[Influence of lipid-lowering drugs on homocysteine levels].
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Adolescent↗
Biomedical subjects
T Boudemaghe
Publications and source records attributed to T Boudemaghe.
[Should the insulin resistance associated with hepatic iron overload be researched during diabetes mellitus type II?].
The insulin resistance-associated hepatic iron overload is the first aetiology of iron overload disorders in France. If we do not know its mechanism, the prevalence among type II diabetic patients is around 40%. Hyperferritinaemia is present in all cases, but is not specific of the diagnosis. This pathology features liver fibrosis among 10% of the patients and some cases of primary liver cancer have been described. Moreover, a large body of evidence favors the direct involvement of iron in the development of extra hepatic neoplasia, while therapeutic phlebotomy to maintain low to normal body iron stores can prevent all known complications of insulin resistance-associated hepatic iron overload. In addition, treatment of type II diabetes mellitus and other features of insulin resistance syndrome is essential. In conclusion, it is important to detect this syndrome during type II diabetes mellitus.
Diabetes Mellitus, Type 2↗
[Modeling asthma evolution by a multi-state model].
BACKGROUND: There are many scores for the evaluation of asthma. However, most do not take into account the evolutionary aspects of this illness. We propose a model for the clinical course of asthma by a homogeneous Markov model process based on data provided by the A.R.I.A. (Association de Recherche en Intelligence Artificielle dans le cadre de l'asthme et des maladies respiratoires). METHODS: The criterion used is the activity of the illness during the month before consultation. The activity is divided into three levels: light (state 1), mild (state 2) and severe (state 3). The model allows the evaluation of the strength of transition between states. RESULTS: We found that strong intensities were implicated towards state 2 (lambda(12) and lambda(32)), less towards state 1 (lambda(21) and lambda(31)), and minimum towards state 3 (lambda(23)). This results in an equilibrium distribution essentially divided between state 1 and 2 (44.6% and 51.0% respectively) with a small proportion in state 3 (4.4%). CONCLUSIONS: In the future, the increasing amount of available data should permit the introduction of covariables, the distinction of subgroups and the implementation of clinical studies. The interest of this model falls within the domain of the quantification of the illness as well as the representation allowed thereof, while offering a formal framework for the clinical notion of time and evolution.
Artificial Intelligence↗