Establishment of a New Urban Solid Waste Management Programs in Mazandaran Province, North of Iran

This study reports residents’ preferences to establish a new urban solid waste management programs results from a double-bounded dichotomous choice contingent valuation method and choice experiment in Mazandaran province, north of Iran. In order to analysis the residents’ preferences, a dichotomous hypothetical market and a choice sets with different attributes and options were used For estimation of two mentioned methods, the normal logit and conditional logit were applied. In addition, an empirical comparison of the welfare measures derived from the doublebounded DC-CVM and CE is conducted. The main results show that there is no significant difference between the values derived from the two methods. The mean of WTP to establish a new solid waste management programs in CV and CE were estimated 2.45 and 2.61 US$, respectively, per a person per a month. Also the estimated marginal WTP for all attributes in CE was 8.1 US$ per a month. The results suggest that both double-bounded DC-CVM and CE can be successfully stablished for improvement environmental level quality in Mazandaran province. This paper could provide the basis for further development of other new programs on sustainable urban management of solid waste in Mazandaran province.Keywords: Dichotomous choice, Willingness to pay, Solid waste management, Mazandaran province, Ira

A Comparison between Recombinant Activated Factor VII (Aryoseven) and Novoseven in Patients with Congenital Factor VII Deficiency

In order to establish the efficacy and biosimilar nature of AryoSeven to NovoSeven in the treatment of congenital factor VII (FVII) deficiency, patients received either agent at 30 1/4g/kg, intravenously per week for 4 weeks, in a randomized fashion. The primary aim was to compare FVII:coagulation activity (FVII:C), 20 minutes after recombinant activated FVII (rFVIIa) injection, in the 2 groups. A secondary measure was self-reported bleeding. The median interquartile baseline range of the plasma level of activated FVII (FVIIa) activity in the 2 groups was 1.6 (1.1-14.0) IU/dL and 5.0 (1.1-25.5) IU/dL. All patients achieved levels of FVIIa (FVII:C) >30 IU/dL, 20 minutes after the injection of rFVIIa. Bleeding was similar between the 2 groups, with a comparable decrease in severity and frequency compared to the last month prior to treatment. AryoSeven is similar to NovoSeven in increasing postinjection FVIIa activity as well as in clinical safety and efficacy. © The Author(s) 2014

DNA methylation epi-signature is associated with two molecularly and phenotypically distinct clinical subtypes of Phelan-McDermid syndrome

Background: Phelan-McDermid syndrome is characterized by a range of neurodevelopmental phenotypes with incomplete penetrance and variable expressivity. It is caused by a variable size and breakpoint microdeletions in the distal long arm of chromosome 22, referred to as 22q13.3 deletion syndrome, including the SHANK3 gene. Genetic defects in a growing number of neurodevelopmental genes have been shown to cause genome-wide disruptions in epigenomic profiles referred to as epi-signatures in affected individuals. Results: In this study we assessed genome-wide DNA methylation profiles in a cohort of 22 individuals with Phelan-McDermid syndrome, including 11 individuals with large (2 to 5.8 Mb) 22q13.3 deletions, 10 with small deletions (\u3c 1 Mb) or intragenic variants in SHANK3 and one mosaic case. We describe a novel genome-wide DNA methylation epi-signature in a subset of individuals with Phelan-McDermid syndrome. Conclusion: We identified the critical region including the BRD1 gene as responsible for the Phelan-McDermid syndrome epi-signature. Metabolomic profiles of individuals with the DNA methylation epi-signature showed significantly different metabolomic profiles indicating evidence of two molecularly and phenotypically distinct clinical subtypes of Phelan-McDermid syndrome

Frameshift mutations at the C-terminus of HIST1H1E result in a specific DNA hypomethylation signature

BACKGROUND: We previously associated HIST1H1E mutations causing Rahman syndrome with a specific genome-wide methylation pattern. RESULTS: Methylome analysis from peripheral blood samples of six affected subjects led us to identify a specific hypomethylated profile. This "episignature" was enriched for genes involved in neuronal system development and function. A computational classifier yielded full sensitivity and specificity in detecting subjects with Rahman syndrome. Applying this model to a cohort of undiagnosed probands allowed us to reach diagnosis in one subject. CONCLUSIONS: We demonstrate an epigenetic signature in subjects with Rahman syndrome that can be used to reach molecular diagnosis

A genome-wide DNA methylation signature for SETD1B-related syndrome

SETD1B is a component of a histone methyltransferase complex that specifically methylates Lys-4 of histone H3 (H3K4) and is responsible for the epigenetic control of chromatin structure and gene expression. De novo microdeletions encompassing this gene as well as de novo missense mutations were previously linked to syndromic intellectual disability (ID). Here, we identify a specific hypermethylation signature associated with loss of function mutations in the SETD1B gene which may be used as an epigenetic marker supporting the diagnosis of syndromic SETD1B-related diseases. We demonstrate the clinical utility of this unique epi-signature by reclassifying previously identified SETD1B VUS (variant of uncertain significance) in two patients

A genome-wide DNA methylation signature for SETD1B-related syndrome

SETD1B is a component of a histone methyltransferase complex that specifically methylates Lys-4 of histone H3 (H3K4) and is responsible for the epigenetic control of chromatin structure and gene expression. De novo microdeletions encompassing this gene as well as de novo missense mutations were previously linked to syndromic intellectual disability (ID). Here, we identify a specific hypermethylation signature associated with loss of function mutations in the SETD1B gene which may be used as an epigenetic marker supporting the diagnosis of syndromic SETD1B-related diseases. We demonstrate the clinical utility of this unique epi-signature by reclassifying previously identified SETD1B VUS (variant of uncertain significance) in two patients

A comparison of three clustering methods for finding subgroups in MRI, SMS or clinical data: SPSS TwoStep Cluster analysis, Latent Gold and SNOB

Background: There are various methodological approaches to identifying clinically important subgroups and one method is to identify clusters of characteristics that differentiate people in cross-sectional and/or longitudinal data using Cluster Analysis (CA) or Latent Class Analysis (LCA). There is a scarcity of head-to-head comparisons that can inform the choice of which clustering method might be suitable for particular clinical datasets and research questions. Therefore, the aim of this study was to perform a head-to-head comparison of three commonly available methods (SPSS TwoStep CA, Latent Gold LCA and SNOB LCA). Methods. The performance of these three methods was compared: (i) quantitatively using the number of subgroups detected, the classification probability of individuals into subgroups, the reproducibility of results, and (ii) qualitatively using subjective judgments about each program's ease of use and interpretability of the presentation of results.We analysed five real datasets of varying complexity in a secondary analysis of data from other research projects. Three datasets contained only MRI findings (n = 2,060 to 20,810 vertebral disc levels), one dataset contained only pain intensity data collected for 52 weeks by text (SMS) messaging (n = 1,121 people), and the last dataset contained a range of clinical variables measured in low back pain patients (n = 543 people). Four artificial datasets (n = 1,000 each) containing subgroups of varying complexity were also analysed testing the ability of these clustering methods to detect subgroups and correctly classify individuals when subgroup membership was known. Results: The results from the real clinical datasets indicated that the number of subgroups detected varied, the certainty of classifying individuals into those subgroups varied, the findings had perfect reproducibility, some programs were easier to use and the interpretability of the presentation of their findings also varied. The results from the artificial datasets indicated that all three clustering methods showed a near-perfect ability to detect known subgroups and correctly classify individuals into those subgroups. Conclusions: Our subjective judgement was that Latent Gold offered the best balance of sensitivity to subgroups, ease of use and presentation of results with these datasets but we recognise that different clustering methods may suit other types of data and clinical research questions