Meta-analysis of single trait for multiple cohorts has been used for increasing statistical power in genome-wide association studies (GWASs). Although hundreds of variants have been identified by GWAS, these variants only explain a small fraction of phenotypic variation. Cross-phenotype association analysis (CPASSOC) can further improve statistical power by searching for variants that contribute to multiple traits, which is often relevant to pleiotropy. In this study, we performed CPASSOC analysis on the summary statistics from the Genetic Investigation of ANthropometric Traits (GIANT) consortium using a novel method recently developed by our group. Sex-specific meta-analysis data for height, body mass index (BMI), and waist-to-hip ratio adjusted for BMI (WHRadjBMI) from discovery phase of the GIANT consortium study were combined using CPASSOC for each trait as well as 3 traits together. The conventional meta-analysis results from the discovery phase data of GIANT consortium studies were used to compare with that from CPASSOC analysis. The CPASSOC analysis was able to identify 17 loci associated with anthropometric traits that were missed by conventional meta-analysis. Among these loci, 16 have been reported in literature by including additional samples and 1 is novel. We also demonstrated that CPASSOC is able to detect pleiotropic effects when analyzing multiple traits.
Fusing information from different imaging modalities is crucial for more accurate identification of the brain state because imaging data of different modalities can provide complementary perspectives on the complex nature of brain disorders. However, most existing fusion methods often extract features independently from each modality, and then simply concatenate them into a long vector for classification, without appropriate consideration of the correlation among modalities. In this paper, we propose a novel method to transform the original features from different modalities to a common space, where the transformed features become comparable and easy to find their relation, by canonical correlation analysis. We then perform the sparse multi-task learning for discriminative feature selection by using the canonical features as regressors and penalizing a loss function with a canonical regularizer. In our experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, we use Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) images to jointly predict clinical scores of Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog) and Mini-Mental State Examination (MMSE) and also identify multi-class disease status for Alzheimer’s disease diagnosis. The experimental results showed that the proposed canonical feature selection method helped enhance the performance of both clinical score prediction and disease status identification, outperforming the state-of-the-art methods.
Alzheimer’s disease; Feature selection; Canonical correlation analysis; Multi-class classification; Mild cognitive impairment conversion
Molecules that alter the normal dynamics of microtubule assembly and disassembly include many anticancer drugs in clinical use. So far all such therapeutics target β-tubulin, and structural biology has explained the basis of their action and permitted design of new drugs. However, by shifting the profile of β-tubulin isoforms, cancer cells become resistant to treatment. Compounds that bind to α-tubulin are less well characterized and unexploited. The natural product pironetin is known to bind to α-tubulin and is a potent inhibitor of microtubule polymerization. Previous reports had identified that pironetin reacts with lysine-352 residue however analogues designed on this model had much lower potency, which was difficult to explain, hindering further development. We report crystallographic and mass spectrometric data that reveal that pironetin forms a covalent bond to cysteine-316 in α-tubulin via a Michael addition reaction. These data provide a basis for the rational design of α-tubulin targeting chemotherapeutics.
Microtubule assembly and disassembly is the target of many anticancer therapies, with β-tubulin the most-frequent target. Here, the authors used biochemical and biophysical techniques to demonstrate pironetin binds to α-tubulin and thereby inhibits microtubule polymerization providing a basis for the rational design of novel anticancer drugs.
A challenge preventing routine clinical implementation of Monte Carlo (MC)-based lung SBRT is the difficulty of reinterpreting historical outcome data calculated with inaccurate dose algorithms, because the target dose was found to decrease to varying degrees when recalculated with MC. The large variability was previously found to be affected by factors such as tumour size, location, and lung density, usually through sub-group comparisons. We hereby conducted a pilot study to systematically and quantitatively analyze these patient factors and explore accurate target dose conversion models, so that large-scale historical outcome data can be correlated with more accurate MC dose without recalculation.
Twenty-one patients that underwent SBRT for early-stage lung cancer were replanned with 6MV 360° dynamic conformal arcs using pencil-beam (PB) and recalculated with MC. The percent D95 difference (PB-MC) was calculated for the PTV and GTV. Using single linear regression, this difference was correlated with the following quantitative patient indices: maximum tumour diameter (MaxD); PTV and GTV volumes; minimum distance from tumour to soft tissue (dmin); and mean density and standard deviation of the PTV, GTV, PTV margin, lung, and 2 mm, 15 mm, 50 mm shells outside the PTV. Multiple linear regression and artificial neural network (ANN) were employed to model multiple factors and improve dose conversion accuracy.
Single linear regression with PTV D95 deficiency identified the strongest correlation on mean-density (location) indices, weaker on lung density, and the weakest on size indices, with the following R2 values in decreasing orders: shell2mm (0.71), PTV (0.68), PTV margin (0.65), shell15mm (0.62), shell50mm (0.49), lung (0.40), dmin (0.22), GTV (0.19), MaxD (0.17), PTV volume (0.15), and GTV volume (0.08). A multiple linear regression model yielded the significance factor of 3.0E-7 using two independent features: mean density of shell2mm (P = 1.6E-7) and PTV volume (P = 0.006). A 4-feature ANN model slightly improved the modeling accuracy.
Quantifiable density features were proposed, replacing simple central/peripheral location designation, which showed strong correlations with PB-to-MC target dose conversion magnitude, followed by lung density and target size. Density in the immediate outer and inner areas of the PTV showed the strongest correlations. A multiple linear regression model with one such feature and PTV volume established a high significance factor, improving dose conversion accuracy.
Lung SBRT; Monte Carlo; Prescription; Target dose variation
To develop effective methods for genome wide association studies (GWAS) in admixed populations, such as African Americans.
We show that when testing the null hypothesis that the test single nucleotide polymorphism (SNP) is not in background linkage disequilibrium (LD) with the causal variants, several existing methods cannot control well the family-wise error rate (FWER) in the strong sense in GWAS; the existing methods include association tests adjusting for global ancestry and joint association tests that combine statistics from admixture mapping tests and association tests that correct for local ancestry. Furthermore, we describe a generalized sequential Bonferroni (smooth-GSB) procedure for GWAS that incorporates smoothed weights calculated from admixture mapping tests into association tests that correct for local ancestry. We have applied the smooth-GSB procedure to analyses of GWAS data on American Africans from the Atherosclerosis Risk in Communities (ARIC) Study.
Our simulation studies indicate that the smooth-GSB procedure not only can control the FWER, but also improve statistical power compared with association tests correcting for local ancestry.
The smooth-GSB procedure can result in a better performance than several existing methods for GWAS in admixed populations.
Admixture mapping; GWAS; sequential Bonferroni procedures; admixture LD; background LD
Icariin (ICA), the main active flavonoid glucoside isolated from Herba Epimedii, has been shown to prevent postmenopausal bone loss in vitro. However, the mechanisms by which ICA prevents bone loss in vivo remain poorly understood. In the present study, the effect of ICA in an ovariectomized (OVX) rat model of osteoporosis was evaluated. Sprague-Dawley rats were divided into sham-operated and OVX groups. The OVX rats were randomly divided into five groups: OVX group (water only), Fosamax (positive) group (5.04 mg/kg, weekly, administered orally), and OVX-ICA groups (125, 250 or 500 mg/kg, daily, administered orally) and treated for 12 weeks. The 125, 250 and 500 mg/kg doses of ICA were designated as low (L-ICA), medium (M-ICA) and high (H-ICA), respectively. Compared with the sham-operated group, the OVX rats had significantly decreased bone mineral density (BMD), reduced serum osteoprotegerin (OPG) and increased serum bone gla protein (BGP) concentrations. ICA significantly increased BMD, biomechanical strength, trabecular bone number and trabecular bone thickness, and reduced lumbar trabecular bone separation. Treatment with ICA also completely normalized the expression of osteoblast markers by increasing serum concentrations of OPG and BGP. Enhanced mineralization was demonstrated by increased expression of differentiation markers. Although further in vivo studies are required to investigate the efficacy of ICA in improving bone mass, this study demonstrates that ICA has strong osteogenic activity, inducing osteogenic differentiation and inhibiting resorption by osteoclasts. It also demonstrates an antiosteoporotic effect for ICA on the basis of BMD, biochemical markers, biomechanical tests and histopathological parameters. Compared with L-ICA and H-ICA, M-ICA was more effective and caused no liver or kidney damage.
icariin; Herba Epimedii; osteoporosis; mechanism; in vivo
It has been reported that RhoA activation and Rho-kinase (ROCK) expression are increased in chronic hypoxic lungs, and the long-term inhibition of ROCK markedly improves the survival of patients with pulmonary arterial hypertension (PAH). However, whether Rho-kinase α (ROCK2) participates in regulation of the growth of pulmonary arterial endothelial cells (PAECs) remains unknown. The aim of the present study was to investigate the effect of hypoxia on the proliferation of PAECs and the role of ROCK2 in the underlying mechanism. The results of western blotting and reverse transcription-quantitative polymerase chain reaction analysis showed that hypoxia increased the activity and expression of ROCK2 in PAECs, and the stimulating effects of hypoxia on the proliferation of PAECs were attenuated by either the ROCK inhibitor Y27632 or transfection with ROCK2 small interfering RNA. Moreover, analysis of cyclin A and cyclin D1 mRNA expression indicated that ROCK2 mediates the cell cycle progression promoted by hypoxia. These results indicate that hypoxia promotes the proliferation of pulmonary arterial endothelial cells via activation of the ROCK2 signaling pathway.
hypoxia; pulmonary arterial endothelial cells; proliferation; ROCK2; pulmonary arterial hypertension
The purpose of the study is to describe the values and distribution of corneal epithelium thickness (CET) in normal Chinese school-aged children, and to explore associated factors with CET. CET maps were measured by Fourier-domain optical coherence tomography (FD-OCT) in normal Chinese children aged 7 to 15 years old from two randomly selected schools in Shanghai, China. Children with normal intraocular pressure were further examined for cycloplegic autorefraction, corneal curvature radius (CCR) and axial length. Central (2-mm diameter area), para-central (2- to 5-mm diameter area), and peripheral (5- to 6-mm diameter area) CET in the superior, superotemporal, temporal, inferotemporal, inferior, inferonasal, nasal, superonasal cornea; minimum, maximum, range, and standard deviation of CET within the 5-mm diameter area were recorded. The CET was thinner in the superior than in the inferior and was thinner in the temporal than in the nasal. The maximum CET was located in the inferior zone, and the minimum CET was in the superior zone. A thicker central CET was associated with male gender (p = 0.009) and older age (p = 0.037) but not with CCR (p = 0.061), axial length (p = 0.253), or refraction (p = 0.351) in the multiple regression analyses. CCR, age, and gender were correlated with para-central and peripheral CET.
In this paper, we propose a multi-view learning method using Magnetic Resonance Imaging (MRI) data for Alzheimer’s Disease (AD) diagnosis. Specifically, we extract both Region-Of-Interest (ROI) features and Histograms of Oriented Gradient (HOG) features from each MRI image, and then propose mapping HOG features onto the space of ROI features to make them comparable and to impose high intra-class similarity with low inter-class similarity. Finally, both mapped HOG features and original ROI features are input to the support vector machine for AD diagnosis. The purpose of mapping HOG features onto the space of ROI features is to provide complementary information so that features from different views can not only be comparable (i.e., homogeneous) but also be interpretable. For example, ROI features are robust to noise, but lack of reflecting small or subtle changes, while HOG features are diverse but less robust to noise. The proposed multi-view learning method is designed to learn the transformation between two spaces and to separate the classes under the supervision of class labels. The experimental results on the MRI images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset show that the proposed multi-view method helps enhance disease status identification performance, outperforming both baseline methods and state-of-the-art methods.
Admixture mapping of lipids was followed-up by family-based association analysis to identify variants for cardiovascular disease in African-Americans.
Methods and Results
The present study conducted admixture mapping analysis for total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and triglycerides. The analysis was performed in 1,905 unrelated African-American subjects from the National Heart, Lung and Blood Institute’s Family Blood Pressure Program. Regions showing admixture evidence were followed-up with family-based association analysis in 3,556 African-American subjects from the FBPP. The admixture mapping and family-based association analyses were adjusted for age, age2, sex, body-mass-index, and genome-wide mean ancestry to minimize the confounding due to population stratification. Regions that were suggestive of local ancestry association evidence were found on chromosomes 7 (LDL-C), 8 (HDL-C), 14 (triglycerides) and 19 (total cholesterol and triglycerides). In the fine-mapping analysis, 52,939 SNPs were tested and 11 SNPs (8 independent SNPs) showed nominal significant association with HDL-C (2 SNPs), LDL-C (4 SNPs) and triglycerides (5 SNPs). The family data was used in the fine-mapping to identify SNPs that showed novel associations with lipids and regions including genes with known associations for cardiovascular disease.
This study identified regions on chromosomes 7, 8, 14 and 19 and 11 SNPs from the fine-mapping analysis that were associated with HDL-C, LDL-C and triglycerides for further studies of cardiovascular disease in African-Americans.
lipids; genetics; association studies; African-Americans; admixture mapping analysis
Homozygosity has long been associated with rare, often devastating, Mendelian disorders1 and Darwin was one of the first to recognise that inbreeding reduces evolutionary fitness2. However, the effect of the more distant parental relatedness common in modern human populations is less well understood. Genomic data now allow us to investigate the effects of homozygosity on traits of public health importance by observing contiguous homozygous segments (runs of homozygosity, ROH), which are inferred to be homozygous along their complete length. Given the low levels of genome-wide homozygosity prevalent in most human populations, information is required on very large numbers of people to provide sufficient power3,4. Here we use ROH to study 16 health-related quantitative traits in 354,224 individuals from 102 cohorts and find statistically significant associations between summed runs of homozygosity (SROH) and four complex traits: height, forced expiratory lung volume in 1 second (FEV1), general cognitive ability (g) and educational attainment (nominal p<1 × 10−300, 2.1 × 10−6, 2.5 × 10−10, 1.8 × 10−10). In each case increased homozygosity was associated with decreased trait value, equivalent to the offspring of first cousins being 1.2 cm shorter and having 10 months less education. Similar effect sizes were found across four continental groups and populations with different degrees of genome-wide homozygosity, providing convincing evidence for the first time that homozygosity, rather than confounding, directly contributes to phenotypic variance. Contrary to earlier reports in substantially smaller samples5,6, no evidence was seen of an influence of genome-wide homozygosity on blood pressure and low density lipoprotein (LDL) cholesterol, or ten other cardio-metabolic traits. Since directional dominance is predicted for traits under directional evolutionary selection7, this study provides evidence that increased stature and cognitive function have been positively selected in human evolution, whereas many important risk factors for late-onset complex diseases may not have been.
Corneal thickness (CT) maps of the central (2-mm diameter), para-central (2 to 5-mm diameter), peripheral (5 to 6-mm diameter), and minimum (5-mm diameter) cornea were measured in normal Chinese school children aged 7 to 15 years old using Fourier-domain optical coherence tomography. Multiple regression analyses were performed to explore the effect of associated factors [age, gender, refraction, axial length and corneal curvature radius (CCR)] on CT and the relationship between central corneal thickness (CCT) and intraocular pressure (IOP). A total of 1228 eyes from 614 children were analyzed. The average CCT was 532.96 ± 28.33 μm for right eyes and 532.70 ±28.45 μm for left eyes. With a 10 μm increase in CCT, the IOP was elevated by 0.37 mm Hg, as measured by noncontact tonometry. The CT increased gradually from the center to the periphery. The superior and superior nasal regions had the thickest CTs, while the thinnest points were primarily located in the inferior temporal cornea. The CCT was associated with CCR (p = 0.008) but not with gender (p = 0.075), age (p = 0.286), axial length (p = 0.405), or refraction (p = 0.985). In the para-central region and the peripheral cornea, increased CT was associated with younger age, male gender, and a flatter cornea.
Sesame is an important high-quality oil seed crop. The sesame genome was de novo sequenced and assembled in 2014 (version 1.0); however, the number of anchored pseudomolecules was higher than the chromosome number (2n = 2x = 26) due to the lack of a high-density genetic map with 13 linkage groups.
We resequenced a permanent population consisting of 430 recombinant inbred lines and constructed a genetic map to improve the sesame genome assembly. We successfully anchored 327 scaffolds onto 13 pseudomolecules. The new genome assembly (version 2.0) included 97.5 % of the scaffolds greater than 150 kb in size present in assembly version 1.0 and increased the total pseudomolecule length from 233.7 to 258.4 Mb with 94.3 % of the genome assembled and 97.2 % of the predicted gene models anchored. Based on the new genome assembly, a bin map including 1,522 bins spanning 1090.99 cM was generated and used to identified 41 quantitative trait loci (QTLs) for sesame plant height and 9 for seed coat color. The plant height-related QTLs explained 3–24 % the phenotypic variation (mean value, 8 %), and 29 of them were detected in at least two field trials. Two major loci (qPH-8.2 and qPH-3.3) that contributed 23 and 18 % of the plant height were located in 350 and 928-kb spaces on Chr8 and Chr3, respectively. qPH-3.3, is predicted to be responsible for the semi-dwarf sesame plant phenotype and contains 102 candidate genes. This is the first report of a sesame semi-dwarf locus and provides an interesting opportunity for a plant architecture study of the sesame. For the sesame seed coat color, the QTLs of the color spaces L*, a*, and b* were detected with contribution rates of 3–46 %. qSCb-4.1 contributed approximately 39 % of the b* value and was located on Chr4 in a 199.9-kb space. A list of 32 candidate genes for the locus, including a predicted black seed coat-related gene, was determined by screening the newly anchored genome.
This study offers a high-density genetic map and an improved assembly of the sesame genome. The number of linkage groups and pseudomolecules in this assembly equals the number of sesame chromosomes for the first time. The map and updated genome assembly are expected to serve as a platform for future comparative genomics and genetic studies.
Electronic supplementary material
The online version of this article (doi:10.1186/s12864-015-2316-4) contains supplementary material, which is available to authorized users.
This study aimed to evaluate and compare the utility values associated with diabetic retinopathy (DR) in a sample of Chinese patients and ophthalmologists.
Utility values were evaluated by both the time trade-off (TTO) and rating scale (RS) methods for 109 eligible patients with DR and 2 experienced ophthalmologists. Patients were stratified by Snellen best-corrected visual acuity (BCVA) in the better-seeing eye. The correlations between the utility values and general vision-related health status measures were analyzed. These utility values were compared with data from two other studies.
The mean utility values elicited from the patients themselves with the TTO (0.81; SD 0.10) and RS (0.81; SD 0.11) methods were both statistically lower than the mean utility values assessed by ophthalmologists. Significant predictors of patients’ TTO and RS utility values were both LogMAR BCVA in the affected eye and average weighted LogMAR BCVA. DR grade and duration of visual dysfunction were also variables that significantly predicted patients’ TTO utility values. For ophthalmologists, patients’ LogMAR BCVA in the affected eye and in the better eye were the variables that significantly predicted both the TTO and RS utility values. Patients’ education level was also a variable that significantly predicted RS utility values. Moreover, both diabetic macular edema and employment status were significant predictors of TTO and RS utility values, whether from patients or ophthalmologists. There was no difference in mean TTO utility values compared to our American and Canadian patients.
DR caused a substantial decrease in Chinese patients’ utility values, and ophthalmologists substantially underestimated its effect on patient quality of life.
Oilseed crops are used to produce vegetable oil. Sesame (Sesamum indicum), an oilseed crop grown worldwide, has high oil content and a small diploid genome, but the genetic basis of oil production and quality is unclear. Here we sequence 705 diverse sesame varieties to construct a haplotype map of the sesame genome and de novo assemble two representative varieties to identify sequence variations. We investigate 56 agronomic traits in four environments and identify 549 associated loci. Examination of the major loci identifies 46 candidate causative genes, including genes related to oil content, fatty acid biosynthesis and yield. Several of the candidate genes for oil content encode enzymes involved in oil metabolism. Two major genes associated with lignification and black pigmentation in the seed coat are also associated with large variation in oil content. These findings may inform breeding and improvement strategies for a broad range of oilseed crops.
Sesame is a valuable oilseed crop with a small diploid genome and high seed-oil content making it an attractive model for genetic studies. Here, Wei et al. sequence more than 705 sesame varieties and perform a genome-wide association study to identify genes associated with important agronomic traits.
Recent studies on AD/MCI diagnosis have shown that the tasks of identifying brain disease and predicting clinical scores are highly related to each other. Furthermore, it has been shown that feature selection with a manifold learning or a sparse model can handle the problems of high feature dimensionality and small sample size. However, the tasks of clinical score regression and clinical label classification were often conducted separately in the previous studies. Regarding the feature selection, to our best knowledge, most of the previous work considered a loss function defined as an element-wise difference between the target values and the predicted ones. In this paper, we consider the problems of joint regression and classification for AD/MCI diagnosis and propose a novel matrix-similarity based loss function that uses high-level information inherent in the target response matrix and imposes the information to be preserved in the predicted response matrix. The newly devised loss function is combined with a group lasso method for joint feature selection across tasks, i.e., predictions of clinical scores and a class label. In order to validate the effectiveness of the proposed method, we conducted experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, and showed that the newly devised loss function helped enhance the performances of both clinical score prediction and disease status identification, outperforming the state-of-the-art methods.
Alzheimer’s Disease (AD); feature selection; joint sparse learning; manifold learning; Mild Cognitive Impairment (MCI) conversion
We performed a meta-analysis to clarify the relationship between long chain n-3 polyunsaturated fatty acid (PUFA) intake and stroke risk. Relevant studies were identified by searching online databases through May 2015. Log relative risks (RRs) of the highest versus the lowest for cohort studies were weighed by the inverse variance method to obtain pooled RRs. Fourteen prospective cohort studies including 514,483 individuals and 9,065 strokes were included. The pooled RR of overall stroke risk for long chain n-3 PUFA intake was 0.87 [95% confidence interval (CI), 0.79–0.95]. Stratification analysis showed that higher long chain n-3 PUFAs intake was associated with reduced fatal stroke risk (RR = 0.84; 95% CI, 0.73–0.97), reduced stroke risk for BMI < 24 (RR = 0.86; 95% CI, 0.75–0.98) and reduced stroke risk for females (RR = 0.81; 95% CI, 0.71–0.92), but was not associated with stroke risk for either BMI ≥ 24 or men. This meta-analysis reveals that higher long chain n-3 PUFA intake is inversely associated with risk of stroke morbidity and mortality with BMI and sex as key factors influencing this risk. Individuals should be encouraged to manage their body weight while increasing their intake of long chain n-3 PUFAs.
Recent studies on Alzheimer's Disease (AD) or its prodromal stage, Mild Cognitive Impairment (MCI), diagnosis presented that the tasks of identifying brain disease status and predicting clinical scores based on neuroimaging features were highly related to each other. However, these tasks were often conducted independently in the previous studies. Regarding the feature selection, to our best knowledge, most of the previous work considered a loss function defined as an element-wise difference between the target values and the predicted ones. In this paper, we consider the problems of joint regression and classification for AD/MCI diagnosis and propose a novel matrix-similarity based loss function that uses high-level information inherent in the target response matrix and imposes the information to be preserved in the predicted response matrix. The newly devised loss function is combined with a group lasso method for joint feature selection across tasks, i.e., clinical scores prediction and disease status identification. We conducted experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and showed that the newly devised loss function was effective to enhance the performances of both clinical score prediction and disease status identification, outperforming the state-of-the-art methods.
High blood pressure is the principal risk factor for stroke, heart failure and kidney failure in the young population in Africa. Control of hypertension is associated with a larger reduction in morbidity and mortality in younger populations compared with the elderly; however, blood pressure control efforts in the young are hampered by scarcity of data on prevalence and factors influencing awareness, treatment and control of hypertension. We aimed to describe the prevalence of prehypertension and hypertension among young adults in a peri-urban district of Uganda and the factors associated with occurrence of hypertension in this population.
This cross-sectional study was conducted between August, 2012 and May 2013 in Wakiso district, a suburban district that that encircles Kampala, Uganda’s capital city. We collected data on socio-demographic characteristics and hypertension status using a modified STEPs questionnaire from 3685 subjects aged 18–40 years selected by multistage cluster sampling. Blood pressure and anthropometric measurements were performed using standardized protocols. Fasting blood sugar and HIV status were determined using a venous blood sample. Association between hypertension status and various biosocial factors was assessed using logistic regression.
The overall prevalence of hypertension was 15 % (95 % CI 14.2 – 19.6) and 40 % were pre-hypertensive. Among the 553 hypertensive participants, 76 (13.7 %) were aware of their diagnosis and all these participants had initiated therapy with target blood pressure control attained in 20 % of treated subjects. Hypertension was significantly associated with the older age-group, male sex and obesity. There was a significantly lower prevalence of hypertension among participants with HIV OR 0.6 (95 % CI 0.4–0.8, P = 0.007).
There is a high prevalence of high blood pressure in this young periurban population of Uganda with sub-optimal diagnosis and control. There is previously undocumented high rate of treatment, a unique finding that may be exploited to drive efforts to control hypertension. Specific programs for early diagnosis and treatment of hypertension among the young should be developed to improve control of hypertension. The relationship between HIV infection and blood pressure requires further clarification by longitudinal studies.
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ZD6474, a small molecule VEGFR and EGFR tyrosine kinase inhibitor, has been considered as a promising tumor-targeted drug in various malignancies. EGFR and cyclooxygenase-2 (COX-2) were found overexpressed in osteosarcoma in previous reports, so here we tried to explore the anti-osteosarcoma effect of ZD6474 alone or combination with celecoxib, a COX-2 inhibitor. The data demonstrated that ZD6474 inhibited the growth of osteosarcoma cells, and promoted G1-phase cell cycle arrest and apoptosis by inhibiting the activity of EGFR tyrosine kinase, and consequently suppressing its downstream PI3k/Akt and MAPK/ERK pathway. Additionally, daily administration of ZD6474 produced a dose-dependent inhibition of tumor growth in nude mice. Celecoxib also significantly inhibited the growth of osteosarcoma cells in dose-dependent manner, while combination of ZD6474 and celecoxib displayed a synergistic or additive antitumor effect on osteosarcoma in vitro and in vivo. The possible molecular mechanisms to address the synergism are likely that ZD6474 induces the down-regulation of COX-2 expression through inhibiting ERK phosphorylation, while celecoxib promotes ZD6474-directed inhibition of ERK phosphorylation. In conclusion, ZD6474 exerts direct anti-proliferative effects on osteosarcoma cells, and the synergistic antitumor effect of the combination of ZD6474 with celecoxib may indicate a new strategy of the combinative treatment of human osteosarcoma.
ZD6474; celecoxib; osteosarcoma; EGFR; cyclooxygenase-2
Gallbladder carcinoma is an aggressive malignancy with high mortality mainly due to the limited potential for curative resection and its resistance to chemotherapeutic agents. Here, we show that the histone deacetylase inhibitors (HDACIs) trichostatin-A (TSA) and suberoylanilide hydroxamic acid (SAHA) reduce the proliferation and induce apoptosis of gallbladder carcinoma cells by suppressing the AKT/mammalian target of rapamycin (mTOR) signaling. Gallbladder carcinoma SGC-996 cells were treated with different concentrations of TSA and SAHA for different lengths of time. Cell proliferation and morphology were assessed with MTT assay and microscopy, respectively. Cell cycle distribution and cell apoptosis were analyzed with flow cytometry. Western blotting was used to detect the proteins related to apoptosis, cell cycle, and the AKT/mTOR signaling pathway. Our data showed that TSA and SAHA reduced SGC-996 cell viability and arrested cell cycle at the G1 phase in a dose- and time-dependent manner. TSA and SAHA promoted apoptosis of SGC-996 cells, down-regulated the expression of cyclin D1, c-Myc and Bmi1, and decreased the phosphorylation of AKT, mTOR p70S6K1, S6 and 4E-BP1. Additionally, the mTOR inhibitor rapamycin further reduced the cell viability of TSA- and SAHA-treated SGC-996 cells and the phosphorylation of mTOR, whereas the mTOR activator 1,2-dioctanoyl-sn-glycero-3-phosphate (C8-PA) exerted the opposite influence. Our results demonstrate that histone deacetylase inhibitors (HDACIs) suppress the proliferation of gallbladder carcinoma cell via inhibition of AKT/mTOR signaling. These findings offer a mechanistic rationale for the application of HDACIs in gallbladder carcinoma treatment.
Feature selection has been commonly regarded as an effective method to lessen the problem of high dimension and low sample size in medical image analysis. In this paper, we propose a novel multimodality canonical feature selection method. Unlike the conventional sparse Multi-Task Learning (MTL) based feature selection method that mostly considered only the relationship between target response variables, we further consider the correlations between features of different modalities by projecting them into a canonical space determined by canonical correlation analysis. We call the projections as canonical representations. By setting the canonical representations as regressors in a sparse least square regression framework and by further penalizing the objective function with a new canonical regularizer on the weight coefficient matrix, we formulate a multi-modality canonical feature selection method. With the help of the canonical information of canonical representations and also a canonical regularizer, the proposed method selects canonical-cross-modality features that are useful for the tasks of clinical scores regression and multi-class disease identification. In our experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, we combine Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) images to jointly predict clinical scores of Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog) and Mini-Mental State Examination (MMSE) and also identify multiclass disease status for Alzheimer’s disease diagnosis.
The precise molecular etiology of obstructive sleep apnea (OSA) is unknown; however recent research indicates that several interconnected aberrant pathways and molecular abnormalities are contributors to OSA. Identifying the genes and pathways associated with OSA can help to expand our understanding of the risk factors for the disease as well as provide new avenues for potential treatment. Towards these goals, we have integrated relevant high dimensional data from various sources, such as genome-wide expression data (microarray), protein-protein interaction (PPI) data and results from genome-wide association studies (GWAS) in order to define sub-network elements that connect some of the known pathways related to the disease as well as define novel regulatory modules related to OSA. Two distinct approaches are applied to identify sub-networks significantly associated with OSA. In the first case we used a biased approach based on sixty genes/proteins with known associations with sleep disorders and/or metabolic disease to seed a search using commercial software to discover networks associated with disease followed by information theoretic (mutual information) scoring of the sub-networks. In the second case we used an unbiased approach and generated an interactome constructed from publicly available gene expression profiles and PPI databases, followed by scoring of the network with p-values from GWAS data derived from OSA patients to uncover sub-networks significant for the disease phenotype. A comparison of the approaches reveals a number of proteins that have been previously known to be associated with OSA or sleep. In addition, our results indicate a novel association of Phosphoinositide 3-kinase, the STAT family of proteins and its related pathways with OSA.
Genome-wide association studies (GWAS) have identified at least 133 ulcerative colitis (UC) associated loci. The role of genetic factors in clinical practice is not clearly defined. The relevance of genetic variants to disease pathogenesis is still uncertain because of not characterized gene-gene and gene-environment interactions. We examined the predictive value of combining the 133 UC risk loci with genetic interactions in an ongoing inflammatory bowel disease (IBD) GWAS. The Wellcome Trust Case-Control Consortium (WTCCC) IBD GWAS was used as a replication cohort. We applied logic regression (LR), a novel adaptive regression methodology, to search for high order interactions. Exploratory genotype correlations with UC sub-phenotypes (extent of disease, need of surgery, age of onset, extra-intestinal manifestations and primary sclerosing cholangitis (PSC)) were conducted. The combination of 133 UC loci yielded good UC risk predictability (area under the curve [AUC] of 0.86). A higher cumulative allele score predicted higher UC risk. Through LR, several lines of evidence for genetic interactions were identified and successfully replicated in the WTCCC cohort. The genetic interactions combined with the gene-smoking interaction significantly improved predictability in the model (AUC, from 0.86 to 0.89, P=3.26E-05). Explained UC variance increased from 37% to 42% after adding the interaction terms. A within case analysis found suggested genetic association with PSC. Our study demonstrates that the LR methodology allows the identification and replication of high order genetic interactions in UC GWAS datasets. UC risk can be predicted by a 133 loci and improved by adding gene-gene and gene-environment interactions.
ulcerative colitis; genetic polymorphism; IBD - genetics; IBD – clinical; primary sclerosing cholangitis