[Protective effect of total flavonoids of epimedium on the kidney in experimental diabetic rats].

OBJECTIVE: To investigate the influence of total flavonoids of epimedium (TFE) on the streptozocin (STZ)-induced kidney injury in diabetic rats and discuss the possible mechanism. METHODS: Diabetes was produced by a single injection of streptozocin (40 mg/kg, iv) in male SD rats. The rats were randomly divided into three groups (n = 10): control group, model group and TFE group (100 mg/kg, ig). Animals were sacrificed 12 weeks later. The level of blood glucose, blood urea nitrogen (BUN) and creatinine (Cr) as well as the renal index were determined. Detect the specific biochemical of renal tissue: superoxide dismutase (SOD), malondialdehyde (MDA). Use masson staining to observe the morphology of the renal tissue. Immunohistochemistry was employed to determine the protein levels of transforming growth factor-beta1 (TGF-beta1). RESULTS: Compared to control group, the enhancement of blood glucose, renal index, BUN and Cr was found in model group, which was significantly attenuated by treatment with TFE. Meanwhile, elevated MDA level in renal tissue as well as decreased SOD activities in renal tissue were significantly remitted by TFE. Furthermore, TFE decreased the expression of TGF-beta1. CONCLUSION: TFE can evidently relieve renal damage in rats with diabetic nephropathy induced by STZ, which might be related to antioxidation and modulating the expression of TGF-beta1 protein.

The drug cocktail network.

BACKGROUND: Combination of different agents is widely used in clinic to combat complex diseases with improved therapy and reduced side effects. However, the identification of effective drug combinations remains a challenging task due to the huge number of possible combinations among candidate drugs that makes it impractical to screen putative combinations. RESULTS: In this work, we construct a 'drug cocktail network' using all the known effective drug combinations extracted from the Drug Combination Database (DCDB), and propose a network-based approach to investigate drug combinations. Our results show that the agents in an effective combination tend to have more similar therapeutic effects and share more interaction partners. Based on our observations, we further develop a statistical approach termed as DCPred (Drug Combination Predictor) to predict possible drug combinations by exploiting the topological features of the drug cocktail network. Validating on the known drug combinations, DCPred achieves the overall AUC (Area Under the receiver operating characteristic Curve) score of 0.92, indicating the predictive power of our proposed approach. CONCLUSIONS: The drug cocktail network constructed in this work provides useful insights into the underlying rules of effective drug combinations and offer important clues to accelerate the future discovery of new drug combinations.

Exploring drug combinations in genetic interaction network.

BACKGROUND: Drug combination that consists of distinctive agents is an attractive strategy to combat complex diseases and has been widely used clinically with improved therapeutic effects. However, the identification of efficacious drug combinations remains a non-trivial and challenging task due to the huge number of possible combinations among the candidate drugs. As an important factor, the molecular context in which drugs exert their functions can provide crucial insights into the mechanism underlying drug combinations. RESULTS: In this work, we present a network biology approach to investigate drug combinations and their target proteins in the context of genetic interaction networks and the related human pathways, in order to better understand the underlying rules of effective drug combinations. Our results indicate that combinatorial drugs tend to have a smaller effect radius in the genetic interaction networks, which is an important parameter to describe the therapeutic effect of a drug combination from the network perspective. We also find that drug combinations are more likely to modulate functionally related pathways. CONCLUSIONS: This study confirms that the molecular networks where drug combinations exert their functions can indeed provide important insights into the underlying rules of effective drug combinations. We hope that our findings can help shortcut the expedition of the future discovery of novel drug combinations.

[Quantity and distribution of CD44+/CD24- cells in breast cancer tissue and the cell lines].

OBJECTIVE: To study the distribution and quantity of CD44+/CD24- cells in breast cancer tissue and the cell lines, and as well as its correlation with the expression of various breast cancer markers and molecular subtyping of breast carcinoma. METHODS: The expression of CD44/CD24, estrogen receptor, progesterone receptor, HER2, human estrogen-induced protein PS2, bcl-2 and nm23 in 60 cases of invasive ductal carcinoma of breast were studied by either single or double immunohistochemical staining. The co-expression of CD44 and CD24 in 3 breast cancer cell lines (MCF-7, MDA-MB-468, and MDA-MB-231) was also examined. RESULTS: The quantity and distribution of CD44+/CD24- cells varied greatly and no specific patterns were identified. The percentage of CD44+/CD24- in breast cancer was 65%. The amount of CD44+/CD24- cells did not correlate with the age of patients, lymph node metastasis, tumor size, molecular subtypes and expression of various breast cancer markers in breast carcinoma. The proportion of CD44+/CD24- cells in MCF-7, MDA-MB-468, and MDA-MB-231 cell lines was <1%, 5% and >80%, respectively. CONCLUSIONS: CD44+/CD24- cells are demonstrated in certain breast cancer tissues and cell lines. However, there is no relationship obtained between the quantity or the distribution of these cells and the molecular subtyping or the clinicopathologic parameters in breast cancer.