BackgroundRevealing protein complexes are important for understanding principles of cellular organization and function. High-throughput experimental techniques have produced a large amount of protein interactions, which makes it possible to predict protein complexes from protein-protein interaction (PPI) networks. However, the small amount of known physical interactions may limit protein complex detection.MethodsThe new PPI networks are constructed by integrating PPI datasets with the large and readily available PPI data from biomedical literature, and then the less reliable PPI between two proteins are filtered out based on semantic similarity and topological similarity of the two proteins. Finally, the supervised learning protein complex detection (SLPC), which can make full use of the information of available known complexes, is applied to detect protein complex on the new PPI networks.ResultsThe experimental results of SLPC on two different categories yeast PPI networks demonstrate effectiveness of the approach: compared with the original PPI networks, the best average improvements of 4.76, 6.81 and 15.75 percentage units in the F-score, accuracy and maximum matching ratio (MMR) are achieved respectively; compared with the denoising PPI networks, the best average improvements of 3.91, 4.61 and 12.10 percentage units in the F-score, accuracy and MMR are achieved respectively; compared with ClusterONE, the start-of the-art complex detection method, on the denoising extended PPI networks, the average improvements of 26.02 and 22.40 percentage units in the F-score and MMR are achieved respectively.ConclusionsThe experimental results show that the performances of SLPC have a large improvement through integration of new receivable PPI data from biomedical literature into original PPI networks and denoising PPI networks. In addition, our protein complexes detection method can achieve better performance than ClusterONE.

BackgroundLiquid-state specimen carriers are inadequate for sample transportation in large-scale screening projects in low-resource settings, which necessitates the exploration of novel non-hazardous solid-state alternatives. Studies investigating the feasibility and accuracy of a solid-state human papillomavirus (HPV) sampling medium in combination with different down-stream HPV DNA assays for cervical cancer screening are needed.MethodsWe collected two cervical specimens from 396 women, aged 25–65 years, who were enrolled in a cervical cancer screening trial. One sample was stored using DCM preservative solution and the other was applied to a Whatman Indicating FTA Elute® card (FTA card). All specimens were processed using three HPV testing methods, including Hybrid capture 2 (HC2), careHPV™, and Cobas®4800 tests. All the women underwent a rigorous colposcopic evaluation that included using a microbiopsy protocol.ResultsCompared to the liquid-based carrier, the FTA card demonstrated comparable sensitivity for detecting high grade Cervical Intraepithelial Neoplasia (CIN) using HC2 (91.7 %), careHPV™ (83.3 %), and Cobas®4800 (91.7 %) tests. Moreover, the FTA card showed a higher specificity compared to a liquid-based carrier for HC2 (79.5 % vs. 71.6 %, P = 0.015), comparable specificity for careHPV™ (78.1 % vs. 73.0 %, P > 0.05), but lower specificity for the Cobas®4800 test (62.4 % vs. 69.9 %, P = 0.032). Generally, the FTA card-based sampling medium’s accuracy was comparable with that of liquid-based medium for the three HPV testing assays.ConclusionsFTA cards are a promising sample carrier for cervical cancer screening. With further optimization, it can be utilized for HPV testing in areas of varying economic development.

BackgroundN-myc down-regulated gene 2 (NDRG2) is a tumor suppressor involved in cell proliferation and differentiation. The aim of this study was to determine the uterine expression pattern of this gene during early pregnancy in mice.MethodsUterine NDRG2 mRNA and protein expression levels were determined by RT-PCR and Western blot analyses, respectively, during the peri-implantation period in mice. Immunohistochemical (IHC) analysis was performed to examine the spatial localization of NDRG2 expression in mouse uterine tissues. The in vitro decidualization model of mouse endometrial stromal cells (ESCs) was used to evaluate decidualization of ESCs following NDRG2 knock down by small interfering RNA (siRNA). Statistical significance was analyzed by one-way ANOVA using SPSS 19.0 software.ResultsUterine NDRG2 gene expression was significantly up-regulated and was predominantly localized to the secondary decidual zone on days 5 and 8 of pregnancy in mice. Its increased expression was associated with artificial decidualization as well as the activation of delayed implantation. Furthermore, uterine NDRG2 expression was induced by estrogen and progesterone treatments. The in vitro decidualization of mouse ESCs was accompanied by up-regulation of NDRG2 expression, and knock down of its expression in these cells by siRNA inhibited the decidualization process.ConclusionsThese results suggest that NDRG2 might play an important role in the process of decidualization during early pregnancy.

BackgroundLiquid-state specimen carriers are inadequate for sample transportation in large-scale screening projects in low-resource settings, which necessitates the exploration of novel non-hazardous solid-state alternatives. Studies investigating the feasibility and accuracy of a solid-state human papillomavirus (HPV) sampling medium in combination with different down-stream HPV DNA assays for cervical cancer screening are needed.MethodsWe collected two cervical specimens from 396 women, aged 25–65 years, who were enrolled in a cervical cancer screening trial. One sample was stored using DCM preservative solution and the other was applied to a Whatman Indicating FTA Elute® card (FTA card). All specimens were processed using three HPV testing methods, including Hybrid capture 2 (HC2), careHPV™, and Cobas®4800 tests. All the women underwent a rigorous colposcopic evaluation that included using a microbiopsy protocol.ResultsCompared to the liquid-based carrier, the FTA card demonstrated comparable sensitivity for detecting high grade Cervical Intraepithelial Neoplasia (CIN) using HC2 (91.7 %), careHPV™ (83.3 %), and Cobas®4800 (91.7 %) tests. Moreover, the FTA card showed a higher specificity compared to a liquid-based carrier for HC2 (79.5 % vs. 71.6 %, P = 0.015), comparable specificity for careHPV™ (78.1 % vs. 73.0 %, P > 0.05), but lower specificity for the Cobas®4800 test (62.4 % vs. 69.9 %, P = 0.032). Generally, the FTA card-based sampling medium’s accuracy was comparable with that of liquid-based medium for the three HPV testing assays.ConclusionsFTA cards are a promising sample carrier for cervical cancer screening. With further optimization, it can be utilized for HPV testing in areas of varying economic development.

BackgroundDiabetic nephropathy is a serious complication of diabetes whose development process is associated with inflammation, renal hypertrophy, and fibrosis. Coreopsis tinctoria Nutt, traditionally used as a healthcare tea, has anti-inflammatory, anti-hyperlipidemia, and glycemic regulation activities. The aim of our study was to investigate the renal protective effect of ethyl acetate extract of C. tinctoria Nutt (AC) on high-glucose–fat diet and streptozotocin (STZ)-induced diabetic rats.MethodsA diabetic rat model was induced by high-glucose–fat diet and intraperitoneal injection of 35 mg/kg STZ. After treatment with AC at a daily dose of 150, 300 or, 600 mg/kg for 4 weeks, metabolic and renal function parameters of serum and urine were examined. Degree of renal damage, renal proinflammatory cytokines, and fibrotic protein expression were analyzed by histopathology and immunohistochemistry. Renal AMP-activated protein kinase (AMPK) and transforming growth factor (TGF)-β1/Smad signaling pathway were determined by western blotting.ResultsDiabetic rats showed obvious renal dysfunction, inflammation and fibrosis. However, AC significantly reduced levels of blood glucose, total cholesterol, triglyceride, blood urea nitrogen, serum creatinine and urinary albumin, as well as expression of kidney proinflammatory cytokines of monocyte chemoattractant protein-1 and intercellular adhesion molecule-1. AC also ameliorated renal hypertrophy and fibrosis by reducing fibronectin and collagen IV and suppressing the TGF-β1/Smad signaling pathway. Meanwhile, AMPKα as a protective cytokine was markedly stimulated by AC.ConclusionIn summary, AC controls blood glucose, inhibits inflammatory and fibrotic processes, suppresses the TGF-β1/Smad signaling pathway, and activates phosphorylation of AMPKα in the kidneys, which confirms the protective effects of AC in the early stage of diabetic kidney disease.

BackgroundRevealing protein complexes are important for understanding principles of cellular organization and function. High-throughput experimental techniques have produced a large amount of protein interactions, which makes it possible to predict protein complexes from protein-protein interaction (PPI) networks. However, the small amount of known physical interactions may limit protein complex detection.MethodsThe new PPI networks are constructed by integrating PPI datasets with the large and readily available PPI data from biomedical literature, and then the less reliable PPI between two proteins are filtered out based on semantic similarity and topological similarity of the two proteins. Finally, the supervised learning protein complex detection (SLPC), which can make full use of the information of available known complexes, is applied to detect protein complex on the new PPI networks.ResultsThe experimental results of SLPC on two different categories yeast PPI networks demonstrate effectiveness of the approach: compared with the original PPI networks, the best average improvements of 4.76, 6.81 and 15.75 percentage units in the F-score, accuracy and maximum matching ratio (MMR) are achieved respectively; compared with the denoising PPI networks, the best average improvements of 3.91, 4.61 and 12.10 percentage units in the F-score, accuracy and MMR are achieved respectively; compared with ClusterONE, the start-of the-art complex detection method, on the denoising extended PPI networks, the average improvements of 26.02 and 22.40 percentage units in the F-score and MMR are achieved respectively.ConclusionsThe experimental results show that the performances of SLPC have a large improvement through integration of new receivable PPI data from biomedical literature into original PPI networks and denoising PPI networks. In addition, our protein complexes detection method can achieve better performance than ClusterONE.