Economic evaluation of a national vitamin D supplementation program among Iranian adolescents for the prevention of adulthood type 2 diabetes mellitus.

BACKGROUND: This study aimed to evaluate the cost-effectiveness of vitamin D supplementation in preventing type 2 diabetes mellitus (T2DM) among Iranian adolescents. METHODS: This analytical observational study was conducted, using the decision tree model constructed in TreeAge Pro to assess the cost per quality-adjusted life-year (QALY) of monthly intake vitamin D supplements to prevent T2DM compared to no intervention from the viewpoint of Iran's Ministry of Health and through an one-year horizon. In the national program of vitamin D supplementation, 1,185,211 Iranian high-school students received 50,000 IU vitamin D supplements monthly for nine months. The costs-related data were modified to 2018. The average cost and effectiveness were compared based on the Incremental Cost-Effectiveness Ratio (ICER). RESULTS: Our analytical analysis estimated the 4071.25 (USD / QALY) cost per AQALY gained of the monthly intake of 50,000 IU vitamin D for nine months among adolescents over a one-year horizon. Based on the ICER threshold of 1032-2666, vitamin D supplementation was cost-effective for adolescents to prevent adulthood T2DM. It means that vitamin D supplementation costs were substantially less than the costs of T2DM treatments than the no intervention. CONCLUSIONS: Based on the findings, the national vitamin D supplementation program for Iranian adolescents could be a cost-effective strategy to reduce the risk of diabetes in adulthood. From an economic perspective, vitamin D supplementation, especially in adolescents with vitamin D deficiency, would be administrated.

Enhancing gene regulatory networks inference through hub-based data integration.

One of the main research topics in computational biology is Gene Regulatory Network (GRN) reconstruction that refers to inferring the relationships between genes involved in regulating cell conditions in response to internal or external stimuli. To this end, most computational methods use only transcriptional gene expression data to reconstruct gene regulatory networks, but recent studies suggest that gene expression data must be integrated with other types of data to obtain more accurate models predicting real relationships between genes. In this study, a diffusion-based method is enhanced to integrate biological data of network types besides structural prior knowledge. The Random Walk with Restart algorithm (RWR) with an emphasis on hub nodes is executed separately on each network, and then jointly optimizes low-dimensional feature vectors for network nodes by diffusion component analysis. Next, these feature vectors are used to infer gene regulatory networks. Fourteen centrality measures are studied for the detection of hub nodes to be used in the RWR algorithm, and the best centrality measure having the greatest effect on the improvement of gene network inference is selected. A case study for the Saccharomyces cerevisiae and E. coli networks shows that using the proposed features in comparison with gene expression data alone results in 0.02-0.08 units improvement in Area Under Receiver Characteristic Operator (AUROC) criteria across different gene regulatory network inference methods. Furthermore, the proposed method was applied to the esophageal cancer data to infer its gene regulatory network. The proposed framework substantially improves accuracy and scalability of GRN inference. The fused features and the best centrality measure detected can be used to provide functional insights about genes or proteins in various biological applications. Moreover, it can be served as a general framework for network data and structural data integration and analysis problems in various scientific disciplines including biology.

The cost-effectiveness analysis of a nationwide vitamin D supplementation program among Iranian adolescents for adulthood cardiovascular diseases prevention.

OBJECTIVE: This study aimed to evaluate a national vitamin D supplementation program's cost-effectiveness among Iranian adolescents to prevent cardiovascular diseases (CVDs) in adulthood. STUDY DESIGN: A cost-effectiveness analytical study. METHODS: A decision tree model was adopted to evaluate the cost per quality-adjusted life-year (QALY) of monthly intake of nine pearls of 50,000 IU vitamin D for nine months to prevent CVD a one-year horizon compared to no intervention. The analysis was conducted in Iranian adolescents in first or second high school grades of 47 climatically different Iran regions. RESULTS: Our analytical analysis estimated the 1090$ cost per QALY gained of the monthly intake of 50,000 IU vitamin D for nine months among adolescents over a one-year horizon. Based on the incremental cost-effectiveness ratio (ICER) threshold of 1032-2666, vitamin D supplementation was cost-effective for adolescents to prevent adulthood CVD. It means that vitamin D supplementation costs were substantially less than the costs of CVD treatments compared to the no intervention. CONCLUSIONS: Based on these findings, the national program of vitamin D supplementation in adolescents would be cost effective to prevent CVD development in adulthood. From an economic perspective, vitamin D supplementation, especially in adolescents with vitamin D deficiency, would be administrated.

On the manipulation of flow and acoustic fields of a blunt trailing edge aerofoil by serrated leading edges.

This paper employs serrated leading edges to inject streamwise vorticity to the downstream boundary layer and wake to manipulate the flow field and noise sources near the blunt trailing edge of an asymmetric aerofoil. The use of a large serration amplitude is found to be effective to suppress the first noise source-bluntness-induced vortex shedding tonal noise-through the destruction of the coherent eigenmodes in the wake. The second noise source is the instability noise, which is produced by the interaction between the boundary layer instability and separation bubble near the blunt edge. The main criterion needed to suppress this noise source is related to a small serration wavelength because, through the generation of more streamwise vortices, it would facilitate a greater level of destructive interaction with the separation bubble. If the leading edge has both a large serration amplitude and wavelength, the interaction between the counter-rotating vortices themselves would trigger a turbulent shear layer through an inviscid mechanism. The turbulent shear layer will produce strong hydrodynamic pressure fluctuations to the trailing edge, which then scatter into broadband noise and transform into a trailing edge noise mechanism. This would become the third noise source that can be identified in several serrated leading edge configurations. Overall, a leading edge with a large serration amplitude and small serration wavelength appears to be the optimum choice to suppress the first and second noise sources and, at the same time, avoid the generation of the third noise source.

A hybrid clustering and classification approach for predicting crash injury severity on rural roads.

As a threat for transportation system, traffic crashes have a wide range of social consequences for governments. Traffic crashes are increasing in developing countries and Iran as a developing country is not immune from this risk. There are several researches in the literature to predict traffic crash severity based on artificial neural networks (ANNs), support vector machines and decision trees. This paper attempts to investigate the crash injury severity of rural roads by using a hybrid clustering and classification approach to compare the performance of classification algorithms before and after applying the clustering. In this paper, a novel rule-based genetic algorithm (GA) is proposed to predict crash injury severity, which is evaluated by performance criteria in comparison with classification algorithms like ANN. The results obtained from analysis of 13,673 crashes (5600 property damage, 778 fatal crashes, 4690 slight injuries and 2605 severe injuries) on rural roads in Tehran Province of Iran during 2011-2013 revealed that the proposed GA method outperforms other classification algorithms based on classification metrics like precision (86%), recall (88%) and accuracy (87%). Moreover, the proposed GA method has the highest level of interpretation, is easy to understand and provides feedback to analysts.