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Introduction: Staphylococcus aureus is a gram-positive bacterium that causes serious infection. With the increasing resistance of bacteria to current antibiotics, it is necessary to learn more about the molecular mechanism and cellular pathways involved in the Staphylococcus aureus infection. Methods: We downloaded the GSE33341 dataset from the GEO database and applied the weighted gene co-expression network analysis (WGCNA), from which we obtained some critical modules. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were applied to illustrate the biological functions of genes in these modules. We constructed the protein-protein interaction (PPI) network by Cytoscape and selected five candidate hub genes. Five potential hub genes were validated in GSE30119 by GraphPad Prism 8.0. The diagnostic values of these genes were calculated and present in the ROC curve based on the GSE13670 dataset. Their gene functions were analyzed by Gene Set Enrichment Analysis (GSEA). Results: A co-expression network was built with 5000 genes divided into 11 modules. The genes in green and turquoise modules demonstrated a high correlation. According to the KEGG and GO analyses, genes in the green module were closely related to ubiquitination and autophagy. Subsequently, we picked out the top five hub genes in the green module. And UBB was determined as the hub gene in the GSE30119 dataset. The expression level of UBB, ASB, and MKRN1 could significantly differentiate between Staphylococcus aureus infection and healthy controls based on the ROC curve. The GSEA analysis indicated that lower expression levels of UBB were associated with the P53 signal pathway. Conclusions: We identified some hub genes and significant signal enrichment pathways in Staphylococcus aureus infection via bioinformatics analysis, which may facilitate the development of potential clinical therapeutic strategies. [ABSTRACT FROM AUTHOR]

Background: Drug Combination is one of the effective approaches for treating complex diseases. However, determining combinative drug pairs in clinical trials is still costly. Thus, computational approaches are used to identify potential drug pairs in advance. Existing computational approaches have the following shortcomings: (i) the lack of an effective integration of heterogeneous features leads to a time-consuming training and even results in an over-fitted classifier; and (ii) the narrow consideration of predicting potential drug combinations only among known drugs having known combinations cannot meet the demand of realistic screenings, which pay more attention to potential combinative pairs among newly-coming drugs that have no approved combination with other drugs at all. Results: In this paper, to tackle the above two problems, we propose a novel drug-driven approach for predicting potential combinative pairs on a large scale. We define four new features based on heterogeneous data and design an efficient fusion scheme to integrate these feature. Moreover importantly, we elaborate appropriate cross-validations towards realistic screening scenarios of drug combinations involving both known drugs and new drugs. In addition, we perform an extra investigation to show how each kind of heterogeneous features is related to combinative drug pairs. The investigation inspires the design of our approach. Experiments on real data demonstrate the effectiveness of our fusion scheme for integrating heterogeneous features and its predicting power in three scenarios of realistic screening. In terms of both AUC and AUPR, the prediction among known drugs achieves 0.954 and 0.821, that between known drugs and new drugs achieves 0.909 and 0.635, and that among new drugs achieves 0.809 and 0.592 respectively. Conclusions: Our approach provides not only an effective tool to integrate heterogeneous features but also the first tool to predict potential combinative pairs among new drugs [ABSTRACT FROM AUTHOR]

Background: There has been paid more and more attention to supervised classification models in the area of predicting drug-target interactions (DTIs). However, in terms of classification, unavoidable missing DTIs in data would cause three issues which have not yet been addressed appropriately by former approaches. Directly labeled as negatives (non-DTIs), missing DTIs increase the confusion of positives (DTIs) and negatives, aggravate the imbalance between few positives and many negatives, and are usually discriminated as highly-scored false positives, which influence the existing measures sharply. Results: Under the framework of local classification model (LCM), this work focuses on the scenario of predicting how possibly a new drug interacts with known targets. To address the first two issues, two strategies, Spy and Super-target, are introduced accordingly and further integrated to form a two-layer LCM. In the bottom layer, Spy-based local classifiers for protein targets are built by positives, as well as reliable negatives identified among unlabeled drug-target pairs. In the top layer, regular local classifiers specific to super-targets are built with more positives generated by grouping similar targets and their interactions. Furthermore, to handle the third issue, an additional performance measure, Coverage, is presented for assessing DTI prediction. The experiments based on benchmark datasets are finally performed under five-fold cross validation of drugs to evaluate this approach. The main findings are concluded as follows. (1) Both two individual strategies and their combination are effective to missing DTIs, and the combination wins the best. (2) Having the advantages of less confusing decision boundary at the bottom layer and less biased decision boundary at the top layer, our two-layer LCM outperforms two former approaches. (3) Coverage is more robust to missing interactions than other measures and is able to evaluate how far one needs to go down the list of targets to cover all the proper targets of a drug. Conclusions: Proposing two strategies and one performance measure, this work has addressed the issues derived from missing interactions, which cause confusing and biased decision boundaries in classifiers, as well as the inappropriate measure of predicting performance, in the scenario of predicting interactions between new drugs and known targets. [ABSTRACT FROM AUTHOR]

Background: To investigate the clinical feature and the local failure patterns after intensity-modulated radiotherapy for nasopharyngeal carcinoma. Methods: Between March 2007 and July 2009, 710 patients with nasopharyngeal carcinoma were treated with intensity-modulated radiotherapy. The magnetic resonance imagings obtained at recurrence were registered with the original planning computed tomography for dosimetry analysis. Results: With a median follow-up of 38 months, 34 patients have developed local recurrence (32 cases valid). The incidence of invasion to nasopharynx, parapharyngeal space and the retropharyngeal space by the primary tumors was 100%, 75.0% and 62.5%, respectively, but 78.1%, 34.4% and 21.9% at recurrence, respectively. The rate of invasion to ethmoid sinus was 3.1% by the primary tumors but 28.1% at recurrence (p = 0.005). The topographic analysis of the local failure patterns showed "central" in 16 patients; "marginal" in 9; and "outside" in 7. The median volumes of primary gross tumor were 45.84 cm3 in the central failure group, 29.44 cm3 in the marginal failure group, and 21.52 cm3 in the outside failure group, respectively(p = 0.012), and the median volumes of primary clinical target1 were 87.28 cm3, 61.90 cm3 and 58.74 cm3 in the three groups, respectively (p = 0.033). Conclusions: In patients with nasopharyngeal carcinoma treated with intensity-modulated radiotherapy, the recurrent tumors had their unique characteristic and regularity of invasion to adjacent structures. "Central" failure was the major local failure pattern. The volumes of primary gross tumor and clinical target1 were significantly correlated with recurrent patterns. Employ more aggressive approaches to tumor cells which will be insensitive to radiotherapy may be an effective way to reduce the central failure. [ABSTRACT FROM AUTHOR]

Background Serum pepsinogen (PG) levels are valuable in the diagnosis of gastric diseases. However, PG levels are affected by many factors such as the area and race. This study aimed to investigate serum PG levels in patients with different gastric diseases who were Chinese Han people in Hunan Province, midsouth China. Methods A total of 248 gastric disease patients and 34 healthy controls were enrolled. The patients included those with non-atrophic and chronic atrophic gastritis, gastric and duodenal ulcer, early and advanced gastric cancer. Serum PG I and II levels were detected by Biohit ELISA kit (Finland), and PG I/II ratio was calculated. Differences in patients with gastric disease and healthy controls were analyzed using paired t-test. Results Compared with controls, patients with early and advanced gastric cancer had a significantly lower PG I level and PG I/II ratio (p <0.005). In contrast, patients with gastric and duodenal ulcer had a significantly higher PG I level (p <0.005). Compared with atrophic gastritis patients, patients with early and advanced carcinoma of the stomach had a significantly lower PG I/II ratio (p < 0.001). Combination of the cut-off levels of PG I (70 μg/L) and PG I/II ratio (6) provided 62.1% sensitivity of and 94.2% specificity for the diagnosis of gastric cancer. Conclusions Decreased PG I level and PG I/II ratio are risk factors for gastric cancer. Combined use of serum PG I level and PG I/II ratio may help the early diagnosis of gastric cancer. [ABSTRACT FROM AUTHOR]