Socioeconomic status and prostate cancer incidence and mortality rates among the diverse population of California

The racial/ethnic disparities in prostate cancer rates are well documented, with the highest incidence and mortality rates observed among African-Americans followed by non-Hispanic Whites, Hispanics, and Asian/Pacific Islanders. Whether socioeconomic status (SES) can account for these differences in risk has been investigated in previous studies, but with conflicting results. Furthermore, previous studies have focused primarily on the differences between African-Americans and non-Hispanic Whites, and little is known for Hispanics and Asian/Pacific Islanders. To further investigate the relationship between SES and prostate cancer among African-Americans, non-Hispanic Whites, Hispanics, and Asian/Pacific Islanders, we conducted a large population-based cross-sectional study of 98,484 incident prostate cancer cases and 8,997 prostate cancer deaths from California. Data were abstracted from the California Cancer Registry, a population-based surveillance, epidemiology, and end results (SEER) registry. Each prostate cancer case and death was assigned a multidimensional neighborhood-SES index using the 2000 US Census data. SES quintile-specific prostate cancer incidence and mortality rates and rate ratios were estimated using SEER*Stat for each race/ethnicity categorized into 10-year age groups. For prostate cancer incidence, we observed higher levels of SES to be significantly associated with increased risk of disease [SES Q1 vs. Q5: relative risk (RR) = 1.28; 95% confidence interval (CI): 1.25–1.30]. Among younger men (45–64 years), African-Americans had the highest incidence rates followed by non-Hispanic Whites, Hispanics, and Asian/Pacific Islanders for all SES levels. Yet, among older men (75–84 years) Hispanics, following African-Americans, displayed the second highest incidence rates of prostate cancer. For prostate cancer deaths, higher levels of SES were associated with lower mortality rates of prostate cancer deaths (SES Q1 vs. Q5: RR = 0.88; 95% CI: 0.92–0.94). African-Americans had a twofold to fivefold increased risk of prostate cancer deaths in comparison to non-Hispanic Whites across all levels of SES. Our findings suggest that SES alone cannot account for the greater burden of prostate cancer among African-American men. In addition, incidence and mortality rates of prostate cancer display different age and racial/ethnic patterns across gradients of SES

Interactions between the adducin 2 gene and antihypertensive drug therapies in determining blood pressure in people with hypertension

<p>Abstract</p> <p>Background</p> <p>As part of the NHLBI Family Blood Pressure Program, the Genetic Epidemiology Network of Arteriopathy (GENOA) recruited 575 sibships (n = 1583 individuals) from Rochester, MN who had at least two hypertensive siblings diagnosed before age 60. Linkage analysis identified a region on chromosome 2 that was investigated using 70 single nucleotide polymorphisms (SNPs) typed in 7 positional candidate genes, including adducin 2 (<it>ADD2</it>).</p> <p>Method</p> <p>To investigate whether blood pressure (BP) levels in these hypertensives (n = 1133) were influenced by gene-by-drug interactions, we used cross-validation statistical methods (i.e., estimating a model for predicting BP levels in one subgroup and testing it in a different subgroup). These methods greatly reduced the chance of false positive findings.</p> <p>Results</p> <p>Eight SNPs in <it>ADD2 </it>were significantly associated with systolic BP in untreated hypertensives (p-value < 0.05). Moreover, we also identified SNPs associated with gene-by-drug interactions on systolic BP in drug-treated hypertensives. The TT genotype at SNP rs1541582 was associated with an average systolic BP of 133 mmHg in the beta-blocker subgroup and 148 mmHg in the diuretic subgroup after adjusting for overall mean differences among drug classes.</p> <p>Conclusion</p> <p>Our findings suggest that hypertension candidate gene variation may influence BP responses to specific antihypertensive drug therapies and measurement of genetic variation may assist in identifying subgroups of hypertensive patients who will benefit most from particular antihypertensive drug therapies.</p

Assessment of the Red Cell Proteome of Young Patients with Unexplained Hemolytic Anemia by Two-Dimensional Differential In-Gel Electrophoresis (DIGE)

Erythrocyte cytosolic protein expression profiles of children with unexplained hemolytic anemia were compared with profiles of close relatives and controls by two-dimensional differential in-gel electrophoresis (2D-DIGE). The severity of anemia in the patients varied from compensated (i.e., no medical intervention required) to chronic transfusion dependence. Common characteristics of all patients included chronic elevation of reticulocyte count and a negative workup for anemia focusing on hemoglobinopathies, morphologic abnormalities that would suggest a membrane defect, immune-mediated red cell destruction, and evaluation of the most common red cell enzyme defects, glucose-6-phosphate dehydrogenase and pyruvate kinase deficiency. Based upon this initial workup and presentation during infancy or early childhood, four patients classified as hereditary nonspherocytic hemolytic anemia (HNSHA) of unknown etiology were selected for proteomic analysis. DIGE analysis of red cell cytosolic proteins clearly discriminated each anemic patient from both familial and unrelated controls, revealing both patient-specific and shared patterns of differential protein expression. Changes in expression pattern shared among the four patients were identified in several protein classes including chaperons, cytoskeletal and proteasome proteins. Elevated expression in patient samples of some proteins correlated with high reticulocyte count, likely identifying a subset of proteins that are normally lost during erythroid maturation, including proteins involved in mitochondrial metabolism and protein synthesis. Proteins identified with patient-specific decreased expression included components of the glutathione synthetic pathway, antioxidant pathways, and proteins involved in signal transduction and nucleotide metabolism. Among the more than 200 proteins identified in this study are 21 proteins not previously described as part of the erythrocyte proteome. These results demonstrate the feasibility of applying a global proteomic approach to aid characterization of red cells from patients with hereditary anemia of unknown cause, including the identification of differentially expressed proteins as potential candidates with a role in disease pathogenesis

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation

Design, construction and operation of the ProtoDUNE-SP Liquid Argon TPC

The ProtoDUNE-SP detector is a single-phase liquid argon time projection chamber (LArTPC) that was constructed and operated in the CERN North Area at the end of the H4 beamline. This detector is a prototype for the first far detector module of the Deep Underground Neutrino Experiment (DUNE), which will be constructed at the Sandford Underground Research Facility (SURF) in Lead, South Dakota, U.S.A. The ProtoDUNE-SP detector incorporates full-size components as designed for DUNE and has an active volume of 7 × 6 × 7.2 m3. The H4 beam delivers incident particles with well-measured momenta and high-purity particle identification. ProtoDUNE-SP's successful operation between 2018 and 2020 demonstrates the effectiveness of the single-phase far detector design. This paper describes the design, construction, assembly and operation of the detector components

Low exposure long-baseline neutrino oscillation sensitivity of the DUNE experiment

The Deep Underground Neutrino Experiment (DUNE) will produce world-leading neutrino oscillation measurements over the lifetime of the experiment. In this work, we explore DUNE's sensitivity to observe charge-parity violation (CPV) in the neutrino sector, and to resolve the mass ordering, for exposures of up to 100 kiloton-megawatt-years (kt-MW-yr). The analysis includes detailed uncertainties on the flux prediction, the neutrino interaction model, and detector effects. We demonstrate that DUNE will be able to unambiguously resolve the neutrino mass ordering at a 3σ (5σ) level, with a 66 (100) kt-MW-yr far detector exposure, and has the ability to make strong statements at significantly shorter exposures depending on the true value of other oscillation parameters. We also show that DUNE has the potential to make a robust measurement of CPV at a 3σ level with a 100 kt-MW-yr exposure for the maximally CP-violating values \delta_{\rm CP}} = \pm\pi/2. Additionally, the dependence of DUNE's sensitivity on the exposure taken in neutrino-enhanced and antineutrino-enhanced running is discussed. An equal fraction of exposure taken in each beam mode is found to be close to optimal when considered over the entire space of interest

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10.1038/nature06315Nature4507167E2-E4NATU