5 resultados para Van Nostrand, David, 1811-1888.


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The CIAO Study ("Complicated Intra-Abdominal infection Observational" Study) is a multicenter investigation performed in 68 medical institutions throughout Europe over the course of a 6-month observational period (January-June 2012).Patients with either community-acquired or healthcare-associated complicated intra-abdominal infections (IAIs) were included in the study.2,152 patients with a mean age of 53.8 years (range: 4-98 years) were enrolled in the study. 46.3% of the patients were women and 53.7% were men. Intraperitoneal specimens were collected from 62.2% of the enrolled patients, and from these samples, a variety of microorganisms were collectively identified.The overall mortality rate was 7.5% (163/2.152).According to multivariate analysis of the compiled data, several criteria were found to be independent variables predictive of patient mortality, including patient age, the presence of an intestinal non-appendicular source of infection (colonic non-diverticular perforation, complicated diverticulitis, small bowel perforation), a delayed initial intervention (a delay exceeding 24 hours), sepsis and septic shock in the immediate post-operative period, and ICU admission.Given the sweeping geographical distribution of the participating medical centers, the CIAO Study gives an accurate description of the epidemiological, clinical, microbiological, and treatment profiles of complicated intra-abdominal infections (IAIs) throughout Europe.

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The mTOR (mammalian target of rapamycin) signal transduction pathway integrates various signals, regulating ribosome biogenesis and protein synthesis as a function of available energy and amino acids, and assuring an appropriate coupling of cellular proliferation with increases in cell size. In addition, recent evidence has pointed to an interplay between the mTOR and p53 pathways. We investigated the genetic variability of 67 key genes in the mTOR pathway and in genes of the p53 pathway which interact with mTOR. We tested the association of 1,084 tagging SNPs with prostate cancer risk in a study of 815 prostate cancer cases and 1,266 controls nested within the European Prospective Investigation into Cancer and Nutrition (EPIC). We chose the SNPs (n = 11) with the strongest association with risk (p<0.01) and sought to replicate their association in an additional series of 838 prostate cancer cases and 943 controls from EPIC. In the joint analysis of first and second phase two SNPs of the PRKCI gene showed an association with risk of prostate cancer (ORallele = 0.85, 95% CI 0.78–0.94, p = 1.3×10−3 for rs546950 and ORallele = 0.84, 95% CI 0.76–0.93, p = 5.6×10−4 for rs4955720). We confirmed this in a meta-analysis using as replication set the data from the second phase of our study jointly with the first phase of the Cancer Genetic Markers of Susceptibility (CGEMS) project. In conclusion, we found an association with prostate cancer risk for two SNPs belonging to PRKCI, a gene which is frequently overexpressed in various neoplasms, including prostate cancer.

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INTRODUCTION Associations of hormone-receptor positive breast cancer with excess adiposity are reasonably well characterized; however, uncertainty remains regarding the association of body mass index (BMI) with hormone-receptor negative malignancies, and possible interactions by hormone replacement therapy (HRT) use. METHODS Within the European EPIC cohort, Cox proportional hazards models were used to describe the relationship of BMI, waist and hip circumferences with risk of estrogen-receptor (ER) negative and progesterone-receptor (PR) negative (n = 1,021) and ER+PR+ (n = 3,586) breast tumors within five-year age bands. Among postmenopausal women, the joint effects of BMI and HRT use were analyzed. RESULTS For risk of ER-PR- tumors, there was no association of BMI across the age bands. However, when analyses were restricted to postmenopausal HRT never users, a positive risk association with BMI (third versus first tertile HR = 1.47 (1.01 to 2.15)) was observed. BMI was inversely associated with ER+PR+ tumors among women aged ≤49 years (per 5 kg/m2 increase, HR = 0.79 (95%CI 0.68 to 0.91)), and positively associated with risk among women ≥65 years (HR = 1.25 (1.16 to 1.34)). Adjusting for BMI, waist and hip circumferences showed no further associations with risks of breast cancer subtypes. Current use of HRT was significantly associated with an increased risk of receptor-negative (HRT current use compared to HRT never use HR: 1.30 (1.05 to 1.62)) and positive tumors (HR: 1.74 (1.56 to 1.95)), although this risk increase was weaker for ER-PR- disease (Phet = 0.035). The association of HRT was significantly stronger in the leaner women (BMI ≤22.5 kg/m2) than for more overweight women (BMI ≥25.9 kg/m2) for, both, ER-PR- (HR: 1.74 (1.15 to 2.63)) and ER+PR+ (HR: 2.33 (1.84 to 2.92)) breast cancer and was not restricted to any particular HRT regime. CONCLUSIONS An elevated BMI may be positively associated with risk of ER-PR- tumors among postmenopausal women who never used HRT. Furthermore, postmenopausal HRT users were at an increased risk of ER-PR- as well as ER+PR+ tumors, especially among leaner women. For hormone-receptor positive tumors, but not for hormone-receptor negative tumors, our study confirms an inverse association of risk with BMI among young women of premenopausal age. Our data provide evidence for a possible role of sex hormones in the etiology of hormone-receptor negative tumors.

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Systemic sclerosis (SSc) is a fibrotic autoimmune disease in which the genetic component plays an important role. One of the strongest SSc association signals outside the human leukocyte antigen (HLA) region corresponds to interferon (IFN) regulatory factor 5 (IRF5), a major regulator of the type I IFN pathway. In this study we aimed to evaluate whether three different haplotypic blocks within this locus, which have been shown to alter the protein function influencing systemic lupus erythematosus (SLE) susceptibility, are involved in SSc susceptibility and clinical phenotypes. For that purpose, we genotyped one representative single-nucleotide polymorphism (SNP) of each block (rs10488631, rs2004640, and rs4728142) in a total of 3,361 SSc patients and 4,012 unaffected controls of Caucasian origin from Spain, Germany, The Netherlands, Italy and United Kingdom. A meta-analysis of the allele frequencies was performed to analyse the overall effect of these IRF5 genetic variants on SSc. Allelic combination and dependency tests were also carried out. The three SNPs showed strong associations with the global disease (rs4728142: P  = 1.34×10(-8), OR  = 1.22, CI 95%  = 1.14-1.30; rs2004640: P  = 4.60×10(-7), OR  = 0.84, CI 95%  = 0.78-0.90; rs10488631: P  = 7.53×10(-20), OR  = 1.63, CI 95%  = 1.47-1.81). However, the association of rs2004640 with SSc was not independent of rs4728142 (conditioned P  = 0.598). The haplotype containing the risk alleles (rs4728142*A-rs2004640*T-rs10488631*C: P  = 9.04×10(-22), OR  = 1.75, CI 95%  = 1.56-1.97) better explained the observed association (likelihood P-value  = 1.48×10(-4)), suggesting an additive effect of the three haplotypic blocks. No statistical significance was observed in the comparisons amongst SSc patients with and without the main clinical characteristics. Our data clearly indicate that the SLE risk haplotype also influences SSc predisposition, and that this association is not sub-phenotype-specific.

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In epidemiologic studies, measurement error in dietary variables often attenuates association between dietary intake and disease occurrence. To adjust for the attenuation caused by error in dietary intake, regression calibration is commonly used. To apply regression calibration, unbiased reference measurements are required. Short-term reference measurements for foods that are not consumed daily contain excess zeroes that pose challenges in the calibration model. We adapted two-part regression calibration model, initially developed for multiple replicates of reference measurements per individual to a single-replicate setting. We showed how to handle excess zero reference measurements by two-step modeling approach, how to explore heteroscedasticity in the consumed amount with variance-mean graph, how to explore nonlinearity with the generalized additive modeling (GAM) and the empirical logit approaches, and how to select covariates in the calibration model. The performance of two-part calibration model was compared with the one-part counterpart. We used vegetable intake and mortality data from European Prospective Investigation on Cancer and Nutrition (EPIC) study. In the EPIC, reference measurements were taken with 24-hour recalls. For each of the three vegetable subgroups assessed separately, correcting for error with an appropriately specified two-part calibration model resulted in about three fold increase in the strength of association with all-cause mortality, as measured by the log hazard ratio. Further found is that the standard way of including covariates in the calibration model can lead to over fitting the two-part calibration model. Moreover, the extent of adjusting for error is influenced by the number and forms of covariates in the calibration model. For episodically consumed foods, we advise researchers to pay special attention to response distribution, nonlinearity, and covariate inclusion in specifying the calibration model.