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Background Some proposed methods for identifying essential proteins have better results by using biological information. Gene expression data is generally used to identify essential proteins. However, gene expression data is prone to fluctuations, which may affect the accuracy of essential protein identification. Therefore, we propose an essential protein identification method based on gene expression and the PPI network data to calculate the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network. Our experiments show that the method can improve the accuracy in predicting essential proteins. Results In this paper, we propose a new measure named JDC, which is based on the PPI network data and gene expression data. The JDC method offers a dynamic threshold method to binarize gene expression data. After that, it combines the degree centrality and Jaccard similarity index to calculate the JDC score for each protein in the PPI network. We benchmark the JDC method on four organisms respectively, and evaluate our method by using ROC analysis, modular analysis, jackknife analysis, overlapping analysis, top analysis, and accuracy analysis. The results show that the performance of JDC is better than DC, IC, EC, SC, BC, CC, NC, PeC, and WDC. We compare JDC with both NF-PIN and TS-PIN methods, which predict essential proteins through active PPI networks constructed from dynamic gene expression. Conclusions We demonstrate that the new centrality measure, JDC, is more efficient than state-of-the-art prediction methods with same input. The main ideas behind JDC are as follows: (1) Essential proteins are generally densely connected clusters in the PPI network. (2) Binarizing gene expression data can screen out fluctuations in gene expression profiles. (3) The essentiality of the protein depends on the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network.

Acute kidney injury (AKI) is a common clinical problem and an efficacious treatment is lacking. Ferroptosis, a newly discovered type of programmed cell death, has been reported to alleviate renal tubular injury in ischemia/reperfusion-induced acute kidney injury (I/R-AKI). Entacapone is a specific inhibitor of catechol-O-methyltransferase, which is used as an adjuvant drug against Parkinson's disease. We demonstrated that entacapone prevents renal I/R injury by inhibiting ferroptosis. Compared with a sham group, entacapone treatment mitigated I/R-induced pathological alterations, improved renal function, and inhibited ferroptosis. In HK-2 cells, entacapone treatment significantly reduced the lipid peroxidation and iron accumulation induced by the ferroptosis inducers erastin and RSL3, and significantly regulated expression of ferroptosis-related proteins. Entacapone upregulates p62 expression and affects the p62-KEAP1-NRF2 pathway, thereby upregulating nuclear translocation of NRF2. This action results in increased expression of the downstream SLC7A11, and significant suppression of oxidative stress and ferroptosis. Our results identify entacapone as a ferroptosis inhibitor that enhances antioxidant capacity. Entacapone may serve as a novel strategy to improve treatment of, and recovery from, I/R-AKI.

Global knockout of the BK channel has been proven to affect bone formation; however, whether it directly affects osteoblast differentiation and the mechanism are elusive. In the current study, we further investigated the role of BK channels in bone development and explored whether BK channels impacted the differentiation and proliferation of osteoblasts via the canonical Wnt signaling pathway. Our findings demonstrated that knockout of Kcnma1 disrupted the osteogenesis of osteoblasts and inhibited the stabilization of β-catenin. Western blot analysis showed that the protein levels of Axin1 and USP7 increased when Kcnma1 was deficient. Together, this study confirmed that BK ablation decreased bone mass via the Wnt/β-catenin signaling pathway. Our findings also showed that USP7 might have the ability to stabilize the activity of Axin1, which would increase the degradation of β-catenin in osteoblasts.

A d-peptide ligand of the nicotine acetylcholine receptors (nAChRs), termed DCDX, enables drug delivery to the brain when incorporated into liposomes and has shown promise as a nanocarrier for treating brain diseases. However, few reports have described the mechanisms whereby DCDX-modified liposomes traverse the blood-brain barrier (BBB). Here, we studied the molecular mechanisms enabling DCDX (and its associated liposomes) to cross an in vitro BBB using a simulated cerebral endothelium monolayer formed by brain capillary endothelial cells (bEnd.3 cells). We also examined the mechanisms whereby DCDX-modified liposomes cross the BBB in vivo using the brain efflux-index method. Transport of DCDX and its modified liposomes was dominantly mediated via the lipid raft/caveolae endocytic pathway. Both the endoplasmic reticulum (ER) and Golgi complex participated in delivering DCDX-modified liposomes to the plasma membrane (PM). DCDX-modified liposomes also participated in the endosome/lysosome pathway (with high-efficiency BBB crossing observed in vitro), while competing for the ER/Golgi/PM pathway. In addition, nAChR α7 did not promote the transportation of DCDX-modified liposomes in vivo or in vitro, as assessed with α7-knockout mice and by performing α-bungarotoxin (α-Bgt) binding-competition experiments. P-glycoprotein (P-gp) was identified as the main efflux transporter across the BBB, in vivo and in vitro. Using a xenograft nude mouse model of human glioblastoma multiforme, blocking the efflux function of P-gp with verapamil enhanced the therapeutic efficiency of DCDX-modified liposomes that were formulated with doxorubicin against glioblastoma. The findings of this study reveal novel mechanisms underlying crossing of the BBB by DCDX-modified liposomes, suggesting that DCDX-modified liposomes can potentially serve as a powerful therapeutic tool for treating glioma.

[This corrects the article DOI: 10.1371/journal.pone.0213668.].

BACKGROUND Pattern mining utilizes multiple algorithms to explore objective and sometimes unexpected patterns in real-world data. This technique could be applied to electronic medical record data mining; however, it first requires a careful clinical assessment and validation. OBJECTIVE The aim of this study was to examine the use of pattern mining techniques on a large clinical dataset to detect treatment and medication use patterns for childhood pneumonia. METHODS We applied 3 pattern mining algorithms to 680,138 medication administration records from 30,512 childhood inpatients with diagnosis of pneumonia during a 6-year period at a children’s hospital in China. Patients’ ages ranged from 0 to 17 years, where 37.53% (11,453/30,512) were 0 to 3 months old, 86.55% (26,408/30,512) were under 5 years, 60.37% (18,419/30,512) were male, and 60.10% (18,338/30,512) had a hospital stay of 9 to 15 days. We used the FP-Growth, PrefixSpan, and USpan pattern mining algorithms. The first 2 are more traditional methods of pattern mining and mine a complete set of frequent medication use patterns. PrefixSpan also incorporates an administration sequence. The newer USpan method considers medication utility, defined by the dose, frequency, and timing of use of the 652 individual medications in the dataset. Together, these 3 methods identified the top 10 patterns from 6 age groups, forming a total of 180 distinct medication combinations. These medications encompassed the top 40 (73.66%, 500,982/680,138) most frequently used medications. These patterns were then evaluated by subject matter experts to summarize 5 medication use and 2 treatment patterns. RESULTS We identified 5 medication use patterns: (1) antiasthmatics and expectorants and corticosteroids, (2) antibiotics and (antiasthmatics or expectorants or corticosteroids), (3) third-generation cephalosporin antibiotics with (or followed by) traditional antibiotics, (4) antibiotics and (medications for enteritis or skin diseases), and (5) (antiasthmatics or expectorants or corticosteroids) and (medications for enteritis or skin diseases). We also identified 2 frequent treatment patterns: (1) 42.89% (291,701/680,138) of specific medication administration records were of intravenous therapy with antibiotics, diluents, and nutritional supplements and (2) 11.53% (78,390/680,138) were of various combinations of inhalation of antiasthmatics, expectorants, or corticosteroids. Fleiss kappa for the subject experts’ evaluation was 0.693, indicating moderate agreement. CONCLUSIONS Utilizing a pattern mining approach, we summarized 5 medication use patterns and 2 treatment patterns. These warrant further investigation.

Essential proteins as a vital part of maintaining the cells’ life play an important role in the study of biology and drug design. With the generation of large amounts of biological data related to essential proteins, an increasing number of computational methods have been proposed. Different from the methods which adopt a single machine learning method or an ensemble machine learning method, this paper proposes a predicting framework named by XGBFEMF for identifying essential proteins, which includes a SUB-EXPAND-SHRINK method for constructing the composite features with original features and obtaining the better subset of features for essential protein prediction, and also includes a model fusion method for getting a more effective prediction model. We carry out experiments on Yeast data to assess the performance of the XGBFEMF with ROC analysis, accuracy analysis, and top analysis. Meanwhile, we set up experiments on E. coli data for the validation of performance. The test results show that the XGBFEMF framework can effectively improve many essential indicators. In addition, we analyze each step in the XGBFEMF framework; our results show that both each step of the SUB-EXPAND-SHRINK method as well as the step of multi-model fusion can improve prediction performance.

Biobanks are important resources and central tools for translational medicine, which brings scientific research outcomes to clinical practice. The key purpose of biobanking in translational medicine and other medical research is to provide biological samples that are integrated with clinical information. In 2008, the Shanghai Municipal Government launched the "Shanghai Tissue Bank" in an effort to promote research in translational medicine. Now a sharing service platform has been constructed to integrate clinical practice and biological information that can be used in diverse medical and pharmaceutical research studies. The platform collects two kinds of data: sample data and clinical data. The sample data are obtained from the hospital biobank management system, and mainly include the donors' age, gender, marital status, sample source, sample type, collection time, deposit time, and storage method. The clinical data are collected from the "Hospital-Link" system (a medical information sharing system that connects 23 tertiary hospitals in Shanghai). The main contents include donors' corresponding medication information, test reports, inspection reports, and hospital information. As of the end of September 2014, the project has a collection of 16,020 donors and 148,282 samples, which were obtained from 12 medical institutions, and automatically acquired donors' corresponding clinical data from the "Hospital-Link" system for 6830 occurrences. This project will contribute to scientific research at medical institutions in Shanghai, and will also support the development of the biopharmaceutical industry. In this article, we will describe the significance, the construction phases, the application prospects, and benefits of the sample repository and information sharing service platform.

Visual servoing (VS) is an automatic control technique which uses the feedback of visual information to guide the robot motion to converge to the desired location. This paper proposes new moment-based features for robust VS with spheres, especially in cameras obeying the unified model. Specifically, we introduce a virtual plane to reload contours of image projections of spheres, in the process of which image noises will however be greatly magnified and induce large converge error. To solve this, new moment-based features are proposed and extracted on this virtual plane to enhance the robustness of VS to calibration errors and image noises. Simulation results verify the effectiveness of the proposed scheme and demonstrate the high robustness of these set of new features to image noises. The altering trends of converge errors versus increasing noise levels are randomly tested to present the obvious advantage of new moment-based features over the usual ones. At last, a path-planning based VS application is performed with fisheye views of some spheres, benefiting from the robustness and an interpolation-based tracking scheme.

Moment-based features have been intensively explored in visual servoing (VS) applications, especially when lack of representational rigid points like in the bottle production line. Convergence and many other constraints exist in VS with image moments have seldom been simultaneously considered in the literature. This paper takes bottle-shape like cylinders as a target and proposes a set of new moment-based features of cylinders for global convergence in VS application. Building on convergence to the desired location, we plan a straight path from the current camera frame to the desired one and formulate camera rotation into quadratic polynomials of quaternion representation with some variables. A nonlinear minimization method is proposed to reassign these variables for meeting field of view (FOV) limits of these cylinders. In order to follow well this planned straight line, we propose an image-based tracking scheme. This paper belongs to a series of work aiming at robust VS based on image moments of some primitives. Simulation with two parallel cylinders and two crossed cylinders show the advantage of the proposed features and validate our proposed algorithm for both planning and tracking.

Background Renal interstitial fibrosis is a common pathway through which chronic kidney disease progresses to end-stage renal disease. There are currently no effective drugs available to treat kidney fibrosis, so traditional medicine is likely to be a candidate. The therapeutic potential of saikosaponin B2 (SSB2), a biologically active ingredient of Radix Bupleuri, on renal fibrosis has not been reported. Methods A unilateral ureteral obstruction (UUO) model was conducted to induce renal interstitial fibrosis in mice. SSB2’s effect was valuated by histological staining and exploring the changes in expression of relative proteins and mRNAs. A conditional medium containing sonic hedgehog variant protein stimulating normal rat kidney interstitial fibroblast cells (NRK-49F) was used in an in vitro model to determine the possible mechanism. The molecular target of SSB2 was verified using several mutation plasmids. Results SSB2 administration reduced kidney injury and alleviated interstitial fibrosis by decreasing excessive accumulation of extracellular matrix components in UUO mice. It could also reduce the expression of α-SMA, fibronectin and Gli1, a crucial molecule of the hedgehog (Hh) signaling pathway both in vivo and in vitro. In NIH-3T3 cells simulated by conditional medium containing sonic hedgehog variant protein, SSB2 showed the ability to decrease the expression of Gli1 and Ptch1 mRNA. Using a dual-luciferase reporter assay, SSB2 suppressed the Gli-luciferase reporter activity in NIH-3T3 cells, and the IC50 was 0.49 μM, but had no effect on the TNF-α/NF-κB and Wnt/β-catenin signaling pathways, indicating the inhibition selectivity on the Hh signaling pathway. Furthermore, SSB2 failed to inhibit the Hh pathway activity evoked by ectopic expression of Gli2ΔN and Smo D473H, suggesting that SSB2 might potentially act on smoothened receptors. Conclusion SSB2 could attenuate renal fibrosis and decrease fibroblast activation by inhibiting the Hh signaling pathway.

HBV has a relatively high mutation rate in its genome and most of these mutations are associated with the curative effect of nucleoside analogs. In this study, 1,424 patients with HBV-related liver diseases not treated with antiviral drugs, including 197 asymptomatic HBV carriers, 769 chronic hepatitis B patients, 145 acute-on-chronic liver failure (ACLF) patients, 187 HBV-related liver cirrhosis patients, and 126 HBV-related HCC patients, were selected to investigate the distribution and clinical significance of spontaneous YMDD mutations in liver diseases at different stages. Spontaneous YMDD mutations were detected in all stages of liver diseases and the YIDD variant was the dominant mutation type. The mutation rate was higher in genotype C strains than in genotype B strains. The χ2 subdivision method showed that the spontaneous YMDD mutation rate in HCC was higher than that in other liver diseases. The difference in spontaneous YMDD mutation rates in patients with liver diseases infected with genotype C strains at different stages was statistically significant. Spontaneous YMDD mutation rates in patients with liver diseases infected with genotype C strains at different stages were then compared one to one using the χ2 subdivision method. The results showed that the spontaneous YMDD mutation rate in HCC was higher than that in other liver diseases. Taken together, these results indicated that spontaneous YMDD mutations are more likely to occur in patients infected with genotype C strains, and genotype C strains with spontaneous YMDD mutations may be a risk factor for HCC. J. Med. Virol. 86:913–917, 2014. © 2014 Wiley Periodicals, Inc.

By proving an amenable version of Katok's entropy formula and handling the quasi tiling techniques, we establish large deviations bounds for countable discrete amenable group actions. This generalizes the classical results of Lai-Sang Young., 18 pages

HBV has a relatively high mutation rate in its genome and most of these mutations are associated with the curative effect of nucleoside analogs. In this study, 1,424 patients with HBV-related liver diseases not treated with antiviral drugs, including 197 asymptomatic HBV carriers, 769 chronic hepatitis B patients, 145 acute-on-chronic liver failure (ACLF) patients, 187 HBV-related liver cirrhosis patients, and 126 HBV-related HCC patients, were selected to investigate the distribution and clinical significance of spontaneous YMDD mutations in liver diseases at different stages. Spontaneous YMDD mutations were detected in all stages of liver diseases and the YIDD variant was the dominant mutation type. The mutation rate was higher in genotype C strains than in genotype B strains. The χ(2) subdivision method showed that the spontaneous YMDD mutation rate in HCC was higher than that in other liver diseases. The difference in spontaneous YMDD mutation rates in patients with liver diseases infected with genotype C strains at different stages was statistically significant. Spontaneous YMDD mutation rates in patients with liver diseases infected with genotype C strains at different stages were then compared one to one using the χ(2) subdivision method. The results showed that the spontaneous YMDD mutation rate in HCC was higher than that in other liver diseases. Taken together, these results indicated that spontaneous YMDD mutations are more likely to occur in patients infected with genotype C strains, and genotype C strains with spontaneous YMDD mutations may be a risk factor for HCC.

AIM: To investigate the relationship between hepatitis B virus (HBV) genotype with spontaneous YMDD mutations and hepatocellular carcinoma (HCC) in HBV-related cirrhosis. METHODS: We investigated 264 liver cirrhosis patients who were not treated with antiviral drugs, including 81 patients with HCC. YMDD mutations were detected by fluorescent hybridization bioprobe polymerase chain reaction (PCR) and melting curve assay using the Diagnosis Kit for HBV YMDD Mutation. Serum HBV genotypes were detected by real-time PCR using genotype-specific TaqMan probes. Statistical analysis was performed according to data type using the t test, χ2 test and unconditional logistic regression analysis. RESULTS: In the HCC group, genotype C strains, spontaneous YMDD mutations, and genotype C strains with YMDD mutations were detected in 33 (40.74%), 13 (16.05%) and 11 (13.58%) patients, respectively. In the liver cirrhosis (LC) group, HBV genotype C strains, spontaneous YMDD mutations, and genotype C strains with YMDD mutations were detected in 33 (18.03%), 7 (3.83%) and 2 (1.09%) patients, respectively. The differences in genotype C strains, spontaneous YMDD mutations, and genotype C strains with YMDD mutations between the two groups were statistically significant (χ2 = 15.441, P = 0.000; χ2 = 11.983, P = 0.001; P = 0.000). In the HCC and LC groups, there were seven patients infected by genotype B strains with YMDD mutations and 13 by genotype C strains with YMDD mutations. Further research revealed that HCC occurred in 2 patients infected by genotype B strains with YMDD mutations and 11 infected by genotype C strains with YMDD mutations. The difference was statistically significant (P = 0.000). Unconditional logistic regression analysis revealed that patients infected by genotype C strains with spontaneous YMDD mutations had a 7.775-fold higher risk for the development of HBV-related HCC than patients infected by other type HBV strains (P = 0.013, 95%CI: 1.540-39.264). CONCLUSION: Genotype C strains with spontaneous YMDD mutations are an independent risk factor for HCC in LC patients and are important for early warning of HCC.

Combined with pattern recognition and information visualization technology, this paper proposes a visual classification method based on KPCA and parallel coordinate plot - KPPCP. This method maps the raw data space into high-dimensional feature space by means of nuclear function, then the feature space is deal with PCA, finally the processed data is visualized in parallel coordinates. The experiment show that it can effectively extract the non-linear features from the raw data, enlarge the differences between the various categories, provide Interactive visualization, enhance the understanding of experts on the classification process and participation so as to get more effective classification.