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Genetic factors can influence and predict patient outcomes. The association of interactions of germline SNPs with patient outcomes is an understudied area of prognostic research. In this study, we applied the first genome-wide SNP-SNP interaction analysis in relation to colorectal cancer outcomes. Our objective was to explore interacting SNP loci at the genome-wide level that predict the risk of local or distant recurrence (RMFS) in a cohort of stage I-III colorectal cancer patients from the Canadian province of Newfoundland and Labrador. The patient cohort consisted of 430 unrelated Caucasian patients. Genetic and medical data was collected previously and the genetic data consisted of a total of 384,415 genotyped SNPs. The PLINK epistasis function was utilized to examine pairwise SNP interactions. Select interactions were assessed by multivariable Cox-regression models, adjusting for established clinical covariates. Genomic regions identified were explored for additional interactions. Published databases were utilized to retrieve biological information about the loci identified. After Bonferroni correction for multiple testing, no interaction remained significant. We present the top 20 interactions. The interaction p-values ranged from p = 1.37E-8 to p = 2.14E-9 in this set. Interactions were also tested by multivariable Cox regression models including established clinical covariates. Many of the SNPs were intronic and some of them were functional (e.g., expression quantitative expression loci). Analysis of the other SNPs in the same genomic regions as the interacting SNPs led to the identification of three additional interaction models. We present the results of the first genome-wide SNP-SNP interaction analysis in colorectal cancer outcomes. While no SNP-SNP interaction remained significant after correction for multiple testing, our methodology emphasizes the additional knowledge that can be obtained using interaction analyses while studying prognostic markers.

Background Colorectal cancer is the third most common cancer in the world. In this study, we assessed the long-term survival characteristics and prognostic associations and potential time-varying effects of clinico-demographic variables and two molecular markers (microsatellite instability (MSI) and BRAF Val600Glu mutation) in a population-based patient cohort followed up to ~ 19 years. Methods The patient cohort included 738 incident cases diagnosed between 1999 and 2003. Cox models were used to analyze the association between the variables and a set of survival outcome measures (overall survival (OS), disease-specific survival (DSS), recurrence-free survival (RFS), metastasis-free survival (MFS), recurrence/metastasis-free survival (RMFS), and event-free survival (EFS)). Cox proportional hazard (PH) assumption was tested for all variables, and Cox models with time-varying effects were used if any departure from the PH assumption was detected. Results During the follow-up, ~ 61% patients died from any cause, ~ 26% died from colorectal cancer, and ~ 10% and ~ 20% experienced recurrences and distant metastases, respectively. Stage IV disease and post-diagnostic recurrence or metastasis were strongly linked to risk of death from colorectal cancer. If a patient had survived the first 6 years without any disease-related event (i.e., recurrence, metastasis, or death from colorectal cancer), their risks became very minimal after this time period. Distinct sets of markers were associated with different outcome measures. In some cases, the effects by variables were constant throughout the follow-up. For example, MSI-high tumor phenotype and older age at diagnosis predicted longer MFS times consistently over the follow-up. However, in some other cases, the effects of the variables varied with time. For example, adjuvant radiotherapy treatment was associated with increased risk of metastasis in patients who received this treatment after 5.5 years post-diagnosis, but not before that. Conclusions This study describes the long-term survival characteristics of a prospective cohort of colorectal cancer patients, relationships between baseline variables and a detailed set of patient outcomes over a long time, and time-varying effects of a group of variables. The results presented advance our understanding of the long-term prognostic characteristics in colorectal cancer and are expected to inspire future studies and clinical care strategies.

Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.

In genetic association studies of rare variants, low statistical power and potential violations of established estimator properties are among the main challenges of association tests. Multi-marker tests (MMTs) have been proposed to target these challenges, but any comparison with single-marker tests (SMTs) has to consider that their aim is to identify causal genomic regions instead of variants. Valid power comparisons have been performed for the analysis of binary traits indicating that MMTs have higher power, but there is a lack of conclusive studies for quantitative traits. The aim of our study was therefore to fairly compare SMTs and MMTs in their empirical power to identify the same causal loci associated with a quantitative trait. The results of extensive simulation studies indicate that previous results for binary traits cannot be generalized. First, we show that for the analysis of quantitative traits, conventional estimation methods and test statistics of single-marker approaches have valid properties yielding association tests with valid type I error, even when investigating singletons or doubletons. Furthermore, SMTs lead to more powerful association tests for identifying causal genes than MMTs when the effect sizes of causal variants are large, and less powerful tests when causal variants have small effect sizes. For moderate effect sizes, whether SMTs or MMTs have higher power depends on the sample size and percentage of causal SNVs. For a more complete picture, we also compare the power in studies of quantitative and binary traits, and the power to identify causal genes with the power to identify causal rare variants. In a genetic association analysis of systolic blood pressure in the Genetic Analysis Workshop 19 data, SMTs yielded smaller p-values compared to MMTs for most of the investigated blood pressure genes, and were least influenced by the definition of gene regions.

We aimed to examine the associations of a genome‐wide set of single nucleotide polymorphisms (SNPs) and 254 copy number variations (CNVs) and/or insertion/deletions (INDELs) with clinical outcomes in colorectal cancer patients (n = 505). We also aimed to investigate whether their associations changed (e.g., appeared, diminished) over time. Multivariable Cox proportional hazards and piece‐wise Cox regression models were used to examine the associations. The Cancer Genome Atlas (TCGA) datasets were used for replication purposes and to examine the gene expression differences between tumor and nontumor tissue samples. A common SNP (WBP11‐rs7314075) was associated with disease‐specific survival with P‐value of 3.2 × 10−8. Association of this region with disease‐specific survival was also detected in the TCGA patient cohort. Two expression quantitative trait loci (eQTLs) were identified in this locus that were implicated in the regulation of ERP27 expression. Interestingly, expression levels of ERP27 and WBP11 were significantly different between colorectal tumors and nontumor tissues. Three SNPs predicted the risk of recurrent disease only after 5 years postdiagnosis. Overall, our study identified novel variants, one of which also showed an association in the TCGA dataset, but no CNVs/INDELs, that associated with outcomes in colorectal cancer. Three SNPs were candidate predictors of long‐term recurrence/metastasis risk. Stratifying patients with colorectal cancer (CRC) into high and low outcome risk groups is important for informing treatment and follow‐up strategies. Here, we examined the associations of a genome‐wide set of single nucleotide polymorphisms (SNPs) and a set of copy number variations and indels with clinical outcomes in patients with CRC. We showed that CRC patients can be distinguished for their risk of death from this disease based on their genotypes of a SNP in the WBP11 gene.

A new class of BaLiZn 3 (BO 3 ) 3 :RE (RE = Sm 3+ , Tb 3+ , Dy 3+ , and Pb 2+ ) phosphors were synthesized with a solid- state reaction method. The minor concentrations of various rare earth (Tb 3+ , Dy 3+ , and Sm 3+ ) ions and transition metal (Pb 2+ ) ions activated in the BaLiZn 3 (BO 3 ) host matrix were characterized by using X-ray diffraction (XRD), scanning electron microscopy (SEM), and photoluminescence spectroscopy. The XRD results of BaLiZn 3 (BO 3 )3:RE (RE = Sm 3+ , Tb 3+ , Dy 3+ , and Pb 2+ ) phosphors confirmed that all the samples have a monoclinic phase. SEM studies revealed that the morphology of BaLiZn 3 (BO 3 ) 3 :RE (RE = Sm 3+ , Tb 3+ , Dy 3+ , and Pb 2+ ) phosphors was irregular. The photoluminescence emission and excitation spectra show that these phosphors can be effectively excited by near-ultraviolet light-emitting diodes (n-UV), and they all exhibit an efficient orange-red (Sm 3+ , 4 G 5/2 → 6 H 7/2 ), green (Tb 3+ , 5 D 4 → 7 F 5 ), yellow (Dy 3+ , 4 F 9/2 → 6 H 13/2 ), and blue (Pb 2+ , 3 P 1 → 1 S 0 ) emission. All of the above results confirmed that the obtained phosphors could be a potential candidate for n-UV-excited WLEDs.

A bstract Background Differentiating between cancer patients who will experience metastasis within a short time and who will be long-term survivors without metastasis is a critical aim in healthcare. The microsatellite instability (MSI)-high tumor phenotype is such a differentiator in colorectal cancer, as patients with these tumors are unlikely to experience metastasis. Our aim in this study was to determine if germline genetic variations could further differentiate colorectal cancer patients based on the long-term risk and timing of metastasis. Methods The patient cohort consisted of 379 stage I-III Caucasian colorectal cancer patients with microsatellite stable or MSI-low tumors. We performed univariable analysis on 810,622 common single nucleotide polymorphisms (SNPs) under different genetic models. Depending on the long-term metastasis-free survival probability estimates, we applied a mixture cure model, Cox proportional hazards regression model, or log-rank test. For SNPs reaching Bonferroni-corrected significance ( p  < 6.2 × 10 − 8 ) having valid genetic models, multivariable analysis adjusting for significant baseline characteristics was conducted. Results After adjusting for significant baseline characteristics, specific genotypes of ten polymorphisms were significantly associated with time-to-metastasis. These polymorphisms are three intergenic SNPs, rs5749032 ( p  = 1.28 × 10 − 10 ), rs2327990 ( p  = 9.59 × 10 − 10 ), rs1145724 ( p  = 3 × 10 − 8 ), and seven SNPs within the non-coding sequences of three genes: FHIT ( p  = 2.59 × 10 − 9 ), EPHB1 ( p  = 8.23 × 10 − 9 ), and MIR7515 ( p  = 4.87 × 10 − 8 ). Conclusions Our results suggest novel associations of specific genotypes of SNPs with early metastasis in Caucasian colorectal cancer patients. These associations, once replicated in other patient cohorts, could assist in the development of personalized treatment strategies for colorectal cancer patients.

In this study, the effects of the structural geometries of the channels used for cooling blades in the heated regions of the gas turbine exposed to high temperature were investigated. It is aimed to cool the gas turbine blade using 10 different types of ribbed channels by simulation method. Roof, inverted roof, slope and wedge ribs with standard and stepped arrangements developed for improving thermal performance in a rectangular cooling duct with a 4:1 ratio aspect were studied. Square type rib, the basic geometry is designed to make thermal comparisons. Details of turbulent flow and numerical calculations were made using of the standard k-ε turbulence model by means of the Computational Fluid Dynamics (CFD) method. The heat transfer in ribbed walls was examined in four different Reynolds numbers, 10000, 20000, 40000 and 80000 according to the channel input cross section. As a result of the calculations, the temperature changes of the turbine blade depending on Re number, the heat transfer improvement occurring in the internal channels inside the blade and the overall thermal performance were compared. The different types of rib evaluated in the study were compared with the standard-square rib; higher Nu number was obtained in stepped-wedge rib, stepped-roof rib, stepped-slope rib and stepped-inverted roof rib, respectively. It has been observed that a stepped-wedge rib can improve overall thermal performance and is promising for internal turbine blade cooling applications. The best operating range of all models was found to be between Re=40000 and Re=80000. The highest PEC results were obtained in the stepped-wedge rib model. This is 3.37% higher than the closest performing stepped-inverted roof rib model with 5.04 at Re=8000.

Metastasis is a major cause of mortality in cancer. Identifying prognostic factors that distinguish patients who will experience metastasis in the short-term and those that will be free of metastasis in the long-term is of particular interest in current medical research. The objective of this study was to examine if select genetic polymorphisms can differentiate colorectal cancer patients based on timing and long-term risk of metastasis. The patient cohort consisted of 402 stage I-III colorectal cancer patients with microsatellite instability (MSI)-low (MSI-L) or microsatellite stable (MSS) tumors. We applied multivariable mixture cure model, which is the proper model when there is a substantial group of patients who remain free of metastasis in the long-term, to 26 polymorphisms. Time-dependent receiver operator characteristic (ROC) curve analysis was performed to determine the change in discriminatory accuracy of the models when the significant SNPs were included. After adjusting for significant baseline characteristics, two polymorphisms were significantly associated with time-to-metastasis: TT and TC genotypes of the XRCC3 Thr241Met (p = 0.042) and the 3R/3R genotype of TYMS 5'-UTR variable number tandem repeat (VNTR) (p = 0.009) were associated with decreased time-to-metastasis. ROC curves showed that the discriminatory accuracy of the model is increased slightly when these polymorphisms were added to the significant baseline characteristics. Our results indicate XRCC3 Thr241Met and TYMS 5'-UTR VNTR polymorphisms are associated with time-to-metastasis, and may have potential biological roles in expediting the metastatic process. Once replicated, these associations could contribute to the development of precision medicine for colorectal cancer patients.

In many genetic association analyses, while the aim is to identify genetic variants associated with a given quantitative trait, budgetary constraints prevent genotyping all individuals in a cohort. Selection of individuals for genotyping according to their quantitative trait value can improve cost efficiency. We consider quantitative trait-dependent two-phase sampling designs. In the first phase, trait and inexpensive covariate values for all individuals in a cohort are obtained; in the second phase, genetic sequence data for a subset of individuals are obtained according to their trait values and possibly their inexpensive covariates. We consider the likelihood and pseudo-likelihood methods proposed to analyze response-biased samples, assess their performance under common, low-frequency, and rare variant analyses, compare their efficiencies and investigate efficient response-dependent sampling designs under each method. We also assess robustness of the estimation methods and sampling designs under misspecified models. The results show that extreme sampling is the most efficient design for common variant analysis, and that selecting a small sample from the middle stratum improves accuracy and precision in low-frequency and rare variant analyses. Likelihood methods under an extreme sampling design generally give the most accurate and precise estimates when the model is correctly specified. Both the estimated pseudo-likelihood and pseudo-conditional likelihood methods become more efficient under model misspecification.