RESUMO
Purpose: To compare the accuracy of machine learning (ML) algorithms to classify the sex of the participant from retinal thickness datasets in different retinal layers. Methods: This cross-sectional study involved 26 male and 38 female subjects. Data were acquired using HRA + OCT Spectralis, and the thickness and volume of 10 retinal layers were quantified. A total of 10 features were extracted from each retinal layer. The accuracy of various algorithms, including k-nearest-neighbor, support vector classifier, logistic regression, linear discriminant analysis, random forest, decision tree, and Gaussian Naïve Bayes, was quantified. A two-way ANOVA was conducted to assess the ML accuracy, considering both the classifier type and the retinal layer as factors. Results: A comparison of the accuracies achieved by various algorithms in classifying participant sex revealed superior results in datasets related to total retinal thickness and the retinal nerve fiber layer. In these instances, no significant differences in algorithm performance were observed (p > 0.05). Conversely, in other layers, a decrease in classification accuracy was noted as the layer moved outward in the retina. Here, the random forest (RF) algorithm demonstrated superior performance compared to the others (p < 0.05). Conclusion: The current research highlights the distinctive potential of various retinal layers in sex classification. Different layers and ML algorithms yield distinct accuracies. The RF algorithm's consistent superiority suggests its effectiveness in identifying sex-related features from a range of retinal layers.
RESUMO
Metabolic syndrome has been considered a factor of vulnerability and a major public health problem because it increases the risk of cardiovascular disease and type 2 diabetes. The present study from Amazonas, Brazil aimed to estimate the prevalence of the individual and general components of metabolic syndrome in adults and older adults and identify the independent predictors of metabolic syndrome. The sample of the present cross-sectional study comprised 942 participants (590 women), with a mean age of 59.8 ± 19.7 (range: 17.5 to 91.8). Blood pressure in men (62.5%), abdominal obesity in women (67.3%), and lower high-density lipoprotein cholesterol (HDL-C) in both (52.2% in men and 65.0% in women) were the most prevalent individual risk factors for metabolic syndrome. Women had a higher prevalence of abdominal obesity (p < 0.001), low HDL-C (p < 0.001), and metabolic syndrome (p < 0.001) than men; however, opposite results were seen in men for blood pressure (p < 0.001). The overall prevalence of metabolic syndrome was 47.5%. Advanced age, being female, having a higher body mass index, and a having lower educational level independently increased the odds of metabolic syndrome. Due to the association of metabolic syndrome with deterioration of health status and increased vulnerability, this study sustains the need for early public health interventions in the Amazonas region.
Assuntos
Diabetes Mellitus Tipo 2 , Síndrome Metabólica , Idoso , Brasil/epidemiologia , Estudos Transversais , Feminino , Humanos , Masculino , Síndrome Metabólica/epidemiologia , Obesidade , Prevalência , Fatores de RiscoRESUMO
We used blood samples of the Blue-footed Booby, considering sex (female and male) and age-class (adult and chick) of individuals at different breeding stages during two breeding seasons (2010-2011 and 2011-2012) in Isla El Rancho, Sinaloa, to determine lead, cadmium, and mercury concentrations. Lead and cadmium concentrations were below our detection limit (0.05 and 0.36ppm, respectively). A higher concentration of mercury was found in early stages of breeding, likely related to changes in mercury environmental availability. Mercury concentrations in adults did not relate with their breeding output. Males and adults had higher mercury concentration than females and chicks. We provide information of temporal, sex and age-related variations in the concentrations of mercury in blood of the Blue-footed Booby.