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Salvia miltiorrhiza and Panax notoginseng were both considered to be beneficial to cardiovascular diseases in traditional Chinese medicine and often used in combination. To examine the possible interaction between them, the effects of the active fractions of these two herbs, salvianolic acids (SA) and notoginsengnosides (NG), on platelet aggregation were checked respectively or in combination in vitro and in vivo. Both the platelet aggregation of platelet rich plasma (PRP) and washed platelet after ADP induction were checked. In vitro study showed that both SA and NG had an inhibitory effect on platelet aggregation. However, there is no synergistic effect of the combination of SA and NG in vitro. In vivo study showed that i.g. 550 mg/kg/day SA or NG for 5 days could significantly inhibit ADP-induced platelet aggregation of PRP. Moreover, combination of SA and NG at a ratio of 5:1 had a synergistic effect on platelet aggregation of PRP. The mechanism for the synergism of SA and NG in vivo was not clear. High performance liquid chromatography analysis of the plasma of rats received SA, NG or combination of SA and NG showed that co-administration of NG caused change in the plasma distribution profile of SA. The influence of combination on the absorption and/or metabolism of SA may be one of the reasons for the synergism of SA and NG in vivo.

Three-dimensionally (3D) cultured tumor cells (spheroids) exhibit more resistance to therapeutic agents than the cells cultured in traditional two-dimensional (2D) system (monolayers). We previously demonstrated that arsenic disulfide (As₂${\text{S}}_2)$ exerted significant anticancer efficacies in both 2D- and 3D-cultured MCF-7 cells, whereas 3D spheroids were shown to be resistant to the As₂S₂ treatment. L-buthionine-(S, R)-sulfoximine (BSO), an inhibitor of glutathione (GSH) synthesis, has been regarded to be a potent candidate for combinatorial treatment due to its GSH modulation function. In the present study, we introduced BSO in combination with As₂S₂ at a low concentration to investigate the possible enhancing anticancer efficacy by the combinatorial treatment on 2D- and 3D-cultured MCF-7 cells. Our results presented for the first time that the combination of As₂S₂ and BSO exerted potent anticancer synergism in both MCF-7 monolayers and spheroids. The ${\text{IC}}_{50}$ values of As₂S₂ in combinatorial treatment were significantly lower than those in treatment of As₂S₂ alone in both 2D- and 3D-cultured MCF-7 cells ($P<0.01$, respectively). In addition, augmented induction of apoptosis and enhanced cell cycle arrest along with the regulation of apoptosis- and cell cycle-related proteins, as well as synergistic inhibitions of PI3K/Akt signals, were also observed following co-treatment of As₂S₂ and BSO. Notably, the combinatorial treatment significantly decreased the cellular GSH levels in both 2D- and 3D-cultured MCF-7 cells in comparison with each agent alone ($P<0.05$ in each). Our results suggest that the combinatorial treatment with As₂S₂ and BSO could be a promising novel strategy to reverse arsenic resistance in human breast cancer.

The present paper reviews some general aspects of the stochastic analysis performed by the author in the field of statistical physics, particularly concerning the order formation from unstable states. First, a brief review and some new results are given on the generalization of the Itô-type and Stratonovich-type stochastic integrals. Their physical meaning is also discussed form the viewpoint of symmetry. Secondly, Kubo's stochastic Liouville equation is presented from the viewpoint of separation of procedures, to give a simple derivation of the Fokker–Planck equation. Thirdly, the scaling theory of order formation from the unstable point is re-formulated by introducing here a new order parameter to characterize macroscopic order formation and to clarify the synergetic effect of the initial fluctuation, random noise and nonlinearity. Finally, some discussions are given, particularly concerning applications of the Hida calculus based on the Gelfand triplet space.

Compared with monotherapy, combination therapy is the first choice and the most promising method for the treatment of many complex diseases. Due to the wide variety of drugs, it is often difficult to choose desirable combination drugs with synergy and low risk. Additional research should always be done before combining drugs because the combinatorial effects can be synergistic, additive, or even antagonistic. Synergistic drugs work together to cause an effect greater than the sum of its parts. Some studies propose different approaches to detect synergism between two or more drugs. Based on the framework of bifurcation-based method, we propose an approach to screen another potential synergistic drug for a given drug. Different from other methods, the approach can help us screen and detect drugs which have a synergistic effect with a known drug, thus playing critical roles in combination therapy. In order to demonstrate the effectiveness of the approach, we apply it to three models, i.e. the zeroth-order reaction model, the galactose model, and the epithelial-to-mesenchymal transition network. The approach provides a theoretical basis for rational design of combination drugs and new use of old drugs.

Drug combination has become an attractive strategy against complex diseases, despite the challenges in handling a large number of possible combinations among candidate drugs. How to detect effective drug combinations and determine the dosage of each drug in the combination is still a challenging task. When regarding a drug as a perturbation, we propose a bifurcation-based approach to detect synergistic combinatorial perturbations. In the approach, parameters of a dynamical system are divided into two groups according to their responses to perturbations. By combining two parameters chosen from two groups, three types of combinations can be obtained. Synergism for different perturbation combinations can be detected by relative positions of the bifurcation curve and the isobole. The bifurcation-based approach can be used not only to detect combinatorial perturbations but also to determine their perturbation quantities. To demonstrate the effectiveness of the approach, we apply it to the epithelial-to-mesenchymal transition (EMT) network. The approach has implications for the rational design of drug combinations and other combinatorial control, e.g. combinatorial regulation of gene expression.

A four-ball tester was used to evaluate the tribological performances of bismuth naphthenate (BiNap), sulfurized isobutene (VSB), and their combinations. The results show that the antiwear properties of BiNap and VSB are not very visible, but they possess good extreme pressure (EP) properties, particularly sulfur containing bismuth additives. Synergistic EP properties of BiNap with various sulfur-containing additives were investigated. The results indicate that BiNap exhibits good EP synergism with sulfur-containing additives. The surface analytical tools, such as X-ray photoelectron spectrometer (XPS) scanning electron microscope (SEM) and energy dispersive X-ray (EDX), were used to investigate the topography, composition contents, and depth profile of some typical elements on the rubbing surface. Smooth topography of wear scar further confirms that the additive showed good EP capacities, and XPS and EDX analyzes indicate that tribochemical mixed protective films composed of bismuth, bismuth oxides, sulfides, and sulfates are formed on the rubbing surface, which improves the tribological properties of lubricants. In particular, a large number of bismuth atoms and bismuth sulfides play an important role in improving the EP properties of oils.

Combination drug therapy is considered a better treatment option for various diseases, such as cancer, HIV, hypertension, and infections as compared to targeted drug therapies. Combination or synergism helps to overcome drug resistance, reduction in drug toxicity and dosage. Considering the complexity and heterogeneity among cancer types, drug combination provides promising treatment strategy. Increase in drug combination data raises a challenge for developing a computational approach that can effectively predict drugs synergism. There is a need to model the combination drug screening data to predict new synergistic drug combinations for successful cancer treatment. In such a scenario, machine learning approaches can be used to alleviate the process of drugs synergy prediction. Experimental data from a single-agent or multi-agent drug screens provides feature data for model training. On the contrary, identification of effective drug combination using clinical trials is a time consuming and resource intensive task. This paper attempts to address the aforementioned challenges by developing a computational approach to effectively predict drug synergy. Single-drug efficacy is used for predicting drug synergism. Our approach obviates the need to understand the underlying drug mechanism to predict drug combination synergy. For this purpose, nine machine learning algorithms are trained. It is observed that the Random forest models, in comparison to other models, have shown significant performance. The $K$-fold cross-validation is performed to evaluate the robustness of the best predictive model. The proposed approach is applied to mutant-BRAF melanoma and further validated using melanoma cell-lines from AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge dataset.

Xenograft model is a common in vivo model in cancer research, where human cancer (e.g., sliced tumor tissue blocks, or tumor cells) are grafted and grown in severe combined immunodeficient (scid) nude mice. In cancer drug development, demonstrated anti-tumor activity in this model is an important step to bring a promising experimental treatment to human. These experiments provide important data on the mechanism of action of the drug and for the design of future clinical trials. For therapy with single agent, the experimental design and sample size formulae are quite well established. However, cancer therapy typically involves combination of multiple agents. Such studies should be optimally designed, so that with moderate sample size, the joint action of two drugs can be estimated and the best combinations identified. A typical outcome variable in these experiments is tumor volume measured over a period of time. The resulting data have several unique features. Since a mouse may die during the experiment or may be sacrificed when its tumor volume quadruples, then incomplete repeated measurements arise. The incompleteness or missingness is also caused by drastic tumor shrinkage (<0.01 cm³) or random truncation. In addition, if no treatment were given to the tumor-bearing mice, the tumors would keep growing until the mice die or are sacrificed. This intrinsic growth of tumor in the absence of treatment constrains the parameters in the regression and causes further difficulties in statistical analysis. This chapter reviews the current methods of experimental design and data analysis for xenograft experiments. We describe the optimal experimental design for combination studies in xenograft models and likelihood-based methods for estimating the dose-response relationship while accounting for the special features of data such as informative censoring and model parameter constraints.