BackgroundTyrosinemia type 1 (TT1) is an autosomal recessive disorder caused by deficiency of the enzyme fumarylacetoacetate hydrolase (FAH). TT1 usually presents in infancy with features suggestive of liver disease or with sepsis-like symptoms.Case presentationWe report two Saudi siblings with TT1. Case 1 was a male infant who presented at 2 months old with fever, vomiting and refusal of feeding. Examination revealed a sick-looking infant with signs of severe dehydration and hypovolemic shock. He was jaundiced, and had hepatomegaly and elevated liver enzymes. Echocardiography was performed in light of a lack of response to inotropes, and revealed biventricular and interventricular septal hypertrophies. The ventricular ejection fraction was 65%. Urine organic acid analysis showed elevated succinylacetone, consistent with a diagnosis of TT1. An FAH gene study identified a c.1 A > G homozygous mutation. This patient responded well to intensive cardiorespiratory therapy, tyrosine-free formula, and oral 2-nitro-4- trifluoromethylbenzyl 1, 3 cyclohexanedione (NTBC). Echocardiographic findings reverted to normal after 4 weeks. Case 2 was the younger brother of Case 1, and was born 6 months after his brother had been confirmed with tyrosinemia. Pregnancy and delivery were uneventful. Serum amino acid and organic acid analyses 4 days after birth confirmed tyrosinemia. DNA analysis identified a c.1 A > G homozygous mutation, as in his brother. Echocardiography was normal. Special formula and NTBC were commenced on day 7 of life. The infant remained asymptomatic after 9 months of follow-up.ConclusionsThese cases highlight TT1 as a treatable cause of cardiomyopathy in children. It also supports the idea that early diagnosis and treatment may prevent the development of cardiomyopathy associated with tyrosinemia.
Anomalies of systemic venous return are extremely heterogeneous congenital malformations with variable ranges from completely normal physiology to severe forms of right to left shunting requiring surgical treatment. Anomalous drainage of a right-sided superior vena cava (SVC) to the left atrium (LA) is one of the rarest variants of systemic venous return anomalies, characterized by right-to-left shunt physiology and cyanosis. Here we report a 2 years old girl presented with cyanosis which was observed shortly after birth by her parents but not further investigated. She is otherwise active girl and with normal growth and development. Her clinical examination was unremarkable apart from mild clubbing of the fingers and low oxygen saturation of 88-90% in room air. Her ECG and chest X-ray were unremarkable. Echocardiography showed bilateral SVC connected by a small innominate vein. The right SVC drains directly into the LA while the left SVC drains into the right atrium (RA) via a dilated coronary sinus. There is a small superior sinus venosus type atrial septum defect (ASD) with left to right shunt. Also, there is partial anomalous pulmonary venous return with right upper and right middle pulmonary veins draining directly into the right SVC, which is connected to LA. The right lower pulmonary vein and left pulmonary veins drain directly to LA. The rest of her echocardiography demonstrated normal heart structures and function. This patient was referred for surgical correction, including baffling of the right SVC to the RA and closure of the ASD. We describe this case to highlight the importance of recognizing this rare anomalous systemic venous connection as one of the very rare causes of cyanosis in the pediatric age group as well as at older age.
Limb girdle muscular dystrophy type 2 (LGMD2) is a genetically heterogeneous autosomal recessive disorder caused by mutations in 15 known genes. DNA sequencing of all candidate genes can be expensive and laborious, whereas a selective sequencing approach often fails to provide a molecular diagnosis. We aimed to efficiently identify pathogenic mutations via homozygosity mapping in a population in which the genetics of LGMD2 has not been well characterized. Thirteen consanguineous families containing a proband with LGMD2 were recruited from Saudi Arabia, and for 11 of these families, selected individuals were genotyped at 10,204 single nucleotide polymorphisms. Linkage analysis excluded all but one or two known genes in ten of 11 genotyped families, and haplotype comparisons between families allowed further reduction in the number of candidate genes that were screened. Mutations were identified by DNA sequencing in all 13 families, including five novel mutations in four genes, by sequencing at most two genes per family. One family was reclassified as having a different myopathy based on genetic and clinical data after linkage analysis excluded all known LGMD2 genes. LGMD2 subtypes A and B were notably absent from our sample of patients, indicating that the distribution of LGMD2 mutations in Saudi Arabian families may be different than in other populations. Our data demonstrate that homozygosity mapping in consanguineous pedigrees offers a more efficient means of discovering mutations that cause heterogeneous disorders than comprehensive sequencing of known candidate genes.Electronic supplementary materialThe online version of this article (doi:10.1007/s10048-010-0250-9) contains supplementary material, which is available to authorized users.
This paper illustrates how to use data mining techniques to help in advising students and predicting their academic performance. Data mining is used to get previously unknown, hidden and perhaps vital knowledge from a large amount of data. It combines domain knowledge, advanced analytical skills, and a vast knowledge base to reveal hidden patterns and trends that are applicable in virtually any sector ranging from engineering to medicine, to business. However, it is possible for educational institutes to use data mining to find useful information from their databases. This is usually called Educational Data Mining (EDM). Advancing the field of EDM with new data analysis techniques and new machine learning algorithms is vital. Classification and clustering techniques will be used in this project to study and analyse student performance. The key importance of this project is that it discusses different data mining techniques in the literature review to study student behaviour depending upon their performance. We tried to identify the most suitable algorithms from the existing research methods to predict the success of students. Various data mining approaches were discussed and their results were evaluated. In this paper, the J48 algorithm was applied to the data set, gathered from Umm Al-Qura University in Makkah.
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