Towards Genetic Prediction of Coronary Heart Disease in Familial Hypercholesterolemia

Familial hypercholesterolemia (FH) is an autosomal dominant disorder of lipid metabolism caused by mutations in the gene coding for the low-density lipoprotein (LDL) receptor. The LDL receptor is a transmembrane protein that regulates plasma cholesterol levels by uptake of LDL particles from the blood circulation (Figure). Mutations in the LDL receptor gene cause insufficient uptake of circulating LDL particles, which raises the endogenous cholesterol production by the hepatocytes, resulting in twofold increased plasma concentrations of LDL cholesterol in patients with the heterozygous form of FH. The rare (1/million) homozygous FH patients have severely reduced or completely absent residual function of the LDL receptor causing extremely raised plasma LDL cholesterol concentrations. These patients develop tendon xanthomas in childhood and massive atherosclerosis occurs frequently at a very young age. This thesis, however, focuses on patients with heterozygous FH, which is more common with a prevalence of 1/500 in Western societies. The typical heterozygous FH patients develop tendon xanthomas and have accelerated atherosclerosis and coronary heart disease (CHD) at a young age. Nevertheless, substantial variation is seen in the age of onset of CHD among patients with heterozygous FH

Венчурні інвестиції: сутність, форми, контрагенти

У статті досліджено генезис категорії "венчурні інвестиції", еволюцію форм організації венчурних інвестицій, конкретизовано специфіку інвесторів і реципієнтів венчурного капіталу

Value of genetic profiling for the prediction of coronary heart disease

BACKGROUND: Advances in high-throughput genomics facilitate the identification of novel genetic susceptibility variants for coronary heart disease (CHD). This may improve CHD risk prediction. The aim of the present simulation study was to investigate to what degree CHD risk can be predicted by testing multiple genetic variants (genetic profiling). METHODS: We simulated genetic profiles for a population of 100,000 individuals with a 10-year CHD incidence of 10%. For each combination of model parameters (number of variants, genotype frequency and odds ratio [OR]), we calculated the area under the receiver operating characteristic curve (AUC) to indicate the discrimination between individuals who will and will not develop CHD. RESULTS: The AUC of genetic profiles could rise to 0.90 when 100 hypothetical variants with ORs of 1.5 and genotype frequencies of 50% were simulated. The AUC of a genetic profile consisting of 10 established variants, with ORs ranging from 1.13 to 1.42, was 0.59. When 2, 5, and 10 times as many identical variant

Een filosofie van het meedenken als antwoord op fundamentele vraagstukken van hedendaagse Westerse samenlevingen

Contains fulltext : 101089.pdf (Publisher’s version ) (Open Access)Radboud Universiteit Nijmegen, 07 november 2012Promotor : Wils, J.P

Emergency Medical Service response and mission times in an African metropolitan setting

Background: Emergency Medical Services (EMS) aim to respond to emergencies, treat and transport patients efficiently thus ensuring the ambulance call or “mission” is completed with ambulances available to service the next call as soon as possible. A typical mission may be divided into activities, each linked to a set time interval. The response time interval starts from the time a call is received by the call centre until the ambulance arrives on scene. The patient care interval includes the time taken to treat and transport the patient to hospital. The total mission time can be viewed as the time from when a call is first received by the call centre until the ambulance dispatched to that incident is again available to service the next call. The aim of this study was to describe response interval, patient care interval and total mission times routinely associated with servicing emergency incidents within a metropolitan public sector EMS in South Africa. Methods: A quantitative, prospective, descriptive design was followed wherein time intervals associated with 784 missions were analysed to document and describe response time interval, patient care interval and total mission times. Results: On average crews took 0h 23:16 to respond to incidents before spending an additional 0h 43:20 treating and transporting their patients. Lengthy delays were noted between arrival at hospital andcrews booking available for the next call. This led to total mission times averaging 2h 11:00. Conclusion: Average response and patient care time intervals noted in our study were longer than national and international norms and standards. Delays between arrival at hospital and crews booking available to service the next call led to average mission times of over 2 hours. This negatively impacts on availability of ambulances. Further studies are recommended to explore factors that may be contributing to the lengthy response and mission times reported in this study