Impact of dentine hypersensitivity on oral health-related quality of life in individuals receiving supportive periodontal care

AIM: To determine the prevalence and impact of dentine hypersensitivity (DH) on oral health-related quality of life (OHRQoL) in individuals receiving supportive periodontal care (SPC). MATERIALS AND METHODS: One hundred and two adults receiving SPC were recruited for a cross-sectional study. Subjects were categorized into those who self-reported DH (DH1) or did not (DH0). Impact of DH on OHRQoL was assessed using the Chinese Condition-Specific Oral Impact on Daily Performance questionnaire (CS-OIDP). Evaluation of DH included tactile-stimulation followed by air-blast, and recorded using a Visual Analogue Scale (VAS). RESULTS: Sixty-one (59.8%) subjects self-reported DH with mean air-blast VAS score of 29.4 ± 21.3 mm and mean tactile-stimulation VAS score of 10.9 ± 14.7 mm. Fifty (49%) subjects reported impact on OHRQoL (mean CS-OIDP score = 4.7 ± 6.3). The most affected performance was cleaning the mouth (35.3%). Positive expression of DH and worse OHRQoL were associated with higher air-blast and tactile-stimulation VAS scores, and use of desensitizing agents. The minimally important difference (MID) in CS-OIDP scores was 2.0 points. Approximately 30% of the subjects reported CS-OIDP scores above the MID. CONCLUSIONS: Dentine hypersensitivity affects OHRQoL in patients undergoing SPC. The extent of impact was associated with severity of DH. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.postprin

Optimal Control of SOAs With Artificial Intelligence for Sub-Nanosecond Optical Switching

Novel approaches to switching ultra-fast semiconductor optical amplifiers using artificial intelligence algorithms (particle swarm optimisation, ant colony optimisation, and a genetic algorithm) are developed and applied both in simulation and experiment. Effective off-on switching (settling) times of 542 ps are demonstrated with just 4.8% overshoot, achieving an order of magnitude improvement over previous attempts described in the literature and standard dampening techniques from control theory

Harnessing technology and molecular analysis to understand the development of cardiovascular diseases in Asia: a prospective cohort study (SingHEART)

BACKGROUND: Cardiovascular disease (CVD) imposes much mortality and morbidity worldwide. The use of "deep learning", advancements in genomics, metabolomics, proteomics and devices like wearables have the potential to unearth new insights in the field of cardiology. Currently, in Asia, there are no studies that combine the use of conventional clinical information with these advanced technologies. We aim to harness these new technologies to understand the development of cardiovascular disease in Asia. METHODS: Singapore is a multi-ethnic country in Asia with well-represented diverse ethnicities including Chinese, Malays and Indians. The SingHEART study is the first technology driven multi-ethnic prospective population-based study of healthy Asians. Healthy male and female subjects aged 21-69 years old without any prior cardiovascular disease or diabetes mellitus will be recruited from the general population. All subjects are consented to undergo a detailed on-line questionnaire, basic blood investigations, resting and continuous electrocardiogram and blood pressure monitoring, activity and sleep tracking, calcium score, cardiac magnetic resonance imaging, whole genome sequencing and lipidomic analysis. Outcomes studied will include mortality and cause of mortality, myocardial infarction, stroke, malignancy, heart failure, and the development of co-morbidities. DISCUSSION: An initial target of 2500 patients has been set. From October 2015 to May 2017, an initial 683 subjects have been recruited and have completed the initial work-up the SingHEART project is the first contemporary population-based study in Asia that will include whole genome sequencing and deep phenotyping: including advanced imaging and wearable data, to better understand the development of cardiovascular disease across different ethnic groups in Asia

DADA: Degree-Aware Algorithms for Network-Based Disease Gene Prioritization

<p>Abstract</p> <p>Background</p> <p>High-throughput molecular interaction data have been used effectively to prioritize candidate genes that are linked to a disease, based on the observation that the products of genes associated with similar diseases are likely to interact with each other heavily in a network of protein-protein interactions (PPIs). An important challenge for these applications, however, is the incomplete and noisy nature of PPI data. Information flow based methods alleviate these problems to a certain extent, by considering indirect interactions and multiplicity of paths.</p> <p>Results</p> <p>We demonstrate that existing methods are likely to favor highly connected genes, making prioritization sensitive to the skewed degree distribution of PPI networks, as well as ascertainment bias in available interaction and disease association data. Motivated by this observation, we propose several statistical adjustment methods to account for the degree distribution of known disease and candidate genes, using a PPI network with associated confidence scores for interactions. We show that the proposed methods can detect loosely connected disease genes that are missed by existing approaches, however, this improvement might come at the price of more false negatives for highly connected genes. Consequently, we develop a suite called D<smcaps>A</smcaps>D<smcaps>A</smcaps>, which includes different uniform prioritization methods that effectively integrate existing approaches with the proposed statistical adjustment strategies. Comprehensive experimental results on the Online Mendelian Inheritance in Man (OMIM) database show that D<smcaps>A</smcaps>D<smcaps>A</smcaps> outperforms existing methods in prioritizing candidate disease genes.</p> <p>Conclusions</p> <p>These results demonstrate the importance of employing accurate statistical models and associated adjustment methods in network-based disease gene prioritization, as well as other network-based functional inference applications. D<smcaps>A</smcaps>D<smcaps>A</smcaps> is implemented in Matlab and is freely available at <url>http://compbio.case.edu/dada/</url>.</p

Dynamic phenotypic heterogeneity and the evolution of multiple RNA subtypes in Hepatocellular Carcinoma: the PLANET study

Intra-tumor heterogeneity (ITH) is a key challenge in cancer treatment, but previous studies have focused mainly on the genomic alterations without exploring phenotypic (transcriptomic and immune) heterogeneity. Using one of the largest prospective surgical cohorts for Hepatocellular Carcinoma (HCC) with multi-region sampling, we sequenced whole genomes and paired transcriptomes from 67 HCC patients (331 samples). We found that while genomic ITH was rather constant across TNM stages, phenotypic ITH had a very different trajectory and quickly diversified in stage II patients. Most strikingly, 30% patients were found to contain more than one transcriptomic subtype within a single tumor. Such phenotypic ITH was found to be much more informative in predicting patient survival than genomic ITH and explains the poor efficacy of single-target systemic therapies in HCC. Taken together, we not only revealed an unprecedentedly dynamic landscape of phenotypic heterogeneity in HCC, but also highlighted the importance of studying phenotypic evolution across cancer types

Critical assessment of sequence-based protein-protein interaction prediction methods that do not require homologous protein sequences

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions underlie many important biological processes. Computational prediction methods can nicely complement experimental approaches for identifying protein-protein interactions. Recently, a unique category of sequence-based prediction methods has been put forward - unique in the sense that it does not require homologous protein sequences. This enables it to be universally applicable to all protein sequences unlike many of previous sequence-based prediction methods. If effective as claimed, these new sequence-based, universally applicable prediction methods would have far-reaching utilities in many areas of biology research.</p> <p>Results</p> <p>Upon close survey, I realized that many of these new methods were ill-tested. In addition, newer methods were often published without performance comparison with previous ones. Thus, it is not clear how good they are and whether there are significant performance differences among them. In this study, I have implemented and thoroughly tested 4 different methods on large-scale, non-redundant data sets. It reveals several important points. First, significant performance differences are noted among different methods. Second, data sets typically used for training prediction methods appear significantly biased, limiting the general applicability of prediction methods trained with them. Third, there is still ample room for further developments. In addition, my analysis illustrates the importance of complementary performance measures coupled with right-sized data sets for meaningful benchmark tests.</p> <p>Conclusions</p> <p>The current study reveals the potentials and limits of the new category of sequence-based protein-protein interaction prediction methods, which in turn provides a firm ground for future endeavours in this important area of contemporary bioinformatics.</p

Constructing the HBV-human protein interaction network to understand the relationship between HBV and hepatocellular carcinoma

<p>Abstract</p> <p>Background</p> <p>Epidemiological studies have clearly validated the association between hepatitis B virus (HBV) infection and hepatocellular carcinoma (HCC). Patients with chronic HBV infection are at increased risk of HCC, in particular those with active liver disease and cirrhosis.</p> <p>Methods</p> <p>We catalogued all published interactions between HBV and human proteins, identifying 250 descriptions of HBV and human protein interactions and 146 unique human proteins that interact with HBV proteins by text mining.</p> <p>Results</p> <p>Integration of this data set into a reconstructed human interactome showed that cellular proteins interacting with HBV are made up of core proteins that are interconnected with many pathways. A global analysis based on functional annotation highlighted the enrichment of cellular pathways targeted by HBV.</p> <p>Conclusions</p> <p>By connecting the cellular proteins targeted by HBV, we have constructed a central network of proteins associated with hepatocellular carcinoma, which might be to regard as the basis of a detailed map for tracking new cellular interactions, and guiding future investigations.</p

DLEC1 is a functional 3p22.3 tumour suppressor silenced by promoter CpG methylation in colon and gastric cancers

Promoter CpG methylation of tumour suppressor genes (TSGs) is an epigenetic biomarker for TSG identification and molecular diagnosis. We screened genome wide for novel methylated genes through methylation subtraction of a genetic demethylation model of colon cancer (double knockout of DNMT1 and DNMT3B in HCT116) and identified DLEC1 (Deleted in lung and oesophageal cancer 1), a major 3p22.3 TSG, as one of the methylated targets. We further found that DLEC1 was downregulated or silenced in most colorectal and gastric cell lines due to promoter methylation, whereas broadly expressed in normal tissues including colon and stomach, and unmethylated in expressing cell lines and immortalised normal colon epithelial cells. DLEC1 expression was reactivated through pharmacologic or genetic demethylation, indicating a DNMT1/DNMT3B-mediated methylation silencing. Aberrant methylation was further detected in primary colorectal (10 out of 34, 29%) and gastric tumours (30 out of 89, 34%), but seldom in paired normal colon (0 out of 17) and gastric (1 out of 20, 5%) samples. No correlation between DLEC1 methylation and clinical parameters of gastric cancers was found. Ectopic expression of DLEC1 in silenced HCT116 and MKN45 cells strongly inhibited their clonogenicity. Thus, DLEC1 is a functional tumour suppressor, being frequently silenced by epigenetic mechanism in gastrointestinal tumours

Improved Optoelectronic Properties of Rapid Thermally Annealed Dilute Nitride GaInNAs Photodetectors

We investigate the optical and electrical characteristics of GaInNAs/GaAs long-wavelength photodiodes grown under varying conditions by molecular beam epitaxy and subjected to postgrowth rapid thermal annealing (RTA) at a series of temperatures. It is found that the device performance of the nonoptimally grown GaInNAs p-i-n structures, with nominal compositions of 10% In and 3.8% N, can be improved significantly by the RTA treatment to match that of optimally grown structures. The optimally annealed devices exhibit overall improvement in optical and electrical characteristics, including increased photoluminescence brightness, reduced density of deep-level traps, reduced series resistance resulting from the GaAs/GaInNAs heterointerface, lower dark current, and significantly lower background doping density, all of which can be attributed to the reduced structural disorder in the GaInNAs alloy.© 2012 TMS