Identification of Toxocara spp. eggs in dog hair and associated risk factors.
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ABSTRACT:
SUBMITTER: Rojas TO
PROVIDER: S-EPMC5553150 | biostudies-other | 2017 Jul
REPOSITORIES: biostudies-other
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Project description:Leptospirosis is a neglected worldwide zoonotic bacterial disease with a high prevalence in subtropical and tropical countries. The prevalence of Leptospira spp. in humans, cattle and dogs is unknown in Bhutan. Therefore, we sought to find out whether humans, cattle or dogs had been infected in the past with leptospires by measuring antibodies in the serum. We therefore collected blood from 864 humans ≥13 years of age, 130 bovines and 84 dogs from different rural and urban areas in Bhutan and tested the serum for antibodies specific for leptospires with a screening of enzyme-linked immunosorbent assays (ELISA) and a confirmatory microscopic agglutination test (MAT). In humans, 17.6% were seropositive by ELISA and 1.6% by MAT. The seropositivity was stronger in bovines (36.9%) and dogs (47.6%). "Having had a fever recently" (OR 5.2, p = 0.004), "working for the military" (OR 26.6, p = 0.028) and "being unemployed" (OR 12.9, p = 0.041) (reference category = housemaker) were statistically significantly associated with seropositivity when controlled for the effects of other risk factors. However, due to the small number of positive test results, the findings on risk factors should be interpreted with caution. Based on the serogroups found in the three species, dogs could be a source of infection for humans, or dogs and humans are exposed to the same environmental risk factors Clinical leptospirosis in humans and domestic animals should be investigated by testing blood and urine for the presence of leptospires by molecular methods (qPCR).
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Project description:BackgroundToxocara canis and Toxocara cati are globally distributed roundworms and causative agents of human toxocariasis, via ingestion of Toxocara eggs. Control of Toxocara infections is constrained by a lack of sensitive methods for screening of animal faeces and environmental samples potentially contaminated by Toxocara eggs. In this work, a pre-analytical method for efficient extraction of DNA from Toxocara eggs in environmental samples was set up using our previously validated T. canis- and T. cati-specific quantitative real-time polymerase chain reaction (qPCR). For this purpose, the influence of different methods for egg lysis, DNA extraction and purification for removal of PCR inhibitors were assessed on environmental samples.MethodsTo select the best egg disruption method, six protocols were compared on pure T. canis egg suspensions, including enzymatic lysis and thermal or mechanical disruption. Based on the selected best method, an analytical workflow was set up to compare two DNA extraction methods (FastDNA™ SPIN Kit for Soil versus DNeasy® PowerMax® Soil Kit) with an optional dilution and/or clean-up (Agencourt® AMPure®) step. This workflow was evaluated on 10-g soil and 10-g sand samples spiked with egg suspensions of T. canis (tenfold dilutions of 104 eggs in triplicate). The capacity of the different methods, used alone or in combination, to increase the ratio of positive tests was assessed. The resulting optimal workflow for processing spiked soil samples was then tested on environmental soil samples and compared with the conventional flotation-centrifugation and microscopic examination of Toxocara eggs.ResultsThe most effective DNA extraction method for Toxocara eggs in soil samples consisted in the combination of mechanical lysis of eggs using beads, followed by DNA extraction with the DNeasy® PowerMax® Soil Kit, and completed with an additional DNA clean-up step with AMPure® beads and a sample DNA dilution (1:10). This workflow exhibited a limit of detection of 4 and 46 T. canis eggs in 10-g sand and 10-g soil samples, respectively.ConclusionsThe pre-analytical flow process developed here combined with qPCR represents an improved, potentially automatable, and cost-effective method for the surveillance of Toxocara contamination in the environment.
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Project description:Leptospirosis is a ubiquitous distributed zoonotic infectious disease present in wild and domestic animals. This study aimed to estimate within-herd and herd-level seroprevalence against pathogenic Leptospira spp. in dairy cows from southern Chile and identify risk factors associated with the herd-level status. We used a multi-stage strategy combined with a stratified sample strategy for randomly sampling 147 herds and 4876 lactating cows. We considered as infected a herd with at least one positive reactor to MAT. In addition, an epidemiological survey was applied to the herd's owners and a logistic regression (LR) model was constructed to analyze it. The overall within-herd prevalence was 5.9% (95% CI 4.9-6.8), the overall herd-level prevalence was 42.2% (95% CI 9.2-47.9), and there was variation in both between different herd sizes. L. borgpetersenii serovars Hardjo and Tarassovi and L. interrogans serovar Pomona were the more frequent serovars in non-vaccinated herds. A factor that decreases the risk of a farm being infected was Leptospira vaccine usage (OR = 0.04; 95% CI = 0.02-0.11), and variables that increase risk were using bulls for mating (OR = 3.43; 95% CI = 1.1-10.1) and continuous calving distribution (OR = 3.4; 95% CI = 1.3-8.8). The study's results will contribute to unravelling the infection burden in the main dairy area of the country and designing control strategies.
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Project description:Each domestic dog breed is characterized by a strict set of physical and behavioral characteristics by which breed members are judged and rewarded in conformation shows. One defining feature of particular interest is the coat, which is comprised of either a double- or single-layer of hair. The top coat contains coarse guard hairs and a softer undercoat, similar to that observed in wolves and assumed to be the ancestral state. The undercoat is absent in single-coated breeds which is assumed to be the derived state. We leveraged single nucleotide polymorphism (SNP) array and whole genome sequence (WGS) data to perform genome-wide association studies (GWAS), identifying a locus on chromosome (CFA) 28 which is strongly associated with coat number. Using WGS data, we identified a locus of 18.4 kilobases containing 62 significant variants within the intron of a long noncoding ribonucleic acid (lncRNA) upstream of ADRB1. Multiple lines of evidence highlight the locus as a potential cis-regulatory module. Specifically, two variants are found at high frequency in single-coated dogs and are rare in wolves, and both are predicted to affect transcription factor (TF) binding. This report is among the first to exploit WGS data for both GWAS and variant mapping to identify a breed-defining trait.
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Project description:ObjectivesCoronavirus Disease 2019 (COVID-19) has become a pandemic outbreak. Risk stratification at hospital admission is of vital importance for medical decision making and resource allocation. There is no sophisticated tool for this purpose. This study aimed to develop neural network models with predictors selected by genetic algorithms (GA).MethodsThis study was conducted in Wuhan Third Hospital from January 2020 to March 2020. Predictors were collected on day 1 of hospital admission. The primary outcome was the vital status at hospital discharge. Predictors were selected by using GA, and neural network models were built with the cross-validation method. The final neural network models were compared with conventional logistic regression models.ResultsA total of 246 patients with COVID-19 were included for analysis. The mortality rate was 17.1% (42/246). Non-survivors were significantly older (median (IQR): 69 (57, 77) vs. 55 (41, 63) years; p < 0.001), had higher high-sensitive troponin I (0.03 (0, 0.06) vs. 0 (0, 0.01) ng/L; p < 0.001), C-reactive protein (85.75 (57.39, 164.65) vs. 23.49 (10.1, 53.59) mg/L; p < 0.001), D-dimer (0.99 (0.44, 2.96) vs. 0.52 (0.26, 0.96) mg/L; p < 0.001), and ?-hydroxybutyrate dehydrogenase (306.5 (268.75, 377.25) vs. 194.5 (160.75, 247.5); p < 0.001) and a lower level of lymphocyte count (0.74 (0.41, 0.96) vs. 0.98 (0.77, 1.26) × 109/L; p < 0.001) than survivors. The GA identified a 9-variable (NNet1) and a 32-variable model (NNet2). The NNet1 model was parsimonious with a cost on accuracy; the NNet2 model had the maximum accuracy. NNet1 (AUC: 0.806; 95% CI [0.693-0.919]) and NNet2 (AUC: 0.922; 95% CI [0.859-0.985]) outperformed the linear regression models.ConclusionsOur study included a cohort of COVID-19 patients. Several risk factors were identified considering both clinical and statistical significance. We further developed two neural network models, with the variables selected by using GA. The model performs much better than the conventional generalized linear models.
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