12 research outputs found

    Uroflowmetry in a Large Population of Brazilian Men Submitted to a health check up program and its correlation with ipss and prostate size

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    Purpose: the aim of this study was to assess the uroflowmetry data in a large population of asymptomatic Brazilian men submitted to a health check up program and their correlation to IPSS and prostate size.Materials and Methods: Asymptomatic men underwent a health check-up program between January and December 2012. the inclusion criteria were men between 40 and 70 years, IPSS <= 7, without bladder, prostate, urethral surgery, neurological diseases, urinary tract infection, PSA < 4.0 ng/dL and urinary volume higher than 150 mL. Urological assessment consisted of clinical history, IPSS, digital rectal examination (DRE), prostate specific antigen (PSA), urinalysis, ultrasonography and uroflowmetry.Results: A total of 1041 asymptomatic men were included in this study. the average age was 49 years and average maximum flow rate was 17.4 mL/s. in spite of IPSS and prostate size increase with aging, they had a weak correlation with Q(max) cutoffs (10 mL/s and 15 mL/s). A total of 85 men (8.3%) had more than 60 years, and even in this group, Q(max) was higher than 15 mL/s. Out of 1041 men, 117 had IPSS less than 8 and Q(max) less than 10 mL/s.Conclusions: in asymptomatic men there is a weak correlation between IPSS, prostate size and uroflowmetric data. the establishment of different normal cutoffs seems to be complicated and uroflowmetry data should be interpreted with caution in order to avoid misdiagnosis.Hosp Israelita Albert Einstein, São Paulo, BrazilUniversidade Federal de São Paulo, São Paulo, BrazilWake Forest Univ, Winston Salem, NC 27109 USAUniversidade Federal de São Paulo, São Paulo, BrazilWeb of Scienc

    Impact of screening and monitoring of capillary blood glucose in the detection of hyperglycemia and hypoglycemia in non-critical inpatients

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    Objective: To evaluate the impact of screening hyper and hypoglycemia measured by capillary glycemia and standard monitorization of  hyperglycemic patients hospitalized in regular care units of Hospital Israelita Albert Einstein. Methods: The capillary glycemia was  measured by the Precision PCx (Abbott) glucosimeter, using the PrecisionWeb (Abbott) software. The detection of hyper and hypoglycemia during the months of May/June were compared to those of March/April in 2009 and to the frequency of the diagnosis of diabetes in 2007. Rresults: There was an increase in the glycemia screening from 27.7 to 77.5% of hospitalized patients (p < 0.001), of hyperglycemia detection (from 9.3 to 12.2%; p < 0.001) and of hypoglycemia (from 1.5 to 3.3%; p < 0.001) during  the months of May/June  2009. According to this action 14 patients for each additional case of hyperglycemia and 26 cases for each case of hypoglycemia were identified. The detection of hyperglycemia was significantly higher (p < 0.001) than the frequency of registered diagnosis related do diabetes in the year of 2007. Cconclusions: the adoption of an institutional program of glycemia monitorization improves the detection of hyper and hypoglycemia and glycemia control in hospitalized patients in regular care units

    International Nosocomial Infection Control Consortiu (INICC) report, data summary of 43 countries for 2007-2012. Device-associated module

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    We report the results of an International Nosocomial Infection Control Consortium (INICC) surveillance study from January 2007-December 2012 in 503 intensive care units (ICUs) in Latin America, Asia, Africa, and Europe. During the 6-year study using the Centers for Disease Control and Prevention's (CDC) U.S. National Healthcare Safety Network (NHSN) definitions for device-associated health care–associated infection (DA-HAI), we collected prospective data from 605,310 patients hospitalized in the INICC's ICUs for an aggregate of 3,338,396 days. Although device utilization in the INICC's ICUs was similar to that reported from ICUs in the U.S. in the CDC's NHSN, rates of device-associated nosocomial infection were higher in the ICUs of the INICC hospitals: the pooled rate of central line–associated bloodstream infection in the INICC's ICUs, 4.9 per 1,000 central line days, is nearly 5-fold higher than the 0.9 per 1,000 central line days reported from comparable U.S. ICUs. The overall rate of ventilator-associated pneumonia was also higher (16.8 vs 1.1 per 1,000 ventilator days) as was the rate of catheter-associated urinary tract infection (5.5 vs 1.3 per 1,000 catheter days). Frequencies of resistance of Pseudomonas isolates to amikacin (42.8% vs 10%) and imipenem (42.4% vs 26.1%) and Klebsiella pneumoniae isolates to ceftazidime (71.2% vs 28.8%) and imipenem (19.6% vs 12.8%) were also higher in the INICC's ICUs compared with the ICUs of the CDC's NHSN

    BioTIME:a database of biodiversity time series for the Anthropocene

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    Motivation: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community led open-source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene.Main types of variables included: The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of two, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology andcontextual information about each record.Spatial location and grain: BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km2 (158 cm2) to 100 km2 (1 000 000 000 000 cm2).Time period and grain: BioTIME records span from 1874 to 2016. The minimum temporal grain across all datasets in BioTIME is year.Major taxa and level of measurement: BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton, and terrestrial invertebrates to small and large vertebrates.Software format: .csv and .SQ
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