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Background: Dihydroartemisinin (DHA), a semi-synthetic derivative of artemisinin, isolated from the traditional Chinese herb Artemisia annua, is recommended as the first-line anti-malarial drug with low toxicity. DHA has been shown to possess promising anticancer activities and induce cancer cell death through apoptotic pathways, although the molecular mechanisms are not well understood.

Statistical inference on conditional dependence is essential in many fields including genetic association studies and graphical models. The classic measures focus on linear conditional correlations, and are incapable of characterizing non-linear conditional relationship including non-monotonic relationship. To overcome this limitation, we introduces a nonparametric measure of conditional dependence for multivariate random variables with arbitrary dimensions. Our measure possesses the necessary and intuitive properties as a correlation index. Briefly, it is zero almost surely if and only if two multivariate random variables are conditionally independent given a third random variable. More importantly, the sample version of this measure can be expressed elegantly as the root of a V or U-process with random kernels and has desirable theoretical properties. Based on the sample version, we propose a test for conditional independence, which is proven to be more powerful than some recently developed tests through our numerical simulations. The advantage of our test is even greater when the relationship between the multivariate random variables given the third random variable cannot be expressed in a linear or monotonic function of one random variable versus the other. We also show that the sample measure is consistent and weakly convergent, and the test statistic is asymptotically normal. By applying our test in a real data analysis, we are able to identify two conditionally associated gene expressions, which otherwise cannot be revealed. Thus, our measure of conditional dependence is not only an ideal concept, but also has important practical utility.

This report is designed to explore the exact molecular mechanism by which artesunate (ART), a semisynthetic derivative of the herbal antimalaria drug artemisinin, induces apoptosis in human lung adenocarcinoma (ASTC-a-1 and A549) cell lines. ART treatment induced ROS-mediated apoptosis in a concentration- and time-dependent fashion accompanying the loss of mitochondrial potential and subsequent release of Smac and AIF indicative of intrinsic apoptosis pathway. Blockage of casapse-8 and -9 did not show any inhibitory effect on the ART-induced apoptosis, but which was remarkably prevented by silencing AIF. Of the utmost importance, ART treatment induced the activation of Bak but not Bax, and silencing Bak but not Bax remarkably inhibited ART-induced apoptosis and AIF release. Furthermore, although ART treatment did not induced a significant down-regulation of voltage-dependent anion channel 2 (VDAC2) expression and up-regulation of Bim expression, silencing VDAC2 potently enhanced the ART-induced Bak activation and apoptosis which were significantly prevented by silencing Bim. Collectively, our data firstly demonstrate that ART induces Bak-mediated caspase-independent intrinsic apoptosis in which Bim and VDAC2 as well as AIF play important roles in both ASTC-a-1 and A549 cell lines, indicating a potential therapeutic effect of ART for lung cancer.

Lectins including flowering plant lectins, algal lectins, cyanobacterial lectins, actinomycete lectin, worm lectins, and the nonpeptidic lectin mimics pradimicins and benanomicins, exhibit anti-HIV activity. The anti-HIV plant lectins include Artocarpus heterophyllus (jacalin) lectin, concanavalin A, Galanthus nivalis (snowdrop) agglutinin-related lectins, Musa acuminata (banana) lectin, Myrianthus holstii lectin, Narcissus pseudonarcissus lectin, and Urtica diocia agglutinin. The anti-HIV algal lectins comprise Boodlea coacta lectin, Griffithsin, Oscillatoria agardhii agglutinin. The anti-HIV cyanobacterial lectins are cyanovirin-N, scytovirin, Microcystis viridis lectin, and microvirin. Actinohivin is an anti-HIV actinomycete lectin. The anti-HIV worm lectins include Chaetopterus variopedatus polychaete marine worm lectin, Serpula vermicularis sea worm lectin, and C-type lectin Mermaid from nematode (Laxus oneistus). The anti-HIV nonpeptidic lectin mimics comprise pradimicins and benanomicins. Their anti-HIV mechanisms are discussed.

Marine organisms have been extensively explored for the last several decades as potential sources of novel biologically active compounds, and extensive research has been conducted on lectins. Lectins derived from marine organisms are structurally diverse and also differ from those identified from terrestrial organisms. Marine lectins appear to be particularly useful in some biological applications. They seem to induce negligible immunogenicity because they have a relatively small size, are more stable due to extensive disulfide bridge formation, and have high specificity for complex glyco-conjugates and carbohydrates instead of simple sugars. It is clear that many of them have not yet been extensively studied when compared with their terrestrial counterparts. Marine lectins can be used to design and develop new potentially useful therapeutic agents. This review encompasses recent research on the isolation and identification of marine lectins with potential value in medicinal applications.

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In this paper, we first introduce Ball Divergence, a novel measure of the difference between two probability measures in separable Banach spaces, and show that the Ball Divergence of two probability measures is zero if and only if these two probability measures are identical without any moment assumption. Using Ball Divergence, we present a metric rank test procedure to detect the equality of distribution measures underlying independent samples. It is therefore robust to outliers or heavy-tail data. We show that this multivariate two sample test statistic is consistent with the Ball Divergence, and it converges to a mixture of χ2 distributions under the null hypothesis and a normal distribution under the alternative hypothesis. Importantly, we prove its consistency against a general alternative hypothesis. Moreover, this result does not depend on the ratio of the two imbalanced sample sizes, ensuring that can be applied to imbalanced data. Numerical studies confirm that our test is superior to several existing tests in terms of Type I error and power. We conclude our paper with two applications of our method: one is for virtual screening in drug development process and the other is for genome wide expression analysis in hormone replacement therapy.