A secondary Fracture Prevention Programme to reduce fractures, hospital admissions, and mortality rates

Conference Theme: Happy Staff - Healthy People (開心員工 - 共建民康)published_or_final_versionThe Hospital Authority Convention, Hong Kong, 10-11 May 2010

Evaluation of the Osteoporosis Secondary Fracture Prevention Program at Queen Mary Hospital: successful recruitment is associated with lower re-fracture rate and mortality rate at one year

Conference Theme: Happy Staff - Healthy People (開心員工 - 共建民康)published_or_final_versionThe Hospital Authority Convention, Hong Kong, 10-11 May 2010

Diagnostic performance of the Minimal Eating Observation and Nutrition Form - Version II (MEONF-II) and Nutritional Risk Screening 2002 (NRS 2002) among hospital inpatients - a cross-sectional study

<p>Abstract</p> <p>Background</p> <p>The usefulness of the nutritional screening tool Minimal Eating Observation and Nutrition Form - Version II (MEONF-II) relative to Nutritional Risk Screening 2002 (NRS 2002) remains untested. Here we attempted to fill this gap by testing the diagnostic performance and user-friendliness of the MEONF-II and the NRS 2002 in relation to the Mini Nutritional Assessment (MNA) among hospital inpatients.</p> <p>Methods</p> <p>Eighty seven hospital inpatients were assessed for nutritional status with the 18-item MNA (considered as the gold standard), and screened with the NRS 2002 and the MEONF-II.</p> <p>Results</p> <p>The MEONF-II sensitivity (0.61), specificity (0.79), and accuracy (0.68) were acceptable. The corresponding figures for NRS 2002 were 0.37, 0.82 and 0.55, respectively. MEONF-II and NRS 2002 took five minutes each to complete. Assessors considered MEONF-II instructions and items to be easy to understand and complete (96-99%), and the items to be relevant (87%). For NRS 2002, the corresponding figures were 75-93% and 79%, respectively.</p> <p>Conclusions</p> <p>The MEONF-II is an easy to use, relatively quick and sensitive screening tool to assess risk of undernutrition among hospital inpatients. With respect to user-friendliness and sensitivity the MEONF-II seems to perform better than the NRS 2002, although larger studies are needed for firm conclusions. The different scoring systems for undernutrition appear to identify overlapping but not identical patient groups. A potential limitation with the study is that the MNA was used as gold standard among patients younger than 65 years.</p

Human Gene Coexpression Landscape: Confident Network Derived from Tissue Transcriptomic Profiles

This is an open-access article distributed under the terms of the Creative Commons Attribution License.[Background]: Analysis of gene expression data using genome-wide microarrays is a technique often used in genomic studies to find coexpression patterns and locate groups of co-transcribed genes. However, most studies done at global >omic> scale are not focused on human samples and when they correspond to human very often include heterogeneous datasets, mixing normal with disease-altered samples. Moreover, the technical noise present in genome-wide expression microarrays is another well reported problem that many times is not addressed with robust statistical methods, and the estimation of errors in the data is not provided. [Methodology/Principal Findings]: Human genome-wide expression data from a controlled set of normal-healthy tissues is used to build a confident human gene coexpression network avoiding both pathological and technical noise. To achieve this we describe a new method that combines several statistical and computational strategies: robust normalization and expression signal calculation; correlation coefficients obtained by parametric and non-parametric methods; random cross-validations; and estimation of the statistical accuracy and coverage of the data. All these methods provide a series of coexpression datasets where the level of error is measured and can be tuned. To define the errors, the rates of true positives are calculated by assignment to biological pathways. The results provide a confident human gene coexpression network that includes 3327 gene-nodes and 15841 coexpression-links and a comparative analysis shows good improvement over previously published datasets. Further functional analysis of a subset core network, validated by two independent methods, shows coherent biological modules that share common transcription factors. The network reveals a map of coexpression clusters organized in well defined functional constellations. Two major regions in this network correspond to genes involved in nuclear and mitochondrial metabolism and investigations on their functional assignment indicate that more than 60% are house-keeping and essential genes. The network displays new non-described gene associations and it allows the placement in a functional context of some unknown non-assigned genes based on their interactions with known gene families. [Conclusions/Significance]: The identification of stable and reliable human gene to gene coexpression networks is essential to unravel the interactions and functional correlations between human genes at an omic scale. This work contributes to this aim, and we are making available for the scientific community the validated human gene coexpression networks obtained, to allow further analyses on the network or on some specific gene associations. The data are available free online at http://bioinfow.dep.usal.es/coexpression/. © 2008 Prieto et al.Funding and grant support was provided by the Ministery of Health, Spanish Government (ISCiii-FIS, MSyC; Project reference PI061153) and by the Ministery of Education, Castilla-Leon Local Government (JCyL; Project reference CSI03A06).Peer Reviewe

Validation of a multifactorial risk factor model used for predicting future caries risk with nevada adolescents

<p>Abstract</p> <p>Background</p> <p>The objective of this study was to measure the validity and reliability of a multifactorial Risk Factor Model developed for use in predicting future caries risk in Nevada adolescents in a public health setting.</p> <p>Methods</p> <p>This study examined retrospective data from an oral health surveillance initiative that screened over 51,000 students 13-18 years of age, attending public/private schools in Nevada across six academic years (2002/2003-2007/2008). The Risk Factor Model included ten demographic variables: exposure to fluoridation in the municipal water supply, environmental smoke exposure, race, age, locale (metropolitan vs. rural), tobacco use, Body Mass Index, insurance status, sex, and sealant application. Multiple regression was used in a previous study to establish which significantly contributed to caries risk. Follow-up logistic regression ascertained the weight of contribution and odds ratios of the ten variables. Researchers in this study computed sensitivity, specificity, positive predictive value (PVP), negative predictive value (PVN), and prevalence across all six years of screening to assess the validity of the Risk Factor Model.</p> <p>Results</p> <p>Subjects' overall mean caries prevalence across all six years was 66%. Average sensitivity across all six years was 79%; average specificity was 81%; average PVP was 89% and average PVN was 67%.</p> <p>Conclusions</p> <p>Overall, the Risk Factor Model provided a relatively constant, valid measure of caries that could be used in conjunction with a comprehensive risk assessment in population-based screenings by school nurses/nurse practitioners, health educators, and physicians to guide them in assessing potential future caries risk for use in prevention and referral practices.</p

Chapter 8: Meta-analysis of Test Performance When There is a “Gold Standard”

Synthesizing information on test performance metrics such as sensitivity, specificity, predictive values and likelihood ratios is often an important part of a systematic review of a medical test. Because many metrics of test performance are of interest, the meta-analysis of medical tests is more complex than the meta-analysis of interventions or associations. Sometimes, a helpful way to summarize medical test studies is to provide a “summary point”, a summary sensitivity and a summary specificity. Other times, when the sensitivity or specificity estimates vary widely or when the test threshold varies, it is more helpful to synthesize data using a “summary line” that describes how the average sensitivity changes with the average specificity. Choosing the most helpful summary is subjective, and in some cases both summaries provide meaningful and complementary information. Because sensitivity and specificity are not independent across studies, the meta-analysis of medical tests is fundamentaly a multivariate problem, and should be addressed with multivariate methods. More complex analyses are needed if studies report results at multiple thresholds for positive tests. At the same time, quantitative analyses are used to explore and explain any observed dissimilarity (heterogeneity) in the results of the examined studies. This can be performed in the context of proper (multivariate) meta-regressions

Identification of a Highly Conserved H1 Subtype-Specific Epitope with Diagnostic Potential in the Hemagglutinin Protein of Influenza A Virus

Subtype specificity of influenza A virus (IAV) is determined by its two surface glycoproteins, hemagglutinin (HA) and neuraminidase (NA). For HA, 16 distinct subtypes (H1–H16) exist, while nine exist for NA. The epidemic strains of H1N1 IAV change frequently and cause annual seasonal epidemics as well as occasional pandemics, such as the notorious 1918 influenza pandemic. The recent introduction of pandemic A/H1N1 IAV (H1N1pdm virus) into humans re-emphasizes the public health concern about H1N1 IAV. Several studies have identified conserved epitopes within specific HA subtypes that can be used for diagnostics. However, immune specific epitopes in H1N1 IAV have not been completely assessed. In this study, linear epitopes on the H1N1pdm viral HA protein were identified by peptide scanning using libraries of overlapping peptides against convalescent sera from H1N1pdm patients. One epitope, P5 (aa 58–72) was found to be immunodominant in patients and to evoke high titer antibodies in mice. Multiple sequence alignments and in silico coverage analysis showed that this epitope is highly conserved in influenza H1 HA [with a coverage of 91.6% (9,860/10,767)] and almost completely absent in other subtypes [with a coverage of 3.3% (792/23,895)]. This previously unidentified linear epitope is located outside the five well-recognized antigenic sites in HA. A peptide ELISA method based on this epitope was developed and showed high correlation (χ2 = 51.81, P<0.01, Pearson correlation coefficient R = 0.741) with a hemagglutination inhibition test. The highly conserved H1 subtype-specific immunodominant epitope may form the basis for developing novel assays for sero-diagnosis and active surveillance against H1N1 IAVs

Identification of a Highly Conserved H1 Subtype-Specific Epitope with Diagnostic Potential in the Hemagglutinin Protein of Influenza A Virus

Subtype specificity of influenza A virus (IAV) is determined by its two surface glycoproteins, hemagglutinin (HA) and neuraminidase (NA). For HA, 16 distinct subtypes (H1–H16) exist, while nine exist for NA. The epidemic strains of H1N1 IAV change frequently and cause annual seasonal epidemics as well as occasional pandemics, such as the notorious 1918 influenza pandemic. The recent introduction of pandemic A/H1N1 IAV (H1N1pdm virus) into humans re-emphasizes the public health concern about H1N1 IAV. Several studies have identified conserved epitopes within specific HA subtypes that can be used for diagnostics. However, immune specific epitopes in H1N1 IAV have not been completely assessed. In this study, linear epitopes on the H1N1pdm viral HA protein were identified by peptide scanning using libraries of overlapping peptides against convalescent sera from H1N1pdm patients. One epitope, P5 (aa 58–72) was found to be immunodominant in patients and to evoke high titer antibodies in mice. Multiple sequence alignments and in silico coverage analysis showed that this epitope is highly conserved in influenza H1 HA [with a coverage of 91.6% (9,860/10,767)] and almost completely absent in other subtypes [with a coverage of 3.3% (792/23,895)]. This previously unidentified linear epitope is located outside the five well-recognized antigenic sites in HA. A peptide ELISA method based on this epitope was developed and showed high correlation (χ2 = 51.81, P<0.01, Pearson correlation coefficient R = 0.741) with a hemagglutination inhibition test. The highly conserved H1 subtype-specific immunodominant epitope may form the basis for developing novel assays for sero-diagnosis and active surveillance against H1N1 IAVs

The Refinement of Genetic Predictors of Multiple Sclerosis

Medical Research Council [GRANT NUMBER G0801976], a research fellowship FISM-Fondazione Italiana Sclerosi Multipla-Cod.: [2010/B/5 to GD] and an MS Society of Great Britain and Northern Ireland Clinical Research Fellowship [GRANT NUMBER 940/10 to RD]