The role of post-transcriptional regulation in chemokine gene expression in inflammation and allergy.

The aim of this review is to discuss recent advances in the understanding of the regulation of chemokine expression occurring during chronic inflammatory conditions, such as allergic diseases. The focus will be on current data, which suggest that post-transcriptional regulation plays a larger role in chemokine gene regulation than previously recognised. In particular, a growing body of data indicates that mechanisms controlling mRNA stability may be relevant in determining, or maintaining, the increased levels of chemokine gene expression in this context. Such regulatory pathways may be important targets of novel anti-inflammatory strategies

Elevated maternal lipoprotein (a) and neonatal renal vein thrombosis: a case report

<p>Abstract</p> <p>Introduction</p> <p>Renal vein thrombosis, although rare in adults, is well recognized in neonates and is one of the most common manifestations of neonatal thromboembolic events. The etiology of renal vein thrombosis remains unidentified in the majority of cases. We report a case of renal vein thrombosis in a neonate associated with elevated maternal lipoprotein (a).</p> <p>Case presentation</p> <p>A full-term female infant, appropriate for gestational age, was born via spontaneous vaginal delivery to an 18-year-old primigravida. The infant's birth weight was 3680 g and the Apgar scores were eight and nine at 1 and 5 minutes respectively. Evaluation of the infant in the newborn nursery revealed a palpable mass in the right lumbar area. Tests revealed hematuria and a high serum creatinine level of 1.5 mg/dl. An abdominal ultrasound Doppler flow study demonstrated an enlarged right kidney, right renal vein thrombosis, and progression of the thrombosis to the inferior vena cava. There was no evidence of saggital sinus thrombosis. An extensive work-up of parents for hypercoagulable conditions was remarkable for a higher plasma lipoprotein (a) level of 73 mg/dl and an elevated fibrinogen level of 512 mg/dl in the mother. All paternal levels were normal. The plasma lipoprotein (a) level in the neonate was also normal. The neonate was treated with low molecular weight heparin (enoxaparin) at 1.5 mg/kg/day every 12 hours for 2 months, at which time a follow-up ultrasound Doppler flow study showed resolution of the thrombosis in both the renal vein and the inferior vena cava.</p> <p>Conclusion</p> <p>There have been no studies to date that have explored the effect of abnormal maternal risk factors on fetal hemostasis. A case-control study is required to investigate whether elevated levels of maternal lipoprotein (a) may be a risk factor for neonatal thrombotic processes. Although infants with this presentation are typically treated with anticoagulation, there is a lack of evidence-based guidelines. Treatment modalities vary between study and treatment centers which warrants the establishment of a national registry.</p

Validating module network learning algorithms using simulated data

In recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Here, we demonstrate the use of the synthetic data generator SynTReN for the purpose of testing and comparing module network learning algorithms. We introduce a software package for learning module networks, called LeMoNe, which incorporates a novel strategy for learning regulatory programs. Novelties include the use of a bottom-up Bayesian hierarchical clustering to construct the regulatory programs, and the use of a conditional entropy measure to assign regulators to the regulation program nodes. Using SynTReN data, we test the performance of LeMoNe in a completely controlled situation and assess the effect of the methodological changes we made with respect to an existing software package, namely Genomica. Additionally, we assess the effect of various parameters, such as the size of the data set and the amount of noise, on the inference performance. Overall, application of Genomica and LeMoNe to simulated data sets gave comparable results. However, LeMoNe offers some advantages, one of them being that the learning process is considerably faster for larger data sets. Additionally, we show that the location of the regulators in the LeMoNe regulation programs and their conditional entropy may be used to prioritize regulators for functional validation, and that the combination of the bottom-up clustering strategy with the conditional entropy-based assignment of regulators improves the handling of missing or hidden regulators.Comment: 13 pages, 6 figures + 2 pages, 2 figures supplementary informatio

EC-BLAST: a tool to automatically search and compare enzyme reactions.

We present EC-BLAST (http://www.ebi.ac.uk/thornton-srv/software/rbl/), an algorithm and Web tool for quantitative similarity searches between enzyme reactions at three levels: bond change, reaction center and reaction structure similarity. It uses bond changes and reaction patterns for all known biochemical reactions derived from atom-atom mapping across each reaction. EC-BLAST has the potential to improve enzyme classification, identify previously uncharacterized or new biochemical transformations, improve the assignment of enzyme function to sequences, and assist in enzyme engineering

Prospecting environmental mycobacteria: combined molecular approaches reveal unprecedented diversity

Background: Environmental mycobacteria (EM) include species commonly found in various terrestrial and aquatic environments, encompassing animal and human pathogens in addition to saprophytes. Approximately 150 EM species can be separated into fast and slow growers based on sequence and copy number differences of their 16S rRNA genes. Cultivation methods are not appropriate for diversity studies; few studies have investigated EM diversity in soil despite their importance as potential reservoirs of pathogens and their hypothesized role in masking or blocking M. bovis BCG vaccine. Methods: We report here the development, optimization and validation of molecular assays targeting the 16S rRNA gene to assess diversity and prevalence of fast and slow growing EM in representative soils from semi tropical and temperate areas. New primer sets were designed also to target uniquely slow growing mycobacteria and used with PCR-DGGE, tag-encoded Titanium amplicon pyrosequencing and quantitative PCR. Results: PCR-DGGE and pyrosequencing provided a consensus of EM diversity; for example, a high abundance of pyrosequencing reads and DGGE bands corresponded to M. moriokaense, M. colombiense and M. riyadhense. As expected pyrosequencing provided more comprehensive information; additional prevalent species included M. chlorophenolicum, M. neglectum, M. gordonae, M. aemonae. Prevalence of the total Mycobacterium genus in the soil samples ranged from 2.3×107 to 2.7×108 gene targets g−1; slow growers prevalence from 2.9×105 to 1.2×107 cells g−1. Conclusions: This combined molecular approach enabled an unprecedented qualitative and quantitative assessment of EM across soil samples. Good concordance was found between methods and the bioinformatics analysis was validated by random resampling. Sequences from most pathogenic groups associated with slow growth were identified in extenso in all soils tested with a specific assay, allowing to unmask them from the Mycobacterium whole genus, in which, as minority members, they would have remained undetected

Lipoprotein (a), C-reactive protein and some metabolic cardiovascular risk factors in type 2 DM

<p>Abstract</p> <p>Background</p> <p>Lipoprotein (a) (LP (a) is an independent cardiovascular risk factor that is not widely studied in people of sub-Saharan African origin. The aim of this report is to determine the frequency of occurrence of elevated Lp (a) and possible relationship with total cholesterol (TCHOL), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), triglycerides (TG), C reactive protein (CRP) and serum uric acid (SUA).</p> <p>Methods</p> <p>This is a cross sectional study carried out in 200 Nigerian patients with type 2 DM and 100 sex and age matched healthy Controls aged between 32-86 years. We determined the frequency of occurrence of elevated Lp (a) levels in the study subjects and compared clinical and biochemical variables between type 2 diabetic patients and non-diabetic patients. Clinical and biochemical parameters were also compared between subjects with type 2 DM who had elevated LP (a) and normal LP (a) levels. Long term glycaemic control using glycosylated haemoglobin was determined and compared in the study subjects. Test statistics used include chi square, correlation coefficient analysis and Student's t test.</p> <p>Results</p> <p>The mean Lp(a) concentration differed significantly between type 2 diabetic patients and the Control subjects (18.7 (5.8) mg/dl vs 23 (6.8) mg/dl, 0.00001). Similarly, the prevalence of high LP (a) levels in type 2 DM patients was significantly higher than that of the Control subjects (12.5% vs 4%, p-0.019). The mean levels of the lipid profile parameters (TCHOL, LDL-C, TG, LDL/HDL) and CRP were significantly higher in DM patients than in the Control subjects. The mean LP (a) levels were comparable in both sexes and in DM subjects with and without hypertension. TG was the only parameter that differed significantly between subjects with elevated Lp (a) levels and those with normal Lp (a) levels. There was a significant positive correlation (r) between Lp(a) levels and TG, LDL-C. TCHOL, LDL/HDL and uric acid. No association was found between Lp(a) and clinical parameters such as age and anthropometric indices.</p> <p>Conclusion</p> <p>We have showed that Lp (a), CRP and other CVS risk factors cluster more in patients with DM than non DM patients. Serum Lp (a) levels are not associated with anthropometric and glycaemic indices.</p

Social Networks among Elderly Women: Implications for Health Education Practice

The general aim of the present study was to examine and help clarify the properties of the distinctions between social networks and social support, their relationship to health status, and their impli cations for health education practice. More specifically, a secondary data analysis was conducted with 130 white women, community resi dents, between the ages of 60 and 68, which examined the relationship between psychological well-being and social network characteristics. These characteristics are categorized along three broad dimensions: structure—links in the overall network (size and density); interaction— nature of the linkages themselves (frequency, homogeneity, content, reciprocity, intensity, and dispersion); and functions which networks provide (affective support and instrumental support). A combination was made and relative strength investigated of several network char acteristics representative of the quality of interactions (i. e., reciprocal affective support, intensity, and affective support) and those repre senting the quantity of interactions (i.e., size, density, and frequency).Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/67277/2/10.1177_109019818301000304.pd

Artificial intelligence in biological activity prediction

Artificial intelligence has become an indispensable resource in chemoinformatics. Numerous machine learning algorithms for activity prediction recently emerged, becoming an indispensable approach to mine chemical information from large compound datasets. These approaches enable the automation of compound discovery to find biologically active molecules with important properties. Here, we present a review of some of the main machine learning studies in biological activity prediction of compounds, in particular for sweetness prediction. We discuss some of the most used compound featurization techniques and the major databases of chemical compounds relevant to these tasks.This study was supported by the European Commission through project SHIKIFACTORY100 - Modular cell factories for the production of 100 compounds from the shikimate pathway (Reference 814408), and by the Portuguese FCT under the scope of the strategic funding of UID/BIO/04469/2019 unit and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020.info:eu-repo/semantics/publishedVersio

Observation of associated near-side and away-side long-range correlations in √sNN=5.02 TeV proton-lead collisions with the ATLAS detector

Two-particle correlations in relative azimuthal angle (Δϕ) and pseudorapidity (Δη) are measured in √sNN=5.02  TeV p+Pb collisions using the ATLAS detector at the LHC. The measurements are performed using approximately 1  μb-1 of data as a function of transverse momentum (pT) and the transverse energy (ΣETPb) summed over 3.1<η<4.9 in the direction of the Pb beam. The correlation function, constructed from charged particles, exhibits a long-range (2<|Δη|<5) “near-side” (Δϕ∼0) correlation that grows rapidly with increasing ΣETPb. A long-range “away-side” (Δϕ∼π) correlation, obtained by subtracting the expected contributions from recoiling dijets and other sources estimated using events with small ΣETPb, is found to match the near-side correlation in magnitude, shape (in Δη and Δϕ) and ΣETPb dependence. The resultant Δϕ correlation is approximately symmetric about π/2, and is consistent with a dominant cos⁡2Δϕ modulation for all ΣETPb ranges and particle pT