The burden of headache in China: validation of diagnostic questionnaire for a population-based survey

The objective of this study was to test the validity, in the Chinese population, of the Lifting The Burden diagnostic questionnaire for the purpose of a population-based survey of the burden of headache in China. From all regions of China, a population-based sample of 417 respondents had completed the structured questionnaire in a door-to-door survey conducted by neurologists from local hospitals calling unannounced. They were contacted for re-interview by telephone by headache specialists who were unaware of the questionnaire diagnoses. A screening question ascertained whether headache had occurred in the last year. If they had, the specialists applied their expertise and ICHD-II diagnostic criteria to make independent diagnoses which, as the gold standard, were later compared with the questionnaire diagnoses. There were 18 refusals; 399 interviews were conducted in 202 women and 197 men aged 18–65 years (mean age 44.4 ± 12.6 years). In comparison to the specialists’ diagnoses, the sensitivity, specificity, positive predictive value, negative predictive value and Cohen’s kappa (95% CI) of the questionnaire for the diagnosis of migraine were 0.83, 0.99, 0.83, 0.99 and 0.82 (0.71–0.93), respectively; for the diagnosis of tension-type headache (TTH), they were 0.51, 0.99, 0.86, 0.92 and 0.59 (0.46–0.72), respectively. In conclusion, the questionnaire was accurate and reliable in diagnosing migraine (agreement level excellent), less so, but adequate, for TTH (sensitivity relatively low, false negative rate relatively high and agreement level fair to good). The non-specific features of TTH do not lend themselves well to diagnosis by questionnaire

Carte hydrogéologique de Wallonie, Vivy - Paliseul, 64/5-6

actualisation partielle : septembre 2015carte hydrogéologiqueCartographie des eaux souterraines de Wallonie1:25 00

Variants of the Coagulation and Inflammation Genes Are Replicably Associated with Myocardial Infarction and Epistatically Interact in Russians.

In spite of progress in cardiovascular genetics, data on genetic background of myocardial infarction are still limited and contradictory. This applies as well to the genes involved in inflammation and coagulation processes, which play a crucial role in the disease etiopathogenesis.In this study we found genetic variants of TGFB1, FGB and CRP genes associated with myocardial infarction in discovery and replication groups of Russian descent from the Moscow region and the Republic of Bashkortostan (325/185 and 220/197 samples, correspondingly). We also found and replicated biallelic combinations of TGFB1 with FGB, TGFB1 with CRP and IFNG with PTGS1 genetic variants associated with myocardial infarction providing a detectable cumulative effect. We proposed an original two-component procedure for the analysis of nonlinear (epistatic) interactions between the genes in biallelic combinations and confirmed the epistasis hypothesis for the set of alleles of IFNG with PTGS. The procedure is applicable to any pair of logical variables, e.g. carriage of two sets of alleles. The composite model that included three single gene variants and the epistatic pair has AUC of 0.66 both in discovery and replication groups.The genetic impact of TGFB1, FGB, CRP, IFNG, and PTGS and/or their biallelic combinations on myocardial infarction was found and replicated in Russians. Evidence of epistatic interactions between IFNG with PTGS genes was obtained both in discovery and replication groups

The map of possible interactions between components of MI-associated biallelic combination <i>IFNG</i> and <i>PTGS1</i> (black circles) and ten relative partners (gray circles) generated by GeneMania online software [45].

<p>Possible physical interactions (pink), co-expression (violet), pathway (blue), genetic interactions (green), and shared protein domains (yellow) are shown. IDO1 –indoleamine 2,3–dioxygenase 1; IFNG–interferon gamma; IFNGR1 –interferon gamma receptor 1; IFNGR2 –interferon gamma receptor 2; IRF1 –interferon regulatory factor 1; MPO–myeloperoxidase; PTGIS–prostaglandin I2 (prostacyclin) synthase; PRKCD–protein kinase C delta; PTGS1 –prostaglandin–endoperoxide synthase 1; PTGS2 –prostaglandin–endoperoxide synthase 2; PTPN2 –protein tyrosine phosphatase, non–receptor type 2; PTPN6 –protein tyrosine phosphatase, non–receptor type 6.</p

ROC curves demonstrate usefulness of the additive composite model built from all identified genetic markers.

<p><b>A</b>. Comparing performance of the composite model to the performance of each single marker in the Moscow discovery sample. Combining the high specificity of <i>CRP</i> and <i>IFNG</i>+<i>PTGS</i> predictors (the left hump) with relatively high sensitivity of <i>TGFB1</i> and <i>FGB</i> (the right hump) yields a much better classifier. <b>B</b>. Performance of the model stays the same when tested on the independent replication sample (Bashkortostan).</p