Small-size Ti3C2Tx MXene nanosheets coated with metal-polyphenol nanodots for enhanced cancer photothermal therapy and anti-inflammation.

As a controllable, simple method with few side effects, near-infrared (NIR) light-based photothermal therapy (PTT) has been proven an effective cancer therapeutic approach. However, PTT-induced inflammation is a potential negative factor. And the overexpressed heat shock proteins (HSPs) by cancer cells can protect them from hyperthermia during PTT. In this work, small-size Ti3C2Tx MXene nanosheets with high photothermal conversion efficiency in the region of NIR, high cargo loading capability and good free radical scavenging capability were chosen for cancer PTT and anti-inflammation. And (-)-epigallocatechin gallate (EGCG) was applied to form EGCG/Fe metal-polyphenol nanodots on the nanosheets. EGCG being released in acid cancer cells could reduce the expression of HSPs and could be used for anti-inflammation. As a result, the complex nanosheets named MXene@EGCG could achieve enhanced cancer PTT and be anti-inflammatory. Both in vitro and in vivo studies proved the good photothermal ability of MXene@EGCG and demonstrated that it could inhibit the expression of HSPs in tumor cells and relieve PTT-induced inflammation. Therefore, the nanosheets show good results in tumor ablation with a low level of inflammation, which provides another possibility for cancer therapy. STATEMENT OF SIGNIFICANCE: Photothermal therapy (PTT)-induced inflammation plays an essential role in some important stages of tumor development and is unfavorable for cancer treatment. And hyperthermia leads to the overexpression of heat shock proteins (HSPs) in cancer cells, which limits the therapeutic effect of PTT. Therefore, we coated small-size Ti3C2Tx MXene nanosheets with (-)-epigallocatechin gallate (EGCG)/Fe metal-polyphenol nanodots and named them as MXene@EGCG. This system shows a good photothermal conversion efficiency at 808 nm. And it can release EGCG in cancer cells to inhibit the expression of HSPs, thus achieving an enhanced cancer PTT. Both MXene and EGCG can also diminish the PTT-trigged inflammation. Both in vitro and in vivo studies prove the good anti-cancer PTT effect and anti-inflammation capability of MXene@EGCG.

Role of the Glycogen Synthase Kinase 3-Cyclic AMP/Protein Kinase A in the Immobilization of Human Sperm by Tideglusib.

Tideglusib is considered to be a promising alternative to nonyl alcohol-9 contraceptives. Previous studies have demonstrated that the rapid spermicidal effect of tideglusib at a high concentration (≥ 10 µM) may occur through detergent-like activity; however, the effect of low concentrations of tideglusib (< 5 µM) on sperm is unknown. We explored the intracellular mechanism of tideglusib (< 5 µM) on the immobilization of human sperm by exploring related signaling pathways in human sperm. After treatment with tideglusib (1.25 µM) for 2 h, sperm motility rate decreased to 0, while sperm membrane integrity rate was 70%. Protein tyrosine phosphorylation level and intracellular cyclic adenosine 3,5-monophosphate (cAMP) concentration decreased significantly compared to those in the control group. Isobutylmethylxanthine and 8-Bromo-cAMP relieved the inhibition of spermatozoa tyrosine phosphorylation, while tyrosine phosphorylation of sperm protein in the H89 and CALP1 treatment groups was significantly inhibited, and there was no difference in the tideglusib treatment group. H-89 and CALP1 reduced the level of serine phosphorylation of GSK-3α/ß (Ser21/9), while its level was enhanced by IBMX and 8-Bromo-cAMP. Our results show the existence of the GSK3-cAMP/PKA regulatory loop in human sperm, which may mediate the immobilization effect of tideglusib at low of concentrations (e.g., 1.25 µM) on sperm motility.

Moisture Controls the Suppression of Panax notoginseng Root Rot Disease by Indigenous Bacterial Communities.

Harnessing indigenous soil microbial suppression is an emerging strategy for managing soilborne plant diseases. Soil moisture is a vital factor in soil microbiomes, but its role in the regulation of microbial suppression is poorly understood. Here, we investigated the correlation of root rot disease of Panax notoginseng with rhizosphere microbial communities mediated by soil moisture gradients from 55% to 100% field capacity (FC); then, we captured the disease-suppressive and disease-inductive microbiomes and validated their functions by a culture experiment with synthetic microbiotas containing keystone species. We found that proper soil moisture at 75% to 95% FC could maintain a disease-suppressive microbiome to alleviate root rot disease. However, extremely low or high soil moistures (>95% FC or <75% FC) could aggravate root rot disease by depleting the disease-suppressive microbiome while enriching the disease-inductive microbiome. Both the low-soil-moisture-enriched pathogen Monographella cucumerina and the high-soil-moisture-enriched pathogen Ilyonectria destructans could synergize with different disease-inductive microbiomes to aggravate disease. Metagenomic data confirmed that low- and high-moisture conditions suppressed antibiotic biosynthesis genes but enriched pathogenicity-related genes, resulting in a change in the soil state from disease suppressive to inductive. This study highlights the importance of soil moisture when indigenous microbial suppression is harnessed for disease control. IMPORTANCE Soilborne diseases pose a major problem in high-intensity agricultural systems due to the imbalance of microbial communities in soil, resulting in the buildup of soilborne pathogens. Harnessing indigenous soil microbial suppression is an emerging strategy for overcoming soilborne plant diseases. In this study, we showed that soil moisture is a key factor in balancing microbiome effects on root rot disease. Proper soil moisture management represent an effective approach to maintain microbial disease resistance by enriching disease-suppressive microbiomes. Conversely, moisture stresses may enrich for a disease-inductive microbiome and aid accumulation of host-specific soilborne pathogens threatening crop production. This work could provide a new strategy for sustainable control of soilborne diseases by enriching the indigenous disease-suppressive microbiome through soil moisture management.

Multi-Target Markov Boundary Discovery: Theory, Algorithm, and Application.

Markov boundary (MB) has been widely studied in single-target scenarios. Relatively few works focus on the MB discovery for variable set due to the complex variable relationships, where an MB variable might contain predictive information about several targets. This paper investigates the multi-target MB discovery, aiming to distinguish the common MB variables (shared by multiple targets) and the target-specific MB variables (associated with single targets). Considering the multiplicity of MB, the relation between common MB variables and equivalent information is studied. We find that common MB variables are determined by equivalent information through different mechanisms, which is relevant to the existence of the target correlation. Based on the analysis of these mechanisms, we propose a multi-target MB discovery algorithm to identify these two types of variables, whose variant also achieves superiority and interpretability in feature selection tasks. Extensive experiments demonstrate the efficacy of these contributions.

Semi-Supervised Multiview Feature Selection With Adaptive Graph Learning.

As data sources become ever more numerous with increased feature dimensionality, feature selection for multiview data has become an important technique in machine learning. Semi-supervised multiview feature selection (SMFS) focuses on the problem of how to obtain a discriminative feature subset from heterogeneous feature spaces in the case of abundant unlabeled data with little labeled data. Most existing methods suffer from unreliable similarity graph structure across different views since they separate the graph construction from feature selection and use the fixed graphs that are susceptible to noisy features. Furthermore, they directly concatenate multiple feature projections for feature selection, neglecting the contribution diversity among projections. To alleviate these problems, we present an SMFS to simultaneously select informative features and learn a unified graph through the data fusion from aspects of feature projection and similarity graph. Specifically, SMFS adaptively weights different feature projections and flexibly fuses them to form a joint weighted projection, preserving the complementarity and consensus of the original views. Moreover, an implicit graph fusion is devised to dynamically learn a compatible graph across views according to the similarity structure in the learned projection subspace, where the undesirable effects of noisy features are largely alleviated. A convergent method is derived to iteratively optimize SMFS. Experiments on various datasets validate the effectiveness and superiority of SMFS over state-of-the-art methods.

Genetic Relationships of Puccinia striiformis f. sp. tritici in Southwestern and Northwestern China.

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a crucial disease for wheat worldwide and constantly threatens wheat production in southwestern and northwestern China, where the environment is a good fit for Pst oversummering and overwintering. However, the underlying genetic dynamics of spring epidemic Pst populations across large areas of continuous planting in the southwestern and northwestern regions are poorly understood. A total of 2,103 Pst isolates were sampled in the spring of 2019 from the two agroecosystems and grouped into three horizontal spatial scales (countywide, provincial, and regional subpopulations) and two vertical spatial scales that consisted of elevational and geomorphic subpopulations. A total of 776 multilocus genotypes were identified, with the highest genetic diversity found in the northern and Sichuan populations, particularly in the Ningxia and Sichuan Basins, while the lowest genetic diversity was found in the Yunnan and Guizhou populations. Multivariate discriminant analysis of principal components (DAPC) and STRUCTURE (STRUCTURE 2.3.4) analyses revealed variation in the genotypic compositions of the molecular groups on horizontal and vertical dimensions from north to south or vice versa and from low to high or vice versa, respectively. The regional neighbor-joining tree revealed three large spatial structures consisting of the southwestern, the northwestern, and the Xinjiang regions, while the Tibetan population connected the southwestern and northwestern regions. The isolates of the Sichuan Basin were scattered over the four quartiles by principal coordinate analysis, which indicated frequent genotype interchange with others. Greater genetic differentiation was observed between the southwestern and northwestern regions. Linkage equilibrium (P ≥ 0.05) was detected on different spatial scales, suggesting that Pst populations are using sexual reproduction or mixed reproduction (sexual and clonal reproduction) in southwestern and northwestern China. IMPORTANCE Understanding the epidemiology and population genetics of plant pathogens is crucial to formulate efficient predictions of disease outbreaks and achieve sustainable integrated disease management, especially for pathogens with migratory capability. Here, this study covers the genetic homogeneity and heterogeneity of different geographical Pst populations on broad to fine spatial scales from the key epidemic regions of the two agroecosystems in China, where wheat stripe rust occurs annually. We provide knowledge of the population genetics of Pst and reveal that, for instance, there is greater genetic diversity in northwestern China, there are close genetic relationships between Yunnan and Guizhou and between Gansu-Ningxia and Qinghai, and there are effects of altitude on genetic compositions, etc. All of these findings clarify the genetic relationships and expand the insights into the population dynamics and evolutionary mechanisms of Pst in southwestern and northwestern China, providing a theoretical basis for achieving sustainable control of wheat stripe rust in key epidemic regions.

Carbon dots capped cerium oxide nanoparticles for highly efficient removal and sensitive detection of fluoride.

Since the excess exposure to F- may induce serious issues to human health, the effective adsorption and sensitive detection of F- is essential. Therefore, carbon dots (CDs) capped CeO2 (CeO2@CDs) was synthesized via hydrothermal treatment of tannic acid and CeCl3. Due to abundant phenolic hydroxyl are reserved and excellent hydrophilicity, CeO2@CDs possess high F- adsorption capacity. The partition coefficient parameters (PC) are determined to be 2.65 L/g, which is comparable with previous work. The kinetics results and adsorption isotherm are consistent with pseudo-second-order model and Freundlich model, respectively, indicating the chemisorption dominate the adsorption, mainly via the ion exchange between hydroxyl and F-. Since phenolic hydroxyl existed on the CeO2@CDs, synergetic effect of CDs and CeO2 contribute to superior ROS eliminating capacity, even at acidic conditions. Moreover, due to the ROS scavenging of CeO2 @CDs abilities can be potentiated by F-, colorimetric detection of F- can be realized via horseradish peroxidase as an indicator. The linear range is 0.3-2.1 mM with limit of detection is 0.13 mg/L. The current results imply that CeO2@CDs possess potential in both efficient removal and sensitive detection of F- related contamination issues and elucidation of development to address other anions related issues.

MicroRNA editing patterns in Huntington's disease.

Huntington's disease (HD) is a neurodegenerative disease. MicroRNAs (miRNAs) are small non-coding RNAs that mediate post-transcriptional regulation of target genes. Although miRNAs are extensively edited in human brains, the editome of miRNAs in brains of HD patients is largely unknown. By analyzing the small RNA sequencing profiles of brain tissues of 28 HD patients and 83 normal controls, 1182 miRNA editing sites with significant editing levels were identified. In addition to 27 A-to-I editing sites, we identified 3 conserved C-to-U editing sites in miRNAs of HD patients. 30 SNPs in the miRNAs of HD patients were also identified. Furthermore, 129 miRNA editing events demonstrated significantly different editing levels in prefrontal cortex samples of HD patients (HD-PC) when compared to those of healthy controls. We found that hsa-mir-10b-5p was edited to have an additional cytosine at 5'-end in HD-PC, and the edited hsa-mir-10b repressed GTPBP10 that was often downregulated in HD. The down-regulation of GTPBP10 might contribute to the progression of HD by causing gradual loss of function of mitochondrial. These results provide the first endeavor to characterize the miRNA editing events in HD and their potential functions.

Population Genetic Structures of Puccinia striiformis f. sp. tritici in the Gansu-Ningxia Region and Hubei Province, China.

Wheat stripe rust, caused by the fungal pathogen Puccinia striiformis f. sp. tritici (Pst), is a destructive wheat disease in China. The Gansu-Ningxia region (GN) is a key area for pathogen over-summering in China, and northwestern Hubei (HB) is an important region for pathogen over-wintering, serving as a source of inoculum in spring epidemic regions. The spatiotemporal population genetic structure of Pst in HB and the pathogen population exchanges between GN and HB are important for estimating the risk of interregional epidemics. Here, 567 isolates from GN and HB were sampled from fall 2016 to spring 2018 and were genotyped using simple sequence repeat markers. The genotypic and genetic diversity of Pst subpopulations in HB varied among seasons and locations. Greater genetic diversification levels were found in the spring compared with fall populations using principal coordinate analysis and Bayesian assignments. In total, there were 17 common genotypes among the 208 determined, as shown by a small overlap of genotypes in the principal coordinate analysis and dissimilar Bayesian assignments in both regions, which revealed the limited genotype exchange between the populations of GN and HB.

Improving stock trading decisions based on pattern recognition using machine learning technology.

PRML, a novel candlestick pattern recognition model using machine learning methods, is proposed to improve stock trading decisions. Four popular machine learning methods and 11 different features types are applied to all possible combinations of daily patterns to start the pattern recognition schedule. Different time windows from one to ten days are used to detect the prediction effect at different periods. An investment strategy is constructed according to the identified candlestick patterns and suitable time window. We deploy PRML for the forecast of all Chinese market stocks from Jan 1, 2000 until Oct 30, 2020. Among them, the data from Jan 1, 2000 to Dec 31, 2014 is used as the training data set, and the data set from Jan 1, 2015 to Oct 30, 2020 is used to verify the forecasting effect. Empirical results show that the two-day candlestick patterns after filtering have the best prediction effect when forecasting one day ahead; these patterns obtain an average annual return, an annual Sharpe ratio, and an information ratio as high as 36.73%, 0.81, and 2.37, respectively. After screening, three-day candlestick patterns also present a beneficial effect when forecasting one day ahead in that these patterns show stable characteristics. Two other popular machine learning methods, multilayer perceptron network and long short-term memory neural networks, are applied to the pattern recognition framework to evaluate the dependency of the prediction model. A transaction cost of 0.2% is considered on the two-day patterns predicting one day ahead, thus confirming the profitability. Empirical results show that applying different machine learning methods to two-day and three-day patterns for one-day-ahead forecasts can be profitable.

A chitosan/mesoporous silica nanoparticle-based anticancer drug delivery system with a "tumor-triggered targeting" property.

To enhance drug utilization and reduce their side effects, the strategy of "tumor-triggered targeting" was introduced to fabricate dual-pH-sensitive chitosan (CHI)/mesoporous silica nanoparticle (MSN)-based anticancer drug delivery system (DDS) in this work. Model drug doxorubicin hydrochloride (DOX) was loaded in MSN, which was modified with benzimidazole (Bz) group. Then chitosan-graft-ß-cyclodextrin (CHI-g-CD) was applied as the "gatekeeper" to cover MSN through host-guest interaction between ß-CD and Bz. After being coated with targeting peptide adamantane-glycine-arginine-glycine-aspartic acid-serine (Ad-GRGDS), methoxy poly(ethylene glycol) benzaldehyde (mPEG-CHO) was finally grafted on CHI through the pH-sensitive benzoic imine bond. Due to the dynamic protection of PEG, the obtained carriers were "stealthy" at pH 7.4, but could reveal the shielded targeting peptide and the positive charge of CHI in the weakly acidic environment achieved a "tumor-triggered targeting". Inside cancer cells, the interaction between ß-CD and Bz group could be destroyed due to the lower pH, resulted in DOX release. Both in vitro and in vivo studies proved the DDS could targeting induce cancer cell apoptosis, inhibit tumor growth, and reduce the cytotoxicity of DOX against normal cells. It is expected that the system named DOX@MSN-CHI-RGD-PEG could be a potential choice for cancer therapy.

Nanoplatform based on GSH-responsive mesoporous silica nanoparticles for cancer therapy and mitochondrial targeted imaging.

Mitochondria, as the energy factory of most cells, are not only responsible for the generation of adenosine triphosphoric acid (ATP) but also essential targets for therapy and diagnosis of various diseases, especially cancer. The safe and potential nanoplatform which can deliver various therapeutic agents to cancer cells and mitochondrial targeted imaging is urgently required. Herein, Au nanoparticles (AuNPs), mesoporous silica nanoparticles (MSN), cationic ligand (triphenylphosphine (TPP)), doxorubicin (DOX), and carbon nanodots (CDs) were utilized to fabricate mitochondrial targeting drug delivery system (denoted as CDs(DOX)@MSN-TPP@AuNPs). Since AuNPs, as the gatekeepers, can be etched by intracellular glutathione (GSH) via ligand exchange induced etching process, DOX can be released into cells in a GSH-dependent manner which results in the superior GSH-modulated tumor inhibition activity. Moreover, after etching by GSH, the CDs(DOX)@MSN-TPP@AuNPs can serve as promising fluorescent probe (λex = 633 nm, λem = 650 nm) for targeted imaging of mitochondria in living cells with near-infrared fluorescence. The induction of apoptosis derived from the membrane depolarization of mitochondria is the primary anti-tumor route of CDs(DOX)@MSN-TPP@AuNPs. As a kind of GSH-responsive mitochondrial targeting nanoplatform, it holds great promising for effective cancer therapy and mitochondrial targeted imaging. The mitochondrial targeting drug delivery system was fabricated by AuNPs, MSN, TPP, and CDs. The nanoplatform can realize redox-responsive drug delivery and targeted imaging of mitochondria in living cells to improve the therapeutic efficiency and security.

Hollow mesoporous MnO2-carbon nanodot-based nanoplatform for GSH depletion enhanced chemodynamic therapy, chemotherapy, and normal/cancer cell differentiation.

A redox-responsive chemodynamic therapy (CDT)-based theranostic system composed of hollow mesoporous MnO2 (H-MnO2), doxorubicin (DOX), and fluorescent (FL) carbon nanodots (CDs) is reported for the diagnosis and therapy of cancer. In general, since H-MnO2 can be degraded by intracellular glutathione (GSH) to form Mn2+ with excellent Fenton-like activity to generate highly reactive ·OH, the normal antioxidant defense system can be injured via consumption of GSH. This in turn can potentiate the cytotoxicity of CDT and release DOX. The cancer cells can be eliminated effectively by the nanoplatform via the synergistic effect of chemotherapy and CDT. The FL of CDs can be restored after H-MnO2 is degraded which blocked the fluorescence resonance energy transfer process between CDs as an energy donor and H-MnO2 as an FL acceptor. The GSH can be determined by recovery of the FL and limit of detection is 1.30 µM with a linear range of 0.075-0.825 mM. This feature can be utilized to efficiently distinguish cancerous cells from normal ones based on different GSH concentrations in the two types of cells. As a kind of CDT-based theranostic system responsive to GSH, simultaneously diagnostic (normal/cancer cell differentiation) and therapeutic function (chemotherapy and CDT) in a single nanoplatform can be achieved. The redox-responsive chemodynamic therapy (CDT)-based theranostic system is fabricated by H-MnO2, DOX, and fluorescent CDs. The nanoplatform can realize simultaneously diagnostic (normal/cancer cell differentiation) and therapeutic function (chemotherapy and CDT) to improve the therapeutic efficiency and security.

Multi-stimuli responsive hollow MnO2-based drug delivery system for magnetic resonance imaging and combined chemo-chemodynamic cancer therapy.

The exploration and application of hollow manganese dioxide nanoparticle (HMDN) for biosensing and biomedicine has gained significant research attention in the past decade. In this study, a type of biodegradable HMDN is prepared for multi-stimuli responsive tumor-targeted drug delivery, which was successfully loaded with doxorubicin hydrochloride (DOX). Then, the drug-loaded HMDN is functionalized with polyethyleneimine (PEI) as a gatekeeper followed by citraconic anhydride (cit) functionalized poly-L-lysine (PLL(cit)) as a charge reversal moiety successively to yield the resultant DOX@HMDN-PEI-PLL(cit) nanoparticles. In vitro study showed that DOX@HMDN-PEI-PLL(cit) exhibited a ''stealthy'' property under physiological conditions and enhanced cellular uptake activity in response to the mild acidic tumor microenvironment due to the departure of cit. In vitro release profiles proved that the decomposition of HMDN to Mn2+ under acidic condition/high glutathione (GSH) concentration triggered the release of DOX and Fenton-like reaction for improved therapeutic effect. And Mn2+ could also act as a T1-weighted magnetic resonance imaging (MRI) contrast agent. In vivo studies further proved with both the charge reversal and combined therapy properties, DOX@HMDN-PEI-PLL(cit) showed a good tumor enrichment ability and therapeutic effect with few side effects to the mice. These results demonstrate that DOX@HMDN-PEI-PLL(cit) nanoparticles are promising drug delivery systems for targeted cancer therapy. STATEMENT OF SIGNIFICANCE: Traditional chemotherapy based on anticancer drugs such as doxorubicin hydrochloride (DOX) shows limited efficacy with serious side effects. We employed hollow manganese dioxide nanoparticle (HMDN) to loaded DOX and coated it with polyethyleneimine and then citraconic anhydride functionalized poly-L-lysine to endow it with a charge reversal property to obtain a multi-stimuli responsive drug delivery system named DOX@HMDN-PEI-PLL(cit). It was ''stealthy'' with low cellular uptake capability by normal cells, but could be "acid-activated" in tumors for endocytosis by cancer cells to reduce side effects. HMDN could be decomposed to Mn2+ under acidic conditions/high glutathione concentration to release DOX intracellular. DOX and Mn2+ catalyzed Fenton-like reaction could achieve a combined chemo-chemodynamic therapy. And Mn2+ could be used for T1-weighted magnetic resonance imaging.

Multiple Kernel k-Means Clustering by Selecting Representative Kernels.

To cluster data that are not linearly separable in the original feature space, k -means clustering was extended to the kernel version. However, the performance of kernel k -means clustering largely depends on the choice of the kernel function. To mitigate this problem, multiple kernel learning has been introduced into the k -means clustering to obtain an optimal kernel combination for clustering. Despite the success of multiple kernel k -means clustering in various scenarios, few of the existing work update the combination coefficients based on the diversity of kernels, which leads to the result that the selected kernels contain high redundancy and would degrade the clustering performance and efficiency. We resolve this problem from the perspective of subset selection in this article. In particular, we first propose an effective strategy to select a diverse subset from the prespecified kernels as the representative kernels, and then incorporate the subset selection process into the framework of multiple k -means clustering. The representative kernels can be indicated as a significant combination weights. Due to the nonconvexity of the obtained objective function, we develop an alternating minimization method to optimize the combination coefficients of the selected kernels and the cluster membership alternatively. In particular, an efficient optimization method is developed to reduce the time complexity of optimizing the kernel combination weights. Finally, extensive experiments on benchmark and real-world data sets demonstrate the effectiveness and superiority of our approach in comparison with existing methods.

Characterizing microRNAs and their targets in different organs of Camellia sinensis var. assamica.

To comprehensively annotate miRNAs and their targets in tea plant, Camellia sinensis, we sequenced small and messenger RNAs of 9 samples of Camellia sinensis var. assamica (YK-10), a diploid elite cultivar widely grown in southwest China. In order to identify targets of miRNAs, we sequenced two degradome sequencing profiles from leaves and roots of YK-10, respectively. By analyzing the small RNA-Seq profiles, we newly identified 137 conserved miRNAs and 23 species specific miRNAs in the genome of YK-10, which significantly improved the annotation of miRNAs in tea plant. Approximately 2000 differently expressed genes were identified when comparing RNA-Seq profiles of any two of the three organs selected in the study. Totally, more than 5000 targets of conserved miRNAs were identified in the two degradome profiles. Furthermore, our results suggest that a few miRNAs play roles in the biosynthesis pathways of theanine, caffeine and flavonoid. These results enhance our understanding of small RNA guided gene regulations in different organs of tea plant.

Effect of Huaier on Melanoma Invasion, Metastasis, and Angiogenesis.

Malignant melanoma (MM) is a highly metastatic and malignant cancer. Developing potential drugs with good efficacy and low toxicity for MM treatment is needed. Huaier, extracted from the mushroom Trametes robiniophila Murr, has been widely used in clinical anticancer treatments in China. A previous work done by our group confirmed that Huaier could inhibit cell proliferation and induce apoptosis in human melanoma cells. The current study is aimed at investigating the effects of Huaier on melanoma metastasis and angiogenesis in vitro and in vivo and to explore its possible mechanism of action. Our results showed that Huaier not only significantly inhibited the metastasis of A375 cells at the concentration ranging from 4 to 16 mg/ml (P < 0.05), which were determined by the wound healing assay and transwell assay in vitro, but also suppressed the MM tumor growth and metastatic cells to the liver in A375-bearing mice after oral administration at the dose of 5 mg/kg (P < 0.05). In addition, Huaier treatment downregulated the expression of hypoxia-inducible factor-1α (HIF-1α), vascular endothelial growth factor (VEGF), astrocyte-elevated gene-1 (AEG-1), and N-cadherin, while it upregulated E-cadherin expression in both the A375 cells and tumor tissues, which was detected using western blotting and RT-PCR (P < 0.05). Taken together, our data suggests that the antitumor and antimetastatic activities of Huaier are caused by the downregulation of the HIF-1α/VEGF and AEG-1 signaling pathways and by the inhibition of epithelial-mesenchymal transition (EMT). This study provides a new insight into the mechanism of Huaier on antimetastatic therapy and a new scientific basis for comprehensive treatment strategies for MM.

A novel intratumoral pH/redox-dual-responsive nanoplatform for cancer MR imaging and therapy.

The integration of diagnostic and therapeutic functions in a nanoplatform has been a rapidly emerging method in the management of cancer. The application of imaging technology paves the way to track the pharmacokinetics of the nanoplatforms, to guide the treatment, and to monitor the therapeutic processes and outcomes. Herein, we reported a novel type of monodisperses mesoporous silica-coated superparamagnetic iron oxide-based multifunctional nanoplatform (DOX@MMSN-SS-PEI-cit) for the diagnosis and treatment of cancer. The fabrication process included the surface modification of monodisperses mesoporous silica nanoparticle (MMSN) with branched polyethyleneimine (PEI) via disulfide bonds and the further coupling of citraconic anhydride to PEI. Typically, the hydrolysis of amide bonds in the tumor microenvironment (TME) could lead to a negative-to-positive charge reversion, which can enhance the endosomal escape of the resulting nanoplatform. The rapid release of doxorubicin hydrochloride (DOX) directly killed the cancer cells. Due to the superparamagnetic iron oxide-based high-resolution T2-weighted MR imaging contrast agents, this novel multifunctional nanoplatform successfully realized MR imaging, targeted drug delivery and controlled release in one system, and achieved significant improvement in tumor diagnosis and therapy. In summary, the therapeutic nanoplatform is a promising option in precise cancer treatment.

Host-guest fabrication of dual-responsive hyaluronic acid/mesoporous silica nanoparticle based drug delivery system for targeted cancer therapy.

In this paper, a targeting hyaluronic acid (HA)/mesoporous silica nanoparticle (MSN) based drug delivery system (DDS) with dual-responsiveness was prepared for cancer therapy. To avoid the side reaction between the anti-cancer drug doxorubicin hydrochloride (DOX) and HA, host-guest interaction was applied to fabricate the DDS named DOX@MSN-SS-N=C-HA. The "nanocontainer" MSN was modified with benzene ring via both pH-sensitive benzoic imine bond and redox-sensitive disulfide linkage. When DOX was loaded in the pores of MSN, the channels were then capped by the "gatekeeper" ß-CD grafted HA (HA-g-CD) through host-guest interaction between ß-CD and benzene. HA endowed the drug carriers with the targeting capability in CD44 over-expressed cancer cells. After cellular uptake, the carriers could rapidly release DOX for cell apoptosis due to both the hydrolysis of benzoic imine bond at low pH and the cleavage of disulfide bond at a high concentration of glutathione (GSH) intracellular. In vitro drug release studies and in vitro cytotoxicity studies were taken to investigate the dual-responsiveness of the carriers. And the CD44-receptor mediated cancer cell targeting capability was investigated as well. In conclusion, the targeted dual-responsive complex DDS fabricated through host-guest interaction has promising potential in cancer therapy.

Multiclass Probabilistic Classification Vector Machine.

The probabilistic classification vector machine (PCVM) synthesizes the advantages of both the support vector machine and the relevant vector machine, delivering a sparse Bayesian solution to classification problems. However, the PCVM is currently only applicable to binary cases. Extending the PCVM to multiclass cases via heuristic voting strategies such as one-vs-rest or one-vs-one often results in a dilemma where classifiers make contradictory predictions, and those strategies might lose the benefits of probabilistic outputs. To overcome this problem, we extend the PCVM and propose a multiclass PCVM (mPCVM). Two learning algorithms, i.e., one top-down algorithm and one bottom-up algorithm, have been implemented in the mPCVM. The top-down algorithm obtains the maximum a posteriori (MAP) point estimates of the parameters based on an expectation-maximization algorithm, and the bottom-up algorithm is an incremental paradigm by maximizing the marginal likelihood. The superior performance of the mPCVMs, especially when the investigated problem has a large number of classes, is extensively evaluated on the synthetic and benchmark data sets.