Nondestructive Wood Testing via Transfer Network Redesign.

This study systematically developed a deep transfer network for near-infrared spectrum detection using convolutional neural network modules as key components. Through meticulous evaluation, specific modules and structures suitable for constructing the near-infrared spectrum detection model were identified, ensuring its effectiveness. This study extensively analyzed the basic network components and explored three unsupervised domain adaptation structures, highlighting their applications in the nondestructive testing of wood. Additionally, five transfer networks were strategically redesigned to substantially enhance their performance. The experimental results showed that the Conditional Domain Adversarial Network and Globalized Loss Optimization Transfer network outperformed the Direct Standardization, Piecewise Direct Standardization, and Spectral Space Transformation models. The coefficients of determination for the Conditional Domain Adversarial Network and Globalized Loss Optimization Transfer network are 82.11% and 83.59%, respectively, with root mean square error prediction values of 12.237 and 11.582, respectively. These achievements represent considerable advancements toward the practical implementation of an efficient and reliable near-infrared spectrum detection system using a deep transfer network.

TRIB3-TRIM8 complex drives NAFLD progression by regulating HNF4α stability.

BACKGROUND & AIMS: Endoplasmic reticulum (ER) stress of hepatocytes plays a causative role in non-alcoholic fatty liver disease (NAFLD). Reduced expression of hepatic nuclear factor 4α (HNF4α) is a critical event in the pathogenesis of NAFLD and other liver diseases. Whether ER stress regulates HNF4α expression remains unknown. The aim of this study was to delineate the machinery of HNF4α protein degradation and explore a therapeutic strategy based on protecting HNF4α stability during NAFLD progression. METHODS: Correlation of HNF4α and tribbles homologue 3 (TRIB3), an ER stress sensor, was evaluated in human and mouse NAFLD tissues. RNA-sequencing, mass spectrometry analysis, co-immunoprecipitation, in vivo and in vitro ubiquitination assays were used to elucidate the mechanisms of TRIB3-mediated HNF4α degradation. Molecular docking and co-immunoprecipitation analyses were performed to identify a cell-penetrating peptide that ablates the TRIB3-HNF4α interaction. RESULTS: TRIB3 directly interacts with HNF4α and mediates ER stress-induced HNF4α degradation. TRIB3 recruits tripartite motif containing 8 (TRIM8) to form an E3 ligase complex that catalyzes K48-linked polyubiquitination of HNF4α on lysine 470. Abrogating the degradation of HNF4α attenuated the effect of TRIB3 on a diet-induced NAFLD model. Moreover, the TRIB3 gain-of-function variant p.Q84R is associated with NAFLD progression in patients, and induces lower HNF4α levels and more severe hepatic steatosis in mice. Importantly, disrupting the TRIB3-HNF4α interaction using a cell-penetrating peptide restores HNF4α levels and ameliorates NAFLD progression in mice. CONCLUSIONS: Our findings unravel the machinery of HNF4α protein degradation and indicate that targeting TRIB3-TRIM8 E3 complex-mediated HNF4α polyubiquitination may be an ideal strategy for NAFLD therapy. IMPACT AND IMPLICATIONS: Reduced expression of hepatic nuclear factor 4α (HNF4α) is a critical event in the pathogenesis of NAFLD and other liver diseases. However, the mechanism of HNF4α protein degradation remains unknown. Herein, we reveal that TRIB3-TRIM8 E3 ligase complex is responsible for HNF4α degradation during NAFLD. Inhibiting the TRIB3-HNF4α interaction effectively stabilized HNF4α protein levels and transcription factor activity in the liver and ameliorated TRIB3-mediated NAFLD progression. Our findings demonstrate that disturbing the TRIM8-TRIB3-HNF4α interaction may provide a novel approach to treat NAFLD and even other liver diseases by stabilizing the HNF4α protein.

Metabolic regulatory network kinetic modeling with multiple isotopic tracers for iPSCs.

The rapidly expanding market for regenerative medicines and cell therapies highlights the need to advance the understanding of cellular metabolisms and improve the prediction of cultivation production process for human induced pluripotent stem cells (iPSCs). In this paper, a metabolic kinetic model was developed to characterize the underlying mechanisms of iPSC culture process, which can predict cell response to environmental perturbation and support process control. This model focuses on the central carbon metabolic network, including glycolysis, pentose phosphate pathway, tricarboxylic acid cycle, and amino acid metabolism, which plays a crucial role to support iPSC proliferation. Heterogeneous measures of extracellular metabolites and multiple isotopic tracers collected under multiple conditions were used to learn metabolic regulatory mechanisms. Systematic cross-validation confirmed the model's performance in terms of providing reliable predictions on cellular metabolism and culture process dynamics under various culture conditions. Thus, the developed mechanistic kinetic model can support process control strategies to strategically select optimal cell culture conditions at different times, ensure cell product functionality, and facilitate large-scale manufacturing of regenerative medicines and cell therapies.

Multimodal Image Fusion Offers Better Spatial Resolution for Mass Spectrometry Imaging.

High-resolution reconstruction has attracted increasing research interest in mass spectrometry imaging (MSI), but it remains a challenging ill-posed problem. In the present study, we proposed a deep learning model to fuse multimodal images to enhance the spatial resolution of MSI data, namely, DeepFERE. Hematoxylin and eosin (H&E) stain microscopy imaging was used to pose constraints in the process of high-resolution reconstruction to alleviate the ill-posedness. A novel model architecture was designed to achieve multi-task optimization by incorporating multi-modal image registration and fusion in a mutually reinforced framework. Experimental results demonstrated that the proposed DeepFERE model is able to produce high-resolution reconstruction images with rich chemical information and a detailed structure on both visual inspection and quantitative evaluation. In addition, our method was found to be able to improve the delimitation of the boundary between cancerous and para-cancerous regions in the MSI image. Furthermore, the reconstruction of low-resolution spatial transcriptomics data demonstrated that the developed DeepFERE model may find wider applications in biomedical fields.

End-to-end interpretable disease-gene association prediction.

Identifying disease-gene associations is a fundamental and critical biomedical task towards understanding molecular mechanisms, the diagnosis and treatment of diseases. It is time-consuming and expensive to experimentally verify causal links between diseases and genes. Recently, deep learning methods have achieved tremendous success in identifying candidate genes for genetic diseases. The gene prediction problem can be modeled as a link prediction problem based on the features of nodes and edges of the gene-disease graph. However, most existing researches either build homogeneous networks based on one single data source or heterogeneous networks based on multi-source data, and artificially define meta-paths, so as to learn the network representation of diseases and genes. The former cannot make use of abundant multi-source heterogeneous information, while the latter needs domain knowledge and experience when defining meta-paths, and the accuracy of the model largely depends on the definition of meta-paths. To address the aforementioned challenges above bottlenecks, we propose an end-to-end disease-gene association prediction model with parallel graph transformer network (DGP-PGTN), which deeply integrates the heterogeneous information of diseases, genes, ontologies and phenotypes. DGP-PGTN can automatically and comprehensively capture the multiple latent interactions between diseases and genes, discover the causal relationship between them and is fully interpretable at the same time. We conduct comprehensive experiments and show that DGP-PGTN outperforms the state-of-the-art methods significantly on the task of disease-gene association prediction. Furthermore, DGP-PGTN can automatically learn the implicit relationship between diseases and genes without manually defining meta paths.

Myeloidderived suppressor cells: Escorts at the maternal-fetal interface.

Myeloid-derived suppressor cells (MDSCs) are a novel heterogenous group of immunosuppressive cells derived from myeloid progenitors. Their role is well known in tumors and autoimmune diseases. In recent years, the role and function of MDSCs during reproduction have attracted increasing attention. Improving the understanding of their strong association with recurrent implantation failure, pathological pregnancy, and neonatal health has become a focus area in research. In this review, we focus on the interaction between MDSCs and other cell types (immune and non-immune cells) from embryo implantation to postpartum. Furthermore, we discuss the molecular mechanisms that could facilitate the therapeutic targeting of MDSCs. Therefore, this review intends to encourage further research in the field of maternal-fetal interface immunity in order to identify probable pathways driving the accumulation of MDSCs and to effectively target their ability to promote embryo implantation, reduce pathological pregnancy, and increase neonatal health.

Drug-target interaction predication via multi-channel graph neural networks.

Drug-target interaction (DTI) is an important step in drug discovery. Although there are many methods for predicting drug targets, these methods have limitations in using discrete or manual feature representations. In recent years, deep learning methods have been used to predict DTIs to improve these defects. However, most of the existing deep learning methods lack the fusion of topological structure and semantic information in DPP representation learning process. Besides, when learning the DPP node representation in the DPP network, the different influences between neighboring nodes are ignored. In this paper, a new model DTI-MGNN based on multi-channel graph convolutional network and graph attention is proposed for DTI prediction. We use two independent graph attention networks to learn the different interactions between nodes for the topology graph and feature graph with different strengths. At the same time, we use a graph convolutional network with shared weight matrices to learn the common information of the two graphs. The DTI-MGNN model combines topological structure and semantic features to improve the representation learning ability of DPPs, and obtain the state-of-the-art results on public datasets. Specifically, DTI-MGNN has achieved a high accuracy in identifying DTIs (the area under the receiver operating characteristic curve is 0.9665).

Detection of Pine Cones in Natural Environment Using Improved YOLOv4 Deep Learning Algorithm.

Achieving the rapid and accurate detection of pine cones in the natural environment is essential for yield estimation and automatic picking. However, the complex background and tiny target pose a significant challenge to pine cone detection. This paper proposes a pine cone detection method using the improved You Only Look Once (YOLO) version 4 algorithm to overcome these challenges. First, the original pine cone image data come from a natural pine forest. Crawler technology is utilized to collect more pine cone images from the Internet to expand the data set. Second, the densely connected convolution network (DenseNet) structure is introduced in YOLOv4 to improve feature reuse and network performance. In addition, the backbone network is pruned to reduce the computational complexity and keep the output dimension unchanged. Finally, for the problem of feature fusion at different scales, an improved neck network is designed using the scale-equalizing pyramid convolution (SEPC). The experimental results show that the improved YOLOv4 model is better than the original YOLOv4 network; the average values of precision, recall, and AP reach 96.1%, 90.1%, and 95.8%; the calculation amount of the model is reduced by 21.2%; the detection speed is fast enough to meet the real-time requirements. This research could serve as a technical reference for estimating yields and automating the picking of pine cones.

Identification of novel hub genes and lncRNAs related to the prognosis and progression of pancreatic cancer by microarray and integrated bioinformatics analysis.

BACKGROUND: Pancreatic cancer (PC) is one of the most invasive and metastatic neoplasms among the fatal malignancies of the digestive system. Abnormal expression of genes and long non-coding RNAs (lncRNAs) are reportedly linked to multiple cancers. However, the lncRNA-mRNA expression profiles and their molecular mechanisms in PC progression are poorly known. This study aimed to map the hub genes and lncRNAs which might play core roles in the development of PC. METHODS: This study used microarray expression analysis to screen for both differentially expressed genes (DEGs) and differentially expressed lncRNAs (DElncRNAs) between PC and matched adjacent non-tumor (AN) tissues. In order to clarify the functional classification of DEGs, we conducted GO and KEGG pathway enrichment analyses via the Enrichr database. LncRNA-mRNA co-expressed networks were also constructed to explore the probable core regulating DEGs and DElncRNAs. Subsequently, the hub genes and lncRNAs were validated via the ONCOMINE and GEPIA databases and the co-expressed networks. RESULTS: By analyzing an mRNA-lncRNA microarray, we identified 943 mRNAs and 1,138 lncRNAs differentially expressed in PC tumors compared with the matched AN tissues. GO analysis confirmed that both up-regulated and down-regulated DEGs were enriched in multiple terms. The KEGG pathways enrichment analyses revealed that DEGs were mostly enriched in the focal adhesion and glutathione metabolism pathways, amongst others. Co-expressed networks were established to reveal the differential interactions between DEGs and DElncRNAs, and to indicate the core regulatory factors located at the core nodes of the co-expressed networks. The expression levels of potential core-regulating DEGs were validated by the GEPIA and ONCOMINE databases, and the relationship between overall survival and tumor stage and the potential core-regulating DEGs was analyzed using the GEPIA database. As a result, five genes and sixteen lncRNAs were finally considered as the hub transcripts in PC. CONCLUSIONS: This study identified DEGs and DElncRNAs between PC tumors and matched AN tissues, and these transcripts were connected with malignant phenotypes in PC through different BPs and signaling pathways. Furthermore, five hub genes and sixteen lncRNAs were identified, which are expected to represent candidate diagnostic biomarkers or potential therapeutic targets for PC.

Norlichexanthone purified from plant endophyte prevents postmenopausal osteoporosis by targeting ER α to inhibit RANKL signaling.

Although different types of drugs are available for postmenopausal osteoporosis, the limitations of the current therapies including drug resistances and adverse effects require identification of novel anti-osteoporosis agents. Here, we defined that norlichexanthone (NOR), a natural product, is a ligand of estrogen receptor-alpha (ERα) and revealed its therapeutic potential for postmenopausal osteoporosis. We used mammalian-one hybrid assay to screen for ERα modulators from crude extracts of several plant endophytes. As a result, NOR purified from the extract of endophyte ARL-13 was identified as a selective ERα modulator. NOR directly bound to ERα with an affinity in nanomolar range, revealing that it is a natural ligand of ERα. NOR induced osteoblast formation in MC3T3-E1 precursor cells. Conversely, NOR inhibited receptor activator of nuclear factor-kappa B ligand (RANKL)-induced osteoclast formation in both RAW264.7 macrophages and mouse primary monocytes. Mechanistically, NOR inhibited RANKL-induced association of ERα and TRAF6 to prevent ERα-mediated TRAF6 activation via Lys63-linked ubiquitination. Importantly, NOR exhibited potent anti-osteoporosis efficacy in an ovariectomized mouse model. Comparing to estrogen, NOR was of much less capability in stimulating endometrial hyperplasia and promoting mammalian cancer cell proliferation. Taken together, our study identified NOR as a natural and high affinity ligand of ERα with substantial anti-osteoporosis but less estrogenic activity.

miR-3613-5p enhances the metastasis of pancreatic cancer by targeting CDK6.

Pancreatic cancer (PC) is a leading cause of cancer mortality and is expected to continue increasing incidence. Abnormally expressed microRNAs have been demonstrated tightly correlated with the development and progression of PC. However, the molecular mechanisms remain largely unknown. In this study through combing both the TCGA database and our two verification PC cohorts, we found the consistent reduction of miR-3613-5p in PC tumors, which significantly correlated with reduced cumulative survival rate among PC patients. PC patients with higher lymph node metastasis rate show reduced miR-3613-5p expression. Through further mechanistic investigation, we demonstrate that miR-3613-5p down-regulated CDK6 in repressing the metastasis capacity of PC cells in vitro and in vivo. Elevated CDK6 were also found in PC samples, which also correlate with poor prognosis. Thus, our study found a novel tumor repressor miR-3613-5p in PC progression.

Fusion of Hyperspectral CASI and Airborne LiDAR Data for Ground Object Classification through Residual Network.

Modern satellite and aerial imagery outcomes exhibit increasingly complex types of ground objects with continuous developments and changes in land resources. Single remote-sensing modality is not sufficient for the accurate and satisfactory extraction and classification of ground objects. Hyperspectral imaging has been widely used in the classification of ground objects because of its high resolution, multiple bands, and abundant spatial and spectral information. Moreover, the airborne light detection and ranging (LiDAR) point-cloud data contains unique high-precision three-dimensional (3D) spatial information, which can enrich ground object classifiers with height features that hyperspectral images do not have. Therefore, the fusion of hyperspectral image data with airborne LiDAR point-cloud data is an effective approach for ground object classification. In this paper, the effectiveness of such a fusion scheme is investigated and confirmed on an observation area in the middle parts of the Heihe River in China. By combining the characteristics of hyperspectral compact airborne spectrographic imager (CASI) data and airborne LiDAR data, we extracted a variety of features for data fusion and ground object classification. Firstly, we used the minimum noise fraction transform to reduce the dimensionality of hyperspectral CASI images. Then, spatio-spectral and textural features of these images were extracted based on the normalized vegetation index and the gray-level co-occurrence matrices. Further, canopy height features were extracted from airborne LiDAR data. Finally, a hierarchical fusion scheme was applied to the hyperspectral CASI and airborne LiDAR features, and the fused features were used to train a residual network for high-accuracy ground object classification. The experimental results showed that the overall classification accuracy was based on the proposed hierarchical-fusion multiscale dilated residual network (M-DRN), which reached an accuracy of 97.89%. This result was found to be 10.13% and 5.68% higher than those of the convolutional neural network (CNN) and the dilated residual network (DRN), respectively. Spatio-spectral and textural features of hyperspectral CASI images can complement the canopy height features of airborne LiDAR data. These complementary features can provide richer and more accurate information than individual features for ground object classification and can thus outperform features based on a single remote-sensing modality.

3-Cl-AHPC inhibits pro-HGF maturation by inducing matriptase/HAI-1 complex formation.

Matriptase is an epithelia-specific membrane-anchored serine protease, and its dysregulation is highly related to the progression of a variety of cancers. Hepatocyte growth factor activator inhibitor-1 (HAI-1) inhibits matriptase activity through forming complex with activated matriptase. The balance of matriptase activation and matriptase/HAI-1 complex formation determines the intensity and duration of matriptase activity. 3-Cl-AHPC, 4-[3-(1-adamantyl)-4-hydroxyphenyl]-3-chlorocinnamic acid, is an adamantly substituted retinoid-related molecule and a ligand of retinoic acid receptor γ (RARγ). 3-Cl-AHPC is of strong anti-cancer effect but with elusive mechanisms. In our current study, we show that 3-Cl-AHPC time- and dose- dependently induces matriptase/HAI-1 complex formation, leading to the suppression of activated matriptase in cancer cells and tissues. Furthermore, 3-Cl-AHPC promotes matriptase shedding but without increasing the activity of shed matriptase. Moreover, 3-Cl-AHPC inhibits matriptase-mediated cleavage of pro-HGF through matriptase/HAI-1 complex induction, resulting in the suppression of pro-HGF-stimulated signalling and cell scattering. Although 3-Cl-AHPC binds to RARγ, its induction of matriptase/HAI-1 complex is not RARγ dependent. Together, our data demonstrates that 3-Cl-AHPC down-regulates matriptase activity through induction of matriptase/HAI-1 complex formation in a RARγ-independent manner, providing a mechanism of 3-Cl-AHPC anti-cancer activity and a new strategy to inhibit abnormal matriptase activity via matriptase/HAI-1 complex induction using small molecules.

Deletion of HNF1α in hepatocytes results in fatty liver-related hepatocellular carcinoma in mice.

Hepatocyte nuclear factor 1α (HNF1α) is a liver-enriched transcription factor that is critical for the maintenance of hepatocyte function. Our previous studies have demonstrated the therapeutic effects of HNF1α on hepatic fibrosis and hepatocellular carcinoma (HCC) in animals. In this study, we created hepatocyte-specific Hnf1α knockout mice using the Cre-loxP recombination system. The knockout mice display increased fatty acid synthesis in the liver. Moreover, these mice spontaneously develop HCC through fatty liver without cirrhosis. Inflammatory cytokines, such as tumor necrosis factor α and IL-6, are upregulated and accompanied by increased phosphorylation of Akt, p-65 and STAT3 in the livers of HNF1α knockout mice. Our findings suggest that HNF1α plays a crucial role in hepatocyte lipid metabolism and hepatocarcinogenesis.

Type II diabetes patients with proteinuria and the recovery of their metabolic parameters.

BACKGROUND: The aim of this study was to provide reasonable guidelines for type II diabetes patients with proteinuria to recover their metabolic parameters. METHODS: A cross-sectional study was conducted in selected groups of type 2 diabetic patients. Hypoglycemic and antihypertensive drug use was recorded. Certain physical examinations were conducted including routine urine test, urinary albumin/creatinine ratio (ACR), metabolic parameters of blood glucose and lipid, and other biochemical indicators. Medication and metabolic indicators were compared between the groups based on the seriousness of the proteinuria. RESULTS: A total of 923 cases were selected for this study, with an average age of 63 years. Based on the proteinuria contents, the cases were divided into three groups of proteinuria negative, microalbuminuria and clinical proteinuria. The recovery rates of the blood pressure control for the groups were 44.2%, 35.3% and 36.3% respectively. The glycated hemoglobin control recovery rates were 22.2%, 18.5% and 15.2% in the groups. The groups' triglyceride control satisfaction rates were 44.4%, 43.3%, and 39.8%. The satisfaction rates of total cholesterol control were 34.5%, 26.8% and 25.7% respectively and the satisfaction rates of LDL-c control of the three groups were 30.6%, 23.8% and 22.1%. This indicated an unregulated use of hypoglycemic agents, antihypertensives and lipid-lowering drugs and low recovery rates for metabolic indicators of the cases. Amongst the 923 cases, 397 had microalbuminuria or clinical proteinuria, and only 22 patients took angiotensin converting enzyme inhibitors (ACEI) or angiotensin receptor blocker (ARB), as antihypertensive drugs. CONCLUSIONS: The use of antihypertensive drugs is not standardized, and only a small portion of patients were treated with ACEI or ARB drugs. Therefore, the proteinuria in diabetic patients should be timely screened and evaluated, as well as renal and metabolic function and antihypertensives and lipid-lowering drugs rationally used.

The Matrine Derivate MASM Prolongs Survival, Attenuates Inflammation, and Reduces Organ Injury in Murine Established Lethal Sepsis.

BACKGROUND: MASM, a novel derivative of matrine, has inhibitory effects on activation of macrophages, dendritic cells, and hepatic stellate cells and binds to ribosomal protein S5 (RPS5). This study was designed to evaluate the effect of MASM on murine-established lethal sepsis and its mechanisms. METHODS: Mouse peritoneal macrophages and RAW264.7 cells that were infected with recombinant lentiviruses encoding shRPS5 were incubated with lipopolysaccharide (LPS) in the absence or presence of MASM in vitro. Endotoxemia induced by LPS injection and sepsis induced by cecal ligation and puncture was followed by MASM treatment. RESULTS: MASM markedly attenuated LPS-induced release and messenger RNA expression of tumor necrosis factor α, interleukin 6, and NO/inducible NO synthase in murine peritoneal macrophages and RAW264.7 cells. Meanwhile, MASM inhibited LPS-induced activation of nuclear factor κB and MAPK pathways. Consistently, RPS5 suppressed LPS-induced inflammatory responses and at least in part mediated the antiinflammatory effect of MASM in vitro. Remarkably, delayed administration of MASM could significantly reduce mortality in mouse sepsis models, which was associated with the reduction in the inflammatory response, the attenuation in multiple organ injury, and the enhanced bacterial clearance. CONCLUSIONS: MASM could be further explored for the treatments of sepsis, especially for administration later after the onset of sepsis.

Matrine derivate MASM suppresses LPS-induced phenotypic and functional maturation of murine bone marrow-derived dendritic cells.

Dendritic cell (DC) maturation process is a crucial step for the development of T cell immune responses and immune tolerance. In this study, we evaluated MASM, a novel derivative of the natural compound matrine that possesses a significant anti-inflammatory and immune-regulating property, for its efficacy to inhibit lipopolysaccharides (LPS)-induced maturation of murine bone marrow-derived dendritic cells. Here we show that MASM profoundly suppresses LPS-induced phenotypic and functional DC maturation. MASM inhibited LPS-induced expression of costimulatory molecules CD80 and CD86 in a concentration-dependent manner. MASM also attenuated LPS-induced IL-12p70, TNF-α, IL-6 and NO release of DCs. The MASM-treated DCs were highly efficient at antigen capture via mannose receptor-mediated endocytosis but showed weak stimulatory capacity for allogeneic T cell proliferation. Furthermore, MASM inhibited LPS-induced PI3K/Akt, MAPK and NF-κB pathways. These novel findings provide new insight into the immunopharmacological role of MASM in impacting on the DCs.

Purification, antitumor and antioxidant activities in vitro of polysaccharides from the brown seaweed Sargassum pallidum.

Supercritical CO2 extraction, ultrasonic-aid extraction and membrane separation technology were applied to prepare Sargassum pallidum polysaccharides (SP). Three main fractions, SP-1, SP-2 and SP-3, were obtained by membranes of 1.0×10(-4)mm pore size and normal molecular-weight cut-off of 50kDa. The resulting three preparations were further purified by DEAE Cellulose-52 chromatography to afford seven polysaccharide fractions. Furthermore, the antitumor and antioxidant activities, in vitro, of the polysaccharide fractions were evaluated by MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide) assay and DPPH (2,2-diphenyl-1-picrylhydrazyl) free radical-scavenging assay, respectively. SP-3-1 and SP-3-2 showed significantly higher antitumor activity against the HepG2 cells, A549 cells, and MGC-803 cells. SP-3 had the highest sulfate content (22.6%). These results indicate that the higher antitumor activity of SP-3-1 and SP-3-2 from SP-3 with lower molecular weights may be related to their molecular weights and sulfate contents. The antioxidant activities of SP-1, SP-2 and SP-3 were low at the tested concentration.