PYR demonstrated anti-tumour effects on NSCLC in vitro and in vivo, indicating its therapeutic potential against human NSCLC.

The present study was carried out to explore the effect of sinensetin in human T-cell lymphoma Jurkat cells and to reveal the underlying molecular mechanisms. We found that sinensetin significantly impeded Jurkat cell proliferation in a dose-dependent and time-dependent manner. Additionally, sinensetin treatment triggered apoptosis and autophagy in Jurkat cells. The apoptosis induction was related to a loss of mitochondrial membrane potential and to increased caspase-3/-8/-9 and poly(ADP-ribose) polymerase (PARP) cleavage. Sinensetin also induced autophagy, as evidenced by the formation of acidic vacuoles, the upregulation of LC3-II and beclin-1, and the downregulation of p62. In addition, the inhibition of autophagy by 3-methyladenine significantly enhanced the apoptosis rate and improved the sensitivity of the Jurkat cells to sinensetin. Moreover, sinensetin induced cell death, apoptosis, and autophagy through the activation of the reactive oxygen species/ c-Jun N-terminal kinase signaling pathway and the inhibition of the Akt/mTOR signaling pathways. In summary, our results revealed that sinensetin induced apoptosis and autophagy in human T-cell lymphoma Jurkat cells by activating reactive oxygen species/ c-Jun N-terminal kinase and blocking the Akt/mTOR signaling pathways. Thus, sinensetin might be a potential candidate in the development of antitumor drugs targeting T-cell leukemia. Anti-Cancer Drugs 30:485-494

Lobocrassin B, a natural cembrane-type compound isolated from the soft coral Lobophytum crassum, has been shown to have significant biological effects, including anticancer activity. As the most common cause of cancer mortality worldwide, lung cancer remains a major concern threatening human health. In the current study, we conducted in vitro experiments to demonstrate the inhibiting effect of Lobocrassin B on CL1-5 and H520 human lung cancer cells growth and to explore the underlying mechanisms, as well as in nude mice bearing CL1-5 tumor xenografts. Lobocrassin B exerted cytotoxic effects on lung cancer cells, as shown by decreasing cell viability, and inducing apoptosis, oxidative stress and mitochondrial dysfunction. In addition, the increased level of Bax, cleaved caspase-3, -9 and -8, and the suppression of Bcl-2 were observed in the Lobocrassin B treated cells. Moreover, in vivo assays verified the significance of these results, revealing that Lobocrassin B inhibited CL1-5 tumor xenograft growth and that inhibitory effects were accompanied by a marked increase in tumor cell apoptosis. In conclusion, the results suggested that Lobocrassin B could be a potential anticancer compound for its propensity to inhibit growth and induce apoptosis in human lung cancer cells.

When dealing with complex entrepreneurial network problems, such as sustainable resource flows, the highly uncertainty in environment that brings cognitive bias in entrepreneurs’ decision-making means which entrepreneurs who are expert in using the entrepreneurial network can acquire sustainable resources by reducing external interference. To answer a link decision problem of the role played by network features in the entrepreneurial process of resource acquisition, we introduce an exploratory model design by the Naïve Bayesian classification with EM (Expectation Maximization) algorithm based on SNA (Social Network Analysis) theory that is focused on filling the missing data of uncertainty, in order to describe the path of entrepreneurial network resources acquisition. An inter-dynamic model has established between network structure and the value of resources to predict linking probabilities. By expectation-maximization method for Naïve Bayesian, the paper concludes with an empirical evaluation to verify the accuracy of resource acquisition prediction, in 201 entrepreneurial companies, and application in uncertain environmental network governance decision-making problem regarding the selection of optimal resource paths for creating a new company. We hope which this work can stimulate a broader research agenda focused on the impact of network structure on entrepreneurs’ decision-making under uncertainty, especially for developing countries where has a new round of entrepreneurial enthusiasm with high uncertainty.

Background: N1-methyladenosine (m1A) is a reversible post-transcriptional modification in mRNA, which has been proved to play critical roles in various biological processes through interaction with different m1A regulators. There are several m1A regulators existing in the human genome, including YTHDF1-3 and YTHDC1. Methods: Several techniques have been developed to identify the substrates of m1A regulators, but their binding specificity and biological functions are not yet fully understood due to the limitations of wetlab approaches. Here, we submitted the framework m1ARegpred (m1A regulators substrate prediction), which is based on machine learning and the combination of sequence-derived and genome-derived features. Results: Our framework achieved area under the receiver operating characteristic (AUROC) scores of 0.92 in the full transcript model and 0.857 in the mature mRNA model, showing an improvement compared to the existing sequence-derived methods. In addition, motif search and gene ontology enrichment analysis were performed to explore the biological functions of each m1A regulator. Conclusions: Our work may facilitate the discovery of m1A regulators substrates of interest, and thereby provide new opportunities to understand their roles in human bodies.