RESUMEN
Flystrike remains an important animal health issue on New Zealand sheep farms. To date no useful predictive tool to assist farmers to develop control options has been available. The aim of this study was to use National Institute of Water and Atmospheric Research (NIWA) virtual climate station data in New Zealand to develop a weather-based model to accurately predict the presence of Lucilia spp. on sheep farms throughout New Zealand. Three LuciTrap® baited fly traps were positioned on each of eight sheep farms throughout New Zealand (5 in the North Island and 3 in the South Island). The traps were put out for both the 2018-2019 and 2019-2020 seasons. They were emptied each week and the flies morphologically identified; with the counts of Lucilia cuprina and L. sericata combined as Lucilia spp. The count data for Lucilia spp. for each week of trapping was transformed into a binary outcome and a generalised linear mixed effects models fitted to the data, with farm as a random effect. The dependent variable was Lucilia spp. flies caught, yes or no, and the independent variables were mean weekly climate variables from the nearest NIWA virtual climate station to that farm. The model was trained on the 2018-2019 catch data and tested on the 2019-2020 catch data. A cut point was identified which maximised the model's ability to correctly predict whether Lucilia spp. were present or not for the 2019-2020 catch data, and the sensitivity, specificity, accuracy, and area under the curve (AUC) of the model calculated. The final model included just 3 significant variables, mean weekly 10 cm soil temperature, mean weekly soil moisture index, and mean weekly wind speed at 10 m. Mean weekly 10 cm soil temperature accounted for 64.7% of the variance explained by the model, mean weekly soil moisture index 34.7% and mean weekly wind speed at 10 m only 0.6%. The results showed that the predictive model had a sensitivity of 0.93 (95% CI = 0.80-0.98) and a specificity of 0.75 (95% CI = 0.62-0.85), using a cut point for the probability of Lucilia spp. being present on farm = 0.383. This model provides New Zealand farmers with a tool which will allow them to know when Lucilia spp. flies will likely be present and thus more accurately plan their interventions to prevent flystrike.
Asunto(s)
Dípteros , Miasis , Animales , Ovinos , Granjas , Nueva Zelanda/epidemiología , Tiempo (Meteorología) , Miasis/veterinaria , Calliphoridae , SueloRESUMEN
Excessive deposit of epicardial adipose tissue (EAT) were recently shown to be positively correlated with cardiovascular disease (CVD). This study aims to investigate the thickness of EAT and its association with the components of metabolic syndrome among multi-ethnic Malaysians with and without acute coronary syndrome (ACS). A total of 213 patients were recruited, with the thickness of EAT were quantified non-invasively using standard two-dimensional echocardiography. EAT thickness among the Malaysian population was prompted by several demographic factors and medical comorbidities, particularly T2DM and dyslipidaemia. ACS patients have significantly thicker EAT compared to those without ACS (4.1 mm vs 3.7 mm, p = 0.035). Interestingly, among all the races, Chinese had the thickest EAT distribution (4.6 mm vs 3.8 mm), with age (p = 0.04 vs p < 0.001), and overall diastolic blood pressure (p = 0.028) was also found to be associated with EAT thickness. Further study is warranted to investigate its role as a cardiovascular risk marker among Malaysians with ACS.