188 research outputs found

    PMS5 PERSISTENCE WITH BISPHOSPHONATE THERAPY AND RISK OF HIP FRACTURE

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    Primer pla d'un senzill edifici d'habitatges de principis del s. XIX. Consta de planta baixa i quatre plantes pis, separades cada una de les plantes per impostes. S'estructura sobre la base d'eixos de simetria verticals

    Prevalence of Disorders Recorded in Dogs Attending Primary-Care Veterinary Practices in England

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    Purebred dog health is thought to be compromised by an increasing occurence of inherited diseases but inadequate prevalence data on common disorders have hampered efforts to prioritise health reforms. Analysis of primary veterinary practice clinical data has been proposed for reliable estimation of disorder prevalence in dogs. Electronic patient record (EPR) data were collected on 148,741 dogs attending 93 clinics across central and south-eastern England. Analysis in detail of a random sample of EPRs relating to 3,884 dogs from 89 clinics identified the most frequently recorded disorders as otitis externa (prevalence 10.2%, 95% CI: 9.1-11.3), periodontal disease (9.3%, 95% CI: 8.3-10.3) and anal sac impaction (7.1%, 95% CI: 6.1-8.1). Using syndromic classification, the most prevalent body location affected was the head-and-neck (32.8%, 95% CI: 30.7-34.9), the most prevalent organ system affected was the integument (36.3%, 95% CI: 33.9-38.6) and the most prevalent pathophysiologic process diagnosed was inflammation (32.1%, 95% CI: 29.8-34.3). Among the twenty most-frequently recorded disorders, purebred dogs had a significantly higher prevalence compared with crossbreds for three: otitis externa (Pā€Š=ā€Š0.001), obesity (Pā€Š=ā€Š0.006) and skin mass lesion (Pā€Š=ā€Š0.033), and popular breeds differed significantly from each other in their prevalence for five: periodontal disease (Pā€Š=ā€Š0.002), overgrown nails (Pā€Š=ā€Š0.004), degenerative joint disease (Pā€Š=ā€Š0.005), obesity (Pā€Š=ā€Š0.001) and lipoma (Pā€Š=ā€Š0.003). These results fill a crucial data gap in disorder prevalence information and assist with disorder prioritisation. The results suggest that, for maximal impact, breeding reforms should target commonly-diagnosed complex disorders that are amenable to genetic improvement and should place special focus on at-risk breeds. Future studies evaluating disorder severity and duration will augment the usefulness of the disorder prevalence information reported herein

    Albuminuria Testing in Hypertension and Diabetes:An Individual-Participant Data Meta-Analysis in a Global Consortium

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    Albuminuria is an under-recognized component of chronic kidney disease definition, staging, and prognosis. Guidelines, particularly for hypertension, conflict on recommendations for urine albumin-to-creatinine ratio (ACR) measurement. Separately among 1 344 594 adults with diabetes and 2 334 461 nondiabetic adults with hypertension from the chronic kidney disease Prognosis Consortium, we assessed ACR testing, estimated the prevalence and incidence of ACR ā‰„30 mg/g and developed risk models for ACR ā‰„30 mg/g. The ACR screening rate (cohort range) was 35.1% (12.3%-74.5%) in diabetes and 4.1% (1.3%-20.7%) in hypertension. Screening was largely unrelated to the predicted risk of prevalent albuminuria. The median prevalence of ACR ā‰„30 mg/g across cohorts was 32.1% in diabetes and 21.8% in hypertension. Higher systolic blood pressure was associated with a higher prevalence of albuminuria (odds ratio [95% CI] per 20 mm Hg in diabetes, 1.50 [1.42-1.60]; in hypertension, 1.36 [1.28-1.45]). The ratio of undetected (due to lack of screening) to detected ACR ā‰„30 mg/g was estimated at 1.8 in diabetes and 19.5 in hypertension. Among those with ACR/g, the median 5-year incidence of ACR ā‰„30 mg/g across cohorts was 23.9% in diabetes and 21.7% in hypertension. Incident albuminuria was associated with initiation of renin-angiotensin-aldosterone system inhibitors (incidence-rate ratio [95% CI], diabetes 3.09 [2.71-3.53]; hypertension 2.87 [2.29-3.59]). In conclusion, despite similar risk of albuminuria to those with diabetes, ACR screening in patients with hypertension was low. Our findings suggest that regular albuminuria screening should be emphasized to enable early detection of chronic kidney disease and initiation of treatment with cardiovascular and renal benefits

    GPU-based Low Dose CT Reconstruction via Edge-preserving Total Variation Regularization

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    High radiation dose in CT scans increases a lifetime risk of cancer and has become a major clinical concern. Recently, iterative reconstruction algorithms with Total Variation (TV) regularization have been developed to reconstruct CT images from highly undersampled data acquired at low mAs levels in order to reduce the imaging dose. Nonetheless, TV regularization may lead to over-smoothed images and lost edge information. To solve this problem, in this work we develop an iterative CT reconstruction algorithm with edge-preserving TV regularization to reconstruct CT images from highly undersampled data obtained at low mAs levels. The CT image is reconstructed by minimizing an energy consisting of an edge-preserving TV norm and a data fidelity term posed by the x-ray projections. The edge-preserving TV term is proposed to preferentially perform smoothing only on non-edge part of the image in order to avoid over-smoothing, which is realized by introducing a penalty weight to the original total variation norm. Our iterative algorithm is implemented on GPU to improve its speed. We test our reconstruction algorithm on a digital NCAT phantom, a physical chest phantom, and a Catphan phantom. Reconstruction results from a conventional FBP algorithm and a TV regularization method without edge preserving penalty are also presented for comparison purpose. The experimental results illustrate that both TV-based algorithm and our edge-preserving TV algorithm outperform the conventional FBP algorithm in suppressing the streaking artifacts and image noise under the low dose context. Our edge-preserving algorithm is superior to the TV-based algorithm in that it can preserve more information of fine structures and therefore maintain acceptable spatial resolution.Comment: 21 pages, 6 figures, 2 table

    Comparing three basic models for seasonal influenza

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    In this paper we report the use of the open source Spatiotemporal Epidemiological Modeler (STEM, www.eclipse.org/stem) to compare three basic models for seasonal influenza transmission. The models are designed to test for possible differences between the seasonal transmission of influenza A and B. Model 1 assumes that the seasonality and magnitude of transmission do not vary between influenza A and B. Model 2 assumes that the magnitude of seasonal forcing (i.e., the maximum transmissibility), but not the background transmission or flu season length, differs between influenza A and B. Model 3 assumes that the magnitude of seasonal forcing, the background transmission, and flu season length all differ between strains. The models are all optimized using 10 years of surveillance data from 49 of 50 administrative divisions in Israel. Using a cross-validation technique, we compare the relative accuracy of the models and discuss the potential for prediction. We find that accounting for variation in transmission amplitude increases the predictive ability compared to the base. However, little improvement is obtained by allowing for further variation in the shape of the seasonal forcing function
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