35 resultados para Avvakum Petrovich, Protopope, 1620 or 1-1682


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BACKGROUND Little is known about the healthcare process for patients with prostate cancer, mainly because hospital-based data are not routinely published. The main objective of this study was to determine the clinical characteristics of prostate cancer patients, the, diagnostic process and the factors that might influence intervals from consultation to diagnosis and from diagnosis to treatment. METHODS We conducted a multicentre, cohort study in seven hospitals in Spain. Patients' characteristics and diagnostic and therapeutic variables were obtained from hospital records and patients' structured interviews from October 2010 to September 2011. We used a multilevel logistic regression model to examine the association between patient care intervals and various variables influencing these intervals (age, BMI, educational level, ECOG, first specialist consultation, tumour stage, PSA, Gleason score, and presence of symptoms) and calculated the odds ratio (OR) and the interquartile range (IQR). To estimate the random inter-hospital variability, we used the median odds ratio (MOR). RESULTS 470 patients with prostate cancer were included. Mean age was 67.8 (SD: 7.6) years and 75.4 % were physically active. Tumour size was classified as T1 in 41.0 % and as T2 in 40 % of patients, their median Gleason score was 6.0 (IQR:1.0), and 36.1 % had low risk cancer according to the D'Amico classification. The median interval between first consultation and diagnosis was 89 days (IQR:123.5) with no statistically significant variability between centres. Presence of symptoms was associated with a significantly longer interval between first consultation and diagnosis than no symptoms (OR:1.93, 95%CI 1.29-2.89). The median time between diagnosis and first treatment (therapeutic interval) was 75.0 days (IQR:78.0) and significant variability between centres was found (MOR:2.16, 95%CI 1.45-4.87). This interval was shorter in patients with a high PSA value (p = 0.012) and a high Gleason score (p = 0.026). CONCLUSIONS Most incident prostate cancer patients in Spain are diagnosed at an early stage of an adenocarcinoma. The period to complete the diagnostic process is approximately three months whereas the therapeutic intervals vary among centres and are shorter for patients with a worse prognosis. The presence of prostatic symptoms, PSA level, and Gleason score influence all the clinical intervals differently.

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A person's physical and social environment is considered as an influencing factor in terms of rates of engagement in physical activity. This study analyses the influence of socio-demographic, physical and social environmental factors on physical activity reported in the adult population in Andalusia. This is a cross-sectional study using data collected in the Andalusia Health Survey in 1999 and 2003. In addition to the influence of the individual's characteristics, if there are no green spaces in the neighbourhood it is less likely that men and women will take exercise (OR = 1.26; 95% CI = 1.13, 1.41). Likewise, a higher local illiteracy rate also has a negative influence on exercise habits in men (OR = 1.39; 95% CI = 1.21, 1.59) and in women (OR = 1.22; 95% CI = 1.07, 1.40). Physical activity is influenced by individuals' characteristics as well as by their social and physical environment, the most disadvantaged groups are less likely to engage in physical activity.

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Background Coronary microvascular dysfunction (CMD) is associated with cardiovascular events in type 2 diabetes mellitus (T2DM). Optimal glycaemic control does not always preclude future events. We sought to assess the effect of the current target of HBA1c level on the coronary microcirculatory function and identify predictive factors for CMD in T2DM patients. Methods We studied 100 patients with T2DM and 214 patients without T2DM. All of them with a history of chest pain, non-obstructive angiograms and a direct assessment of coronary blood flow increase in response to adenosine and acetylcholine coronary infusion, for evaluation of endothelial independent and dependent CMD. Patients with T2DM were categorized as having optimal (HbA1c < 7 %) vs. suboptimal (HbA1c ≥ 7 %) glycaemic control at the time of catheterization. Results Baseline characteristics and coronary endothelial function parameters differed significantly between T2DM patients and control group. The prevalence of endothelial independent CMD (29.8 vs. 39.6 %, p = 0.40) and dependent CMD (61.7 vs. 62.2 %, p = 1.00) were similar in patients with optimal vs. suboptimal glycaemic control. Age (OR 1.10; CI 95 % 1.04–1.18; p < 0.001) and female gender (OR 3.87; CI 95 % 1.45–11.4; p < 0.01) were significantly associated with endothelial independent CMD whereas glomerular filtrate (OR 0.97; CI 95 % 0.95–0.99; p < 0.05) was significantly associated with endothelial dependent CMD. The optimal glycaemic control was not associated with endothelial independent (OR 0.60, CI 95 % 0.23–1.46; p 0.26) or dependent CMD (OR 0.99, CI 95 % 0.43–2.24; p = 0.98). Conclusions The current target of HBA1c level does not predict a better coronary microcirculatory function in T2DM patients. The appropriate strategy for prevention of CMD in T2DM patients remains to be addressed. Keywords: Endothelial dysfunction; Diabetes mellitus; Coronary microcirculation

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Long non-coding RNAs (lncRNAs) are deregulated in several tumors, although their role in acute myeloid leukemia (AML) is mostly unknown.We have examined the expression of the lncRNA HOX antisense intergenic RNA myeloid 1 (HOTAIRM1) in 241 AML patients. We have correlated HOTAIRM1 expression with a miRNA expression profile. We have also analyzed the prognostic value of HOTAIRM1 expression in 215 intermediate-risk AML (IR-AML) patients.The lowest expression level was observed in acute promyelocytic leukemia (P < 0.001) and the highest in t(6;9) AML (P = 0.005). In 215 IR-AML patients, high HOTAIRM1 expression was independently associated with shorter overall survival (OR:2.04;P = 0.001), shorter leukemia-free survival (OR:2.56; P < 0.001) and a higher cumulative incidence of relapse (OR:1.67; P = 0.046). Moreover, HOTAIRM1 maintained its independent prognostic value within the favorable molecular subgroup (OR: 3.43; P = 0.009). Interestingly, HOTAIRM1 was overexpressed in NPM1-mutated AML (P < 0.001) and within this group retained its prognostic value (OR: 2.21; P = 0.01). Moreover, HOTAIRM1 expression was associated with a specific 33-microRNA signature that included miR-196b (P < 0.001). miR-196b is located in the HOX genomic region and has previously been reported to have an independent prognostic value in AML. miR-196b and HOTAIRM1 in combination as a prognostic factor can classify patients as high-, intermediate-, or low-risk (5-year OS: 24% vs 42% vs 70%; P = 0.004).Determination of HOTAIRM1 level at diagnosis provided relevant prognostic information in IR-AML and allowed refinement of risk stratification based on common molecular markers. The prognostic information provided by HOTAIRM1 was strengthened when combined with miR-196b expression. Furthermore, HOTAIRM1 correlated with a 33-miRNA signature.

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BACKGROUND Obesity is positively associated with colorectal cancer. Recently, body size subtypes categorised by the prevalence of hyperinsulinaemia have been defined, and metabolically healthy overweight/obese individuals (without hyperinsulinaemia) have been suggested to be at lower risk of cardiovascular disease than their metabolically unhealthy (hyperinsulinaemic) overweight/obese counterparts. Whether similarly variable relationships exist for metabolically defined body size phenotypes and colorectal cancer risk is unknown. METHODS AND FINDINGS The association of metabolically defined body size phenotypes with colorectal cancer was investigated in a case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Metabolic health/body size phenotypes were defined according to hyperinsulinaemia status using serum concentrations of C-peptide, a marker of insulin secretion. A total of 737 incident colorectal cancer cases and 737 matched controls were divided into tertiles based on the distribution of C-peptide concentration amongst the control population, and participants were classified as metabolically healthy if below the first tertile of C-peptide and metabolically unhealthy if above the first tertile. These metabolic health definitions were then combined with body mass index (BMI) measurements to create four metabolic health/body size phenotype categories: (1) metabolically healthy/normal weight (BMI < 25 kg/m2), (2) metabolically healthy/overweight (BMI ≥ 25 kg/m2), (3) metabolically unhealthy/normal weight (BMI < 25 kg/m2), and (4) metabolically unhealthy/overweight (BMI ≥ 25 kg/m2). Additionally, in separate models, waist circumference measurements (using the International Diabetes Federation cut-points [≥80 cm for women and ≥94 cm for men]) were used (instead of BMI) to create the four metabolic health/body size phenotype categories. Statistical tests used in the analysis were all two-sided, and a p-value of <0.05 was considered statistically significant. In multivariable-adjusted conditional logistic regression models with BMI used to define adiposity, compared with metabolically healthy/normal weight individuals, we observed a higher colorectal cancer risk among metabolically unhealthy/normal weight (odds ratio [OR] = 1.59, 95% CI 1.10-2.28) and metabolically unhealthy/overweight (OR = 1.40, 95% CI 1.01-1.94) participants, but not among metabolically healthy/overweight individuals (OR = 0.96, 95% CI 0.65-1.42). Among the overweight individuals, lower colorectal cancer risk was observed for metabolically healthy/overweight individuals compared with metabolically unhealthy/overweight individuals (OR = 0.69, 95% CI 0.49-0.96). These associations were generally consistent when waist circumference was used as the measure of adiposity. To our knowledge, there is no universally accepted clinical definition for using C-peptide level as an indication of hyperinsulinaemia. Therefore, a possible limitation of our analysis was that the classification of individuals as being hyperinsulinaemic-based on their C-peptide level-was arbitrary. However, when we used quartiles or the median of C-peptide, instead of tertiles, as the cut-point of hyperinsulinaemia, a similar pattern of associations was observed. CONCLUSIONS These results support the idea that individuals with the metabolically healthy/overweight phenotype (with normal insulin levels) are at lower colorectal cancer risk than those with hyperinsulinaemia. The combination of anthropometric measures with metabolic parameters, such as C-peptide, may be useful for defining strata of the population at greater risk of colorectal cancer.