RESUMO
The Steno Diabetes Center Copenhagen developed the Steno T1 Risk Engine (ST1RE) to predict cardiovascular events, encompassing fatal and nonfatal ischemic heart disease, ischemic stroke, heart failure, and peripheral arterial disease in type 1 diabetes mellitus(T1DM).The current study investigated the agreement between ST1RE and the Brazilian Society for Endocrinology and Metabology (SBEM) classification. Participants were included in the study if diagnosed with T1DM and had at least one outpatient visit in 2021. Patients with established cardiovascular disease and chronic kidney disease on dialysis were excluded. Clinical parameters were obtained from medical records, such as age, body mass index (BMI), blood pressure, physical activity, current smoking, microvascular target organ damage, levels of low-density lipoprotein cholesterol, creatinine, glycated hemoglobin (HbA1c), and albuminuria.Overall, 92 patients (38 males and 53 females) with an age median (P25; P75) of 33 years (25.5;42.5), BMI of 24.8 + 4.1 kg/m2, and duration of diabetes (mean ± SD) of 23.4 + 9.5 years were evaluated. There were no differences considering the gender for most analyzed variables, but a higher proportion of women exhibited microvascular complications such as microalbuminuria, macroalbuminuria, and retinopathy. Our results show a weak agreement in the 10-year cardiovascular risk estimation between SBEM and ST1RE classifications. According to SBEM criteria, 72.8% of patients were considered high-risk, while only 15.2% of patients received the same classification using ST1RE. The dissimilarities between these two classifications were also evident when age and gender factors were compared. While 60% of patients under 35 years were classified as high risk according to SBEM criteria, only 1.8% received this stratification risk in the ST1RE classification.The results indicate a low agreement between the 10-year cardiovascular event risk classification by SBEM and the classification by ST1RE for type 1 diabetes patients without established cardiovascular disease.
RESUMO
Culex quinquefasciatus resistance to the binary (Bin) toxin, the major larvicidal component from Lysinibacillus sphaericus, is associated with mutations in the cqm1 gene, encoding the Bin-toxin receptor. Downregulation of the cqm1 transcript was found in the transcriptome of larvae resistant to the L. sphaericus IAB59 strain, which produces both the Bin toxin and a second binary toxin, Cry48Aa/Cry49Aa. Here, we investigated the transcription profiles of two other mosquito colonies having Bin resistance only. These confirmed the cqm1 downregulation and identified transcripts encoding the enzyme pantetheinase as the most downregulated mRNAs in both resistant colonies. Further quantification of these transcripts reinforced their strong downregulation in Bin-resistant larvae. Multiple genes were found encoding this enzyme in Cx. quinquefasciatus and a recombinant pantetheinase was then expressed in Escherichia coli and Sf9 cells, with its presence assessed in the midgut brush border membrane of susceptible larvae. The pantetheinase was expressed as a ~70 kDa protein, potentially membrane-bound, which does not seem to be significantly targeted by glycosylation. This is the first pantetheinase characterization in mosquitoes, and its remarkable downregulation might reflect features impacted by co-selection with the Bin-resistant phenotype or potential roles in the Bin-toxin mode of action that deserve to be investigated.
Assuntos
Amidoidrolases , Bacillaceae , Bacillus , Culex , Animais , Regulação para Baixo , Escherichia coli , Larva , Proteínas Ligadas por GPIRESUMO
Indoor navigation systems offer many application possibilities for people who need information about the scenery and the possible fixed and mobile obstacles placed along the paths. In these systems, the main factors considered for their construction and evaluation are the level of accuracy and the delivery time of the information. However, it is necessary to notice obstacles placed above the user's waistline to avoid accidents and collisions. In this paper, different methodologies are associated to define a hybrid navigation model called iterative pedestrian dead reckoning (i-PDR). i-PDR combines the PDR algorithm with a Kalman linear filter to correct the location, reducing the system's margin of error iteratively. Obstacle perception was addressed through the use of stereo vision combined with a musical sounding scheme and spoken instructions that covered an angle of 120 degrees in front of the user. The results obtained in the margin of error and the maximum processing time are 0.70 m and 0.09 s, respectively, with obstacles at ground level and suspended with an accuracy equivalent to 90%.