Finite element analysis of aortic root dilation: a new procedure to reproduce pathology based on experimental data

Sinotubular junction dilation is one of the most frequent pathologies associated with aortic root incompetence. Hence, we create a finite element model considering the whole root geometry; then, starting from healthy valve models and referring to measures of pathological valves reported in the literature, we reproduce the pathology of the aortic root by imposing appropriate boundary conditions. After evaluating the virtual pathological process, we are able to correlate dimensions of non-functional valves with dimensions of competent valves. Such a relation could be helpful in recreating a competent aortic root and, in particular, it could provide useful information in advance in aortic valve sparing surgery.

International registry on factor XIII deficiency: a basis formed mostly on European data

FXIII deficiency is known as one of the rarest blood coagulation disorders. In this study, the phenotypic and in part genotypic data of 104 FXIII-deficient patients recorded from 1993 - 2005 are presented. The most common bleeding symptoms were subcutaneous bleeding (57%) followed by delayed umbilical cord bleeding (56%), muscle hematoma (49%), hemorrhage after surgery (40%), hemarthrosis (36%), and intracerebral bleeding (34%). Prophylactic treatment was initiated in about 70% of all patients. FXIII-B subunit-deficient patients had a milder phenotype than patients with FXIII-A subunit deficiency. The most frequent mutation affecting the F13A gene was a splice site mutation in intron 5 (IVS5-1G>A). This mutation was found in eight (17%) of 46 analyzed families. The haplotype analysis of patients carrying the IVS5-1A allele was consistent with a founder effect. The international registry (http://www.f13-database.de) will provide clinicians and scientists working on FXIII deficiency with a helpful tool to improve patient care and direct future studies towards better understanding and treatment of the disease.

A simulation study on the statistical monitoring of condemnation rates from slaughterhouses for syndromic surveillance: an evaluation based on Swiss data

Syndromic surveillance (SyS) systems currently exploit various sources of health-related data, most of which are collected for purposes other than surveillance (e.g. economic). Several European SyS systems use data collected during meat inspection for syndromic surveillance of animal health, as some diseases may be more easily detected post-mortem than at their point of origin or during the ante-mortem inspection upon arrival at the slaughterhouse. In this paper we use simulation to evaluate the performance of a quasi-Poisson regression (also known as an improved Farrington) algorithm for the detection of disease outbreaks during post-mortem inspection of slaughtered animals. When parameterizing the algorithm based on the retrospective analyses of 6 years of historic data, the probability of detection was satisfactory for large (range 83-445 cases) outbreaks but poor for small (range 20-177 cases) outbreaks. Varying the amount of historical data used to fit the algorithm can help increasing the probability of detection for small outbreaks. However, while the use of a 0·975 quantile generated a low false-positive rate, in most cases, more than 50% of outbreak cases had already occurred at the time of detection. High variance observed in the whole carcass condemnations time-series, and lack of flexibility in terms of the temporal distribution of simulated outbreaks resulting from low reporting frequency (monthly), constitute major challenges for early detection of outbreaks in the livestock population based on meat inspection data. Reporting frequency should be increased in the future to improve timeliness of the SyS system while increased sensitivity may be achieved by integrating meat inspection data into a multivariate system simultaneously evaluating multiple sources of data on livestock health.