Periodontal Disease and Risk of Bladder Cancer: A Meta-Analysis of 298476 Participants

Objective: It has been reported that the periodontal disease is linked to a number of malignant tumors such as lung cancer and pancreatic cancer. In this study, we aimed to investigate the association of periodontal disease with risk of bladder cancer by a meta-analysis.Methods: PubMed, Scopus, ScienceDirect, and Chinese National Knowledge Infrastructure (CNKI) were searched for eligible publications up to December 15, 2017. Cohort and nested case-control studies on the association between periodontal disease and risk of bladder cancer were included. After study selection and data extraction, pooled hazard ratios (HRs) and their 95% confidence intervals (95%CIs) were calculated using a fixed-effect inverse-variance model. All analyses were performed using the RevMan 5.3 software.Results: Finally, five cohort studies were identified and included in this meta-analysis, involving 1,104 bladder cancer cases of 298,476 participants. Summary estimates based on adjusted data showed that periodontal disease was not significantly associated with the risk of bladder cancer (HR = 1.09, 95% CI = 0.95–1.25, I2 = 0%). A similar result was also observed after cumulative, subgroup and sensitivity analyses.Conclusions: Current evidence from cohort studies suggests that patients with periodontal disease may not be at an increased risk of developing bladder cancer

The Shenzhen Declaration on Plant Sciences – Uniting plant sciences and society to build a green, sustainable Earth

© 2017 Shenzhen Declaration Drafting Committee. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The file attached is the Published/publisher’s pdf version of the article

Molecular and Functional Characterization of a c-type lysozyme from the Asian Corn Borer, Ostrinia furnacalis

Some lepidopteran lysozymes have been reported to display activity against Gram-positive and Gram-negative bacteria, in contrast to most lysozymes that are active only against Gram-positive bacteria. OstrinLysC, a c-type lysozyme, was purified from the Asian corn borer, Ostrinia furnacalis Guenée (Lepidoptera: Pyralidae), and shows activity against Gram-positive and Gram-negative bacteria. The NH2-terminal amino acid sequence was determined by Edman degradation and used in a homology cloning strategy. The gene coding for OstrinLysC contains three exons and two introns. The expression profile of the OstrinlysC gene was examined by quantitative real-time PCR. Following injection of the larvae with bacteria, the OstrinlysC gene is strongly up-regulated in immune tissues. Transcripts were also detected in gut tissue. After feeding the larvae with bacteria, OstrinlysC transcripts increased in immune tissues. A very low level of transcript abundance was also detected in gut tissue. These results suggested that the OstrinlysC gene is involved in immune responses. The three dimensional structure of OstrinLysC was predicted. Based on comparison of the 3-D structure of OstrinLysC with that of silkworm lysozyme and chicken lysozyme, we hypothesize that the positive charge-rich surface and the short loop-2, which is close to the cluster of hydrophobic residues, may play important roles in the interaction with the outer membrane of Gram-negative bacterial cell walls

Geostatistical analysis of an experimental stratigraphy

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95276/1/wrcr10212.pd

Genomic and oncogenic preference of HBV integration in hepatocellular carcinoma

Hepatitis B virus (HBV) can integrate into the human genome, contributing to genomic instability and hepatocarcinogenesis. Here by conducting high-throughput viral integration detection and RNA sequencing, we identify 4,225 HBV integration events in tumour and adjacent non-tumour samples from 426 patients with HCC. We show that HBV is prone to integrate into rare fragile sites and functional genomic regions including CpG islands. We observe a distinct pattern in the preferential sites of HBV integration between tumour and non-tumour tissues. HBV insertional sites are significantly enriched in the proximity of telomeres in tumours. Recurrent HBV target genes are identified with few that overlap. The overall HBV integration frequency is much higher in tumour genomes of males than in females, with a significant enrichment of integration into chromosome 17. Furthermore, a cirrhosis-dependent HBV integration pattern is observed, affecting distinct targeted genes. Our data suggest that HBV integration has a high potential to drive oncogenic transformation

Diverse Applications of Nanomedicine

The design and use of materials in the nanoscale size range for addressing medical and health-related issues continues to receive increasing interest. Research in nanomedicine spans a multitude of areas, including drug delivery, vaccine development, antibacterial, diagnosis and imaging tools, wearable devices, implants, high-throughput screening platforms, etc. using biological, nonbiological, biomimetic, or hybrid materials. Many of these developments are starting to be translated into viable clinical products. Here, we provide an overview of recent developments in nanomedicine and highlight the current challenges and upcoming opportunities for the field and translation to the clinic. \ua9 2017 American Chemical Society

An Optimization Approach for Mining of Process Models with Infrequent Behaviors Integrating Data Flow and Control Flow

Infrequent behaviors of business process refer to behaviors that occur in very exceptional cases, and their occurrence frequency is low as their required conditions are rarely fulfilled. Hence, a strong coupling relationship between infrequent behavior and data flow exists. Furthermore, some infrequent behaviors may reveal very important information about the process. Thus, not all infrequent behaviors should be disregarded as noise, and identifying infrequent but correct behaviors in the event log is vital to process mining from the perspective of data flow. Existing process mining approaches construct a process model from frequent behaviors in the event log, mostly concentrating on control flow only, without considering infrequent behavior and data flow information. In this paper, we focus on data flow to extract infrequent but correct behaviors from logs. For an infrequent trace, frequent patterns and interactive behavior profiles are combined to find out which part of the behavior in the trace occurs in low frequency. And, conditional dependency probability is used to analyze the influence strength of the data flow information on infrequent behavior. An approach for identifying effective infrequent behaviors based on the frequent pattern under data awareness is proposed correspondingly. Subsequently, an optimization approach for mining of process models with infrequent behaviors integrating data flow and control flow is also presented. The experiments on synthetic and real-life event logs show that the proposed approach can distinguish effective infrequent behaviors from noise compared with others. The proposed approaches greatly improve the fitness of the mined process model without significantly decreasing its precision