Search Results (3)

Background/Objectives: In 2020, ovarian cancer ranked fourth in global incidence among gynecological cancers and remains the deadliest cancer affecting women’s health. Survival rates are significantly higher when the disease is detected at early stages; however, the lack of effective early detection methods underscores the importance of identifying risk factors in order to implement preventive strategies. The objective of this work is to provide an overview of the risk factors of ovarian cancer in South America, emphasizing those linked to social determinants, genetic components, and comorbidities. Methods: A literature search was performed using PubMed and Google Scholar. MeSH descriptors and keywords, such as “BRCA1 genes,” “BRCA2 genes”, “Latin America”, and “ovarian neoplasms” were used, along with terms related to socioeconomic and health factors. Inclusion criteria focused on original studies published in the last five years involving South American women. Results: Studies were identified from Argentina, Brazil, Chile, Colombia, Ecuador, and Peru. These studies addressed genetic factors, health status at diagnosis, and sociodemographic factors, revealing important data gaps, particularly on contraception and hormone replacement therapy. The prevalence of BRCA1 and BRCA2 mutations in South America is estimated to be 15–20% among women with inherited risk factors. Social, demographic and economic factors vary by country, although commonalities include a higher prevalence among women over 50 years of age, those with limited education, and those who face barriers to accessing health care. Conclusions: Although the literature does not conclusively establish a direct link between obesity and/or diabetes and the development of ovarian cancer, the indirect association highlights the need for further clinical studies. A general research gap related to risk factors of ovarian cancer could be observed in the South American region. Full article

Tsunami inundation estimates are of crucial importance to hazard and risk assessments. In the context of tsunami forecast, numerical simulations are becoming more feasible with the growth of computational power. Uncertainties regarding source determination within the first minutes after a tsunami generation might be a major concern in the issuing of an appropriate warning on the coast. However, it is also crucial to investigate differences emerging from the chosen algorithms for the tsunami simulations due to a dependency of the outcomes on the suitable model settings. In this study, we compare the tsunami inundation in three cities in central Chile (Coquimbo, Viña del Mar, and Valparaíso) using three different models (TsunAWI, Tsunami-HySEA, COMCOT) while varying the parameters such as bottom friction. TsunAWI operates on triangular meshes with variable resolution, whereas the other two codes use nested grids for the coastal area. As initial conditions of the experiments, three seismic sources (2010 $Mw$ 8.8 Maule, 2015 $Mw$ 8.3 Coquimbo, and 1730 $Mw$ 9.1 Valparaíso) are considered for the experiments. Inundation areas are determined with high-resolution topo-bathymetric datasets based on specific wetting and drying implementations of the numerical models. We compare each model’s results and sensitivities with respect to parameters such as bottom friction and bathymetry representation in the varying mesh geometries. The outcomes show consistent estimates for the nearshore wave amplitude of the leading wave crest based on identical seismic source models within the codes. However, with respect to inundation, we show high sensitivity to Manning values where a non-linear behaviour is difficult to predict. Differences between the relative decrease in inundation areas and the Manning n-range (0.015–0.060) are high (11–65%), with a strong dependency on the characterization of the local topo-bathymery in the Coquimbo and Valparaíso areas. Since simulations carried out with such models are used to generate hazard estimates and warning products in an early tsunami warning context, it is crucial to investigate differences that emerge from the chosen algorithms for the tsunami simulations. Full article

Background: Walking pace is a well-known indicator of physical capability, but it is also a strong predictor of type 2 diabetes (T2D). However, there is a lack of evidence on the association between walking pace and T2D, specifically, within developing countries such as Chile. Aim: To investigate the association between self-reported walking pace and T2D in the Chilean adult population. Methods: 5520 Chilean participants (aged 15 to 90 years, 52.1% women) from the Chilean National Health Survey 2016–2017 were included in this cross-sectional study. Both walking pace (slow, average, and brisk) and diabetes data were collected through self-reported methods. Fasting blood glucose (reported in mg/dl) and glycosylated haemoglobin A (HbA1c) scores were determined via blood exams. Results: In the unadjusted model, and compared to people who reported a slow walking pace, those with average and brisk walking pace had lower blood glucose levels (β = −7.74 mg/dL (95% CI: −11.08 to −4.40) and β = −11.05 mg/dL (95% CI: −14.36 to −7.75), respectively) and lower HbA1c (β = −0.34% (95% CI: −0.57 to −0.11) and β= −0.72% (95% CI: −0.94 to −0.49)), respectively. After adjusting for sociodemographic, Body Mass Index and lifestyle factors, the association between glycaemia and HbA1c remained only for brisk walkers. Both the average and brisk walker categories had lower odds of T2D (OR: 0.59 (95% CI: 0.41 to 0.84) and (OR 0.48 (95% CI: 0.30 to 0.79), respectively). Conclusion: Brisk walkers were associated with lower blood glucose and HbA1c levels. Moreover, average to brisk walking pace also showed a lower risk for T2D. Full article