Genetic risk factors for cerebrovascular disease in children with sickle cell disease: design of a case-control association study and genomewide screen

BACKGROUND: The phenotypic heterogeneity of sickle cell disease is likely the result of multiple genetic factors and their interaction with the sickle mutation. High transcranial doppler (TCD) velocities define a subgroup of children with sickle cell disease who are at increased risk for developing ischemic stroke. The genetic factors leading to the development of a high TCD velocity (i.e. cerebrovascular disease) and ultimately to stroke are not well characterized. METHODS: We have designed a case-control association study to elucidate the role of genetic polymorphisms as risk factors for cerebrovascular disease as measured by a high TCD velocity in children with sickle cell disease. The study will consist of two parts: a candidate gene study and a genomewide screen and will be performed in 230 cases and 400 controls. Cases will include 130 patients (TCD ≥ 200 cm/s) randomized in the Stroke Prevention Trial in Sickle Cell Anemia (STOP) study as well as 100 other patients found to have high TCD in STOP II screening. Four hundred sickle cell disease patients with a normal TCD velocity (TCD < 170 cm/s) will be controls. The candidate gene study will involve the analysis of 28 genetic polymorphisms in 20 candidate genes. The polymorphisms include mutations in coagulation factor genes (Factor V, Prothrombin, Fibrinogen, Factor VII, Factor XIII, PAI-1), platelet activation/function (GpIIb/IIIa, GpIb IX-V, GpIa/IIa), vascular reactivity (ACE), endothelial cell function (MTHFR, thrombomodulin, VCAM-1, E-Selectin, L-Selectin, P-Selectin, ICAM-1), inflammation (TNFα), lipid metabolism (Apo A1, Apo E), and cell adhesion (VCAM-1, E-Selectin, L-Selectin, P-Selectin, ICAM-1). We will perform a genomewide screen of validated single nucleotide polymorphisms (SNPs) in pooled DNA samples from 230 cases and 400 controls to study the possible association of additional polymorphisms with the high-risk phenotype. High-throughput SNP genotyping will be performed through MALDI-TOF technology using Sequenom's MassARRAY™ system. DISCUSSION: It is expected that this study will yield important information on genetic risk factors for the cerebrovascular disease phenotype in sickle cell disease by clarifying the role of candidate genes in the development of high TCD. The genomewide screen for a large number of SNPs may uncover the association of novel polymorphisms with cerebrovascular disease and stroke in sickle cell disease

Genomic Methods for Bacterial Infection Identification

Hospital-acquired infections (HAIs) have high mortality rates around the world and are a challenge to medical science due to rapid mutation rates in their pathogens. A new methodology is proposed to identify bacterial species causing HAIs based on sets of universal biomarkers for next-generation microarray designs (i.e., nxh chips), rather than a priori selections of biomarkers. This method allows arbitrary organisms to be classified based on readouts of their DNA sequences, including whole genomes. The underlying models are based on the biochemistry of DNA, unlike traditional edit-distance based alignments. Furthermore, the methodology is fairly robust to genetic mutations, which are likely to reduce accuracy. Standard machine learning methods (neural networks, self-organizing maps, and random forests) produce results to identify HAIs on nxh chips that are very competitive, if not superior, to current standards in the field. The potential feasibility of translating these techniques to a clinical test is also discussed

How do red and infrared low-level lasers affect folliculogenesis cycle in rat’s ovary tissue in comparison with clomiphene under in vivo condition

Tabriz University of Medical Sciences, 5/89/137Peer reviewedPublisher PD

Rapid Commun Mass Spectrom

RATIONALE:Metabolomics analyses using gas chromatography mass spectrometry (GC/MS) - based metabolomics are heavily impeded by the lack of high-resolution mass spectrometers and limited spectral libraries to complement the excellent chromatography that GC platforms offer, a challenge that is being addressed with the implementation of high resolution (HR) platforms such as 1D-GC/Orbitrap-MS.METHODS:We used serum samples from a non-human primate (NHP), a baboon (Papio hamadryas), with suitable quality controls to quantify the chemical space using an advanced HR MS platform for confident metabolite identification and robust quantification to assess the suitability of the platform for routine clinical metabolomics research. In a complementary approach, we also analyzed the same serum samples using a two-dimensional gas chromatography time-of-flight mass-spectrometer (2D-GC/ToF-MS) for metabolite identification and quantification following established standard protocols.RESULTS:Overall, the 2D-GC/ToF-MS (~5000 peaks per sample) and 1D-GC/Orbitrap-MS (~500 peaks per sample) analyses enabled identification and quantification of a total of 555 annotated metabolites from the NHP serum with a spectral similarity score Rsim 65 900 and S/N ratio of > 25. A common set of 30 metabolites with HMDB and KEGG IDs was quantified in the serum samples by both platforms where 2D-GC/ToF-MS enabled quantification of a total 384 metabolites (118 HMDB IDs) and 1D-GC/Orbitrap-MS analysis quantification of a total 200 metabolites (47 HMDB IDs). Thus, roughly 30\u201370% of the peaks remain unidentified or un-annotated across both platforms.CONCLUSIONS:Our study provides insights into the benefits and limitations of the use of a higher mass resolution and mass accuracy instrument for untargeted GC/MS-based metabolomics with multi-dimensional chromatography in future studies addressing clinical conditions or exposome studies.P51 OD011133/ODCDC CDC HHS/Office of the Director/United States2019-09-15T00:00:00Z29874398PMC6395519665

Negative Ion Formation in Potassium-Purine Molecules collisions

The research described in this thesis focuses on the study of electron transfer mechanisms in purine molecules and derivatives (adenine, 9-methyl adenine, 6-dimethyl adenine and 2-D adenine), in potassium-molecule collisions. The studies were performed in a crossed beam experiment, comprising a neutral potassium beam and a biomolecular effusive beam with a time-of-flight mass spectrometer and a recently implemented hemispherical analyser, yielding an experimental arrangement capable of providing relevant information of the collision dynamics. From this comprehensive investigation, we report for the first time, collision induced site and bond selective breaking in purine molecules by alkali collisions. The influence of the K+ ion in the vicinity of the temporary molecular anion was also investigated, indicating to partially suppress auto-detachment resulting in new or enhanced dissociation pathways. Concerning the energy loss set-up, we present in the 0 to 15 eV energy range novel K+ profiles in the forward direction ( 0 ) from fast potassium collisions with nitromethane and tetrachloromethane where new features are unravelled, and reported for the first time as far as akali collisions are concerned. Due to the current configuration, it restricts the use of this technique exclusively to samples with high vapour pressure. The potassium beam energy resolution was determined to be 0.6 eV in the laboratory frame

Comparative metabolitefingerprinting of legumes using LC-MS-baseduntargeted metabolomics

Legumes are a well-known source of phytochemicals and are commonly believed to have similar composition between different genera. To date, there are no studies evaluating changes in legumes to discover those compounds that help to discriminate for food quality and authenticity. The aim of this work was to characterize and make a comparative analysis of the composition of bioactive compounds between Cicer arietinum L. (chickpea), Lens culinaris L. (lentil) and Phaseolus vulgaris L. (white bean) through an LC-MS-Orbitrap metabolomic approach to establish which compounds discriminate between the three studied legumes. Untargeted metabolomic analysis was carried out by LC-MS-Orbitrap from extracts of freeze-dried legumes prepared from pre-cooked canned legumes. The metabolomic data treatment and statistical analysis were realized by using MAIT R's package, and final identification and characterization was done using MSn experiments. Fold-change evaluation was made through Metaboanalyst 4.0. Results showed 43 identified and characterized compounds displaying differences between the three legumes. Polyphenols, mainly flavonol and flavanol compounds, were the main group with 30 identified compounds, followed by α-galactosides (n = 5). Fatty acyls, prenol lipids, a nucleoside and organic compounds were also characterized. The fold-change analysis showed flavanols as the wider class of discriminative compounds of lentils compared to the other legumes; prenol lipids and eucomic acids were the most discriminative compounds of beans versus other legumes and several phenolic acids (such as primeveroside salycilic), kaempferol derivatives, coumesterol and α-galactosides were the most discriminative compounds of chickpeas. This study highlights the applicability of metabolomics for evaluating which are the characteristic compounds of the different legumes. In addition, it describes the future application of metabolomics as tool for the quality control of foods and authentication of different kinds of legumes

Feature selection and nearest centroid classification for protein mass spectrometry

BACKGROUND: The use of mass spectrometry as a proteomics tool is poised to revolutionize early disease diagnosis and biomarker identification. Unfortunately, before standard supervised classification algorithms can be employed, the "curse of dimensionality" needs to be solved. Due to the sheer amount of information contained within the mass spectra, most standard machine learning techniques cannot be directly applied. Instead, feature selection techniques are used to first reduce the dimensionality of the input space and thus enable the subsequent use of classification algorithms. This paper examines feature selection techniques for proteomic mass spectrometry. RESULTS: This study examines the performance of the nearest centroid classifier coupled with the following feature selection algorithms. Student-t test, Kolmogorov-Smirnov test, and the P-test are univariate statistics used for filter-based feature ranking. From the wrapper approaches we tested sequential forward selection and a modified version of sequential backward selection. Embedded approaches included shrunken nearest centroid and a novel version of boosting based feature selection we developed. In addition, we tested several dimensionality reduction approaches, namely principal component analysis and principal component analysis coupled with linear discriminant analysis. To fairly assess each algorithm, evaluation was done using stratified cross validation with an internal leave-one-out cross-validation loop for automated feature selection. Comprehensive experiments, conducted on five popular cancer data sets, revealed that the less advocated sequential forward selection and boosted feature selection algorithms produce the most consistent results across all data sets. In contrast, the state-of-the-art performance reported on isolated data sets for several of the studied algorithms, does not hold across all data sets. CONCLUSION: This study tested a number of popular feature selection methods using the nearest centroid classifier and found that several reportedly state-of-the-art algorithms in fact perform rather poorly when tested via stratified cross-validation. The revealed inconsistencies provide clear evidence that algorithm evaluation should be performed on several data sets using a consistent (i.e., non-randomized, stratified) cross-validation procedure in order for the conclusions to be statistically sound