3 resultados para Load factors

em Biblioteca Digital da Produção Intelectual da Universidade de São Paulo


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As part of the international CUPID investigation, we compared physical and psychosocial risk factors for musculoskeletal disorders among nurses in Brazil and Italy. Using questionnaires, we collected information on musculoskeletal disorders and potential risk factors from 751 nurses employed in public hospitals. By fitting country-specific multiple logistic regression models, we investigated the association of stressful physical activities and psychosocial characteristics with site-specific and multisite pain, and associated sickness absence. We found no clear relationship between low back pain and occupational lifting, but neck and shoulder pain were more common among nurses who reported prolonged work with the arms in an elevated position. After adjustment for potential confounding variables, pain in the low back, neck and shoulder, multisite pain, and sickness absence were all associated with somatizing tendency in both countries. Our findings support a role of somatizing tendency in predisposition to musculoskeletal disorders, acting as an important mediator of the individual response to triggering exposures, such as work-load.

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Interleukin-18 (IL-18) and interferon-gamma (IFN-?) exert important functions in both innate and adaptive immune responses against intracellular pathogens and viruses. Previous studies suggested that host genetic factors, including cytokines gene polymorphisms, could be involved in the pathogenesis of human T-cell leukemia virus type 1 (HTLV-1)-associated myelopathy/tropical spastic paraparesis (HAM/TSP). Thus, we analyzed -137C/G and -607A/C of the IL-18 promoter and +874T/A of the IFN-? in DNA samples from 98 HTLV-1-infected individuals exhibiting or not clinical symptoms and 150 healthy control individuals. The IL-18 promoter -607CC genotype was significantly lower in HTLV-1 asymptomatic carriers (HAC) and HTLV-1-infected individuals (HAC + HAM/TSP) than healthy control group. In contrast, the -607AC genotype was significantly higher in HAC and HTLV-1-infected individuals group compared to the healthy control group. The -137G/-607A IL-18 haplotype was higher in infected group than healthy control group, and the -137C/-607C IL-18 haplotype was increased in the healthy control group compared to the others. Finally, the IFN-? polymorphism analysis showed that the HTLV-1-infected individuals with +874AT genotype presented higher proviral load than +874AA genotype. These data indicate that the IL-18-607AC genotype and -137G/-607A haplotype could be a risk factor for HTLV-1 infection, whereas the protective effect could be conferred by -607CC genotype and -137C/-607C haplotype. Also, the IFN-? could be implicated on the proviral load levels.

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Background: This study evaluated a wide range of viral load (VL) thresholds to identify a cut-point that best predicts new clinical events in children on stable highly active antiretroviral therapy (HAART). Methods: Cox proportional hazards modeling was used to assess the adjusted risk for World Health Organization stage 3 or 4 clinical events (WHO events) as a function of time-varying CD4, VL, and hemoglobin values in a cohort study of Latin American children on HAART >= 6 months. Models were fit using different VL cut-points between 400 and 50,000 copies per milliliter, with model fit evaluated on the basis of the minimum Akaike information criterion value, a standard model fit statistic. Results: Models were based on 67 subjects with WHO events out of 550 subjects on study. The VL cut-points of >2600 and >32,000 copies per milliliter corresponded to the lowest Akaike information criterion values and were associated with the highest hazard ratios (2.0, P = 0.015; and 2.1, P = 0.0058, respectively) for WHO events. Conclusions: In HIV-infected Latin American children on stable HAART, 2 distinct VL thresholds (>2600 and >32,000 copies/mL) were identified for predicting children at significantly increased risk for HIV-related clinical illness, after accounting for CD4 level, hemoglobin level, and other significant factors.