Serum vitamin D and asthma control still a controversial link: A cross-sectional study and literature review

BackgroundVitamin D is an important modulator of the innate and adaptive immune system, plays an important role in the airway hyperresponsiveness and the improved asthma control. Several studies suggest that low serum vitamin D may adversely influence asthma outcomes. However, other studies showed inconsistent relationship which increased the controversy of the link between low vitamin D and asthma control.AimsThe aim of this study was to investigate the potential relationship between low serum vitamin D with asthma control.MethodsA cross-sectional study of consecutive patients with the diagnosis of asthma from the outpatient clinic at King Abdulaziz University hospital was analysed between January and December 2011. They were classified according to their asthma control level. Measurement of serum vitamin D was performed. SPSS was used to examine any statistical correlation.ResultsSixty-four asthmatic patients were included in this study; 31.25 per cent (n=20) males and 68.75 per cent (n=44) were females. Serum vitamin D (25-hydroxyvitamin D) deficiency (Less than 50nmol per litre) was found in 84.3 per cent (n=54), insufficiency (50 to 74.9 nmol per litre) in 14.1 per cent (n=9), and sufficient serum level (75 nmol per litre or greater) in 1.6 per cent (n=1) patients. Level of asthma control assessment based on GINA guideline revealed 25 (39 per cent) uncontrolled, 27 (42.2 per cent) partially controlled and 12 (18.8 per cent) controlled patients. Low vitamin D was found in 12 (19 per cent) controlled versus 51 (81 per cent) non-controlled asthmatics. There was no significant statistical correlation found between low serum vitamin D level and asthma control (p value 0.85).ConclusionLow vitamin D was prevalent in more than three-quarters of patients with asthma. The relationship between low serum vitamin D level and poor asthma control was not statistically significant. Further studies are needed to explore the association of low vitamin D with asthma control

Effect of complementary irrigation on yield components and alternate bearing of a traditional olive orchard in semi-arid conditions

Traditional olive orchards are usually not irrigated in the Mediterranean basin, but at those latitudes, the yearly rainfall is frequently insufficient to support equilibrated vegetative growth and high fruit and oil production. This three-year field study investigated the effect of complementary irrigation on olive tree vegetative growth, fruit and oil yield during a biennial alternate bearing cycle in a traditional grove under semi-arid conditions. Adult olive trees (Olea europaea L. cv. Nabali Baladi) were subjected to complementary irrigation in 2011 and 2012 ('on' and 'off' years, respectively) with 6, 10, 15 or 20 m3 of water per tree per season, which corresponded to 14.2%, 23.8%, 35.7% and 47.6% of the whole seasonal evapotranspiration (42 m3 of water per year), respectively. Rain-fed trees were used as control. In 2013, no complementary irrigation was supplied, and any residual effects on the yield components were determined. Results showed that none of the irrigation regimes affected vegetative growth, or olive fruit size (mesocarp and endocarp), as fresh and dry weights. The fruit and oil yield per tree increased compared to the rain-fed conditions only when the threshold of 15 m3 was exceeded, thus inducing a higher crop load compared to the rain-fed control during the 'off' and even further during the 'on' year. No residual effects were registered in 2013. The study showed that complementary irrigation of at least 35% of the seasonal water requirement can produce remarkable positive effects on fruit yield especially during 'on' bearing years

A phased SNP-based classification of sickle cell anemia HBB haplotypes

Background: Sickle cell anemia causes severe complications and premature death. Five common beta-globin gene cluster haplotypes are each associated with characteristic fetal hemoglobin (HbF) levels. As HbF is the major modulator of disease severity, classifying patients according to haplotype is useful. The first method of haplotype classification used restriction fragment length polymorphisms (RFLPs) to detect single nucleotide polymorphisms (SNPs) in the beta-globin gene cluster. This is labor intensive, and error prone. Methods: We used genome-wide SNP data imputed to the 1000 Genomes reference panel to obtain phased data distinguishing parental alleles. Results: We successfully haplotyped 813 sickle cell anemia patients previously classified by RFLPs with a concordance >98%. Four SNPs (rs3834466, rs28440105, rs10128556, and rs968857) marking four different restriction enzyme sites unequivocally defined most haplotypes. We were able to assign a haplotype to 86% of samples that were either partially or misclassified using RFLPs. Conclusion: Phased data using only four SNPs allowed unequivocal assignment of a haplotype that was not always possible using a larger number of RFLPs. Given the availability of genome-wide SNP data, our method is rapid and does not require high computational resources.NIH Bethesda, MDBoston Univ, Sch Med, Dept Med, 72 E Concord St, Boston, MA 02118 USABoston Univ, Bioinformat Program, Boston, MA 02215 USAKing Saud Univ, Coll Med, Sickle Cell Dis Res Ctr, Riyadh, Saudi ArabiaKing Saud Univ, Coll Med, Dept Pediat, Riyadh, Saudi ArabiaKing Faisal Univ, Al Omran Sci Chair, Al Hasa, Saudi ArabiaImam Abdulrahman bin Faisal Univ, Inst Res & Med Consultat, Dammam, Saudi ArabiaEscola Paulista Med, Hematol & Blood Transfus Div, São Paulo, BrazilBoston Univ, Sch Publ Hlth, Dept Biostat, Boston, MA 02118 USAEscola Paulista Med, Hematol & Blood Transfus Div, São Paulo, BrazilNIH: R01 HL 068970NIH: RC2 HL 101212NIH: R01 87681NIH: T32 HL007501Web of Scienc

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense