Lafora Disease during a Seven-Year Period, Bosnian and Herzegovinian experience

Abstract:Lafora progressive myoclonus epilepsy (Lafora disease, LD) is a fatal autosomal recessive neurodegenerative disorder (with an onset in teenage years in previously normal adolescents). This paper represents a view of a patient diagnosed with Lafora progressive myoclonus epilepsy, over a course of seven years. A description of the initial manifestation of symptoms, doctors' attempts to combat the symptoms with drug treatment, further attempts towards reaching the correct diagnosis, the final confirmation of the Lafora diagnosis (mutation in the NHLRC1 gene), and the current state of the patient is presented. The absence of a positive family history, the lack of staff specialized in dealing with this or similar pathology, and the diagnostic inability to characterize this type of disorder in Bosnia and Herzegovina have led to a fair delay in diagnosing and beginning of an adequate pharmacological treatment. Overall, recent identification of LD cases in Bosnia and Herzegovina warrants an establishment of a Centre for Genetic Testing in order to ensure more humane counseling of an entire family whose family member(s) might be diagnosed with this devastating and currently an incurable disorder.Keywords: Progressive myoclonus epilepsy; Lafora disease; NHLRC1 mutatio

FROM HEART MURMUR TO ECHOCARDIOGRAPHY CONGENITAL HEART DEFECTS DIAGNOSTICS USING MACHINELEARNING ALGORITHMS

The most common clinical sign in pediatric cardiology is heart murmur, which can often be uncharacteristic. The aim of this research was to present the results of development of a classifier based on machine learning algorithms whose purpose is to classify organic murmur that occur in congenital heart defect (CHD). The study is based on the data collected at Pediatric Clinic, Clinical Center University of Sarajevo during three-year period. Totally, 116 children aged from 1 to 15 years were enrolled in the study. Input parameters for classification are parameters obtained during basic physical examination and assessment of patient. First, analysis of relevance of the feature for classification was done using InfoGain, GainRatio, Relief and Correlation method. In the second step, classifiers based on Naive Bayes, Logistic Regression, Decision Tree, Random Forest and Support Vector Machine were developed and compared by performance. The results of this research suggest that high accuracy (>90%) classifier for detection of CHD based on 16 parameters can be developed. Such classifier with appropriate user interface would be valuable diagnostic aid to doctors and pediatricians at primary healthcare level for diagnostic of heart murmurs

FROM HEART MURMUR TO ECHOCARDIOGRAPHY CONGENITAL HEART DEFECTS DIAGNOSTICS USING MACHINELEARNING ALGORITHMS

The most common clinical sign in pediatric cardiology is heart murmur, which can often be uncharacteristic. The aim of this research was to present the results of development of a classifier based on machine learning algorithms whose purpose is to classify organic murmur that occur in congenital heart defect (CHD). The study is based on the data collected at Pediatric Clinic, Clinical Center University of Sarajevo during three-year period. Totally, 116 children aged from 1 to 15 years were enrolled in the study. Input parameters for classification are parameters obtained during basic physical examination and assessment of patient. First, analysis of relevance of the feature for classification was done using InfoGain, GainRatio, Relief and Correlation method. In the second step, classifiers based on Naive Bayes, Logistic Regression, Decision Tree, Random Forest and Support Vector Machine were developed and compared by performance. The results of this research suggest that high accuracy (>90%) classifier for detection of CHD based on 16 parameters can be developed. Such classifier with appropriate user interface would be valuable diagnostic aid to doctors and pediatricians at primary healthcare level for diagnostic of heart murmurs