ALG8-CDG: novel patients and review of the literature

Contains fulltext : 153189.pdf (publisher's version ) (Open Access)BACKGROUND: Since 1980, about 100 types of congenital disorders of glycosylation (CDG) have been reported representing an expanding group of inherited disorders. ALG8-CDG (= CDG-Ih) is one of the less frequently reported types of CDG, maybe due to its severe multi-organ involvement with coagulation disturbances, edema, massive gastrointestinal protein loosing enteropathy, cataracts, and often early death. We report three additional patients, provide an update on two previously reported, and summarize features of ten patients reported in literature. RESULTS: Of 15 ALG8-CDG patients, three were homozygous and 12 compound heterozygous. There were multiple prenatal abnormalities in 6/12 patients. In 13/15, there were symptoms at birth, 9/15 died within 12 months. Birth weight was appropriate in 11/12, only one was small for gestational age. Prematurity was reported in 7/12. Hydrops fetalis was noticed in 3, edemas in 11/13; gastrointestinal symptoms in 9/14; structural brain pathology, psychomental retardation, seizures, ataxia in 12/13, muscle hypotonia in 13/14. Common dysmorphic signs were: low set ears, macroglossia, hypertelorism, pes equinovarus, campto- and brachydactyly (13/15). In 10/11, there was coagulopathy, in 8/11 elevated transaminases; thrombocytopenia was present in 9/9. Eye involvement was reported in 9/14. CDG typical skin involvement was reported in 8/13. CONCLUSION: In ALG8-CDG, isoelectric focusing of transferrin in serum or plasma shows an abnormal sialotransferrin pattern. The diagnosis is confirmed by mutation analysis in ALG8; all patients reported so far had point mutations or small deletions. The prognosis is generally poor. Thus, a timely and correct diagnosis is important for counselling

Deep convolutional neural networks for automatic coronary calcium scoring in a screening study with low-dose chest CT

The amount of calcifications in the coronary arteries is a powerful and independent predictor of cardiovascular events and is used to identify subjects at high risk who might benefit from preventive treatment. Routine quantification of coronary calcium scores can complement screening programs using low-dose chest CT, such as lung cancer screening. We present a system for automatic coronary calcium scoring based on deep convolutional neural networks (CNNs). The system uses three independently trained CNNs to estimate a bounding box around the heart. In this region of interest, connected components above 130 HU are considered candidates for coronary artery calcifications. To separate them from other high intensity lesions, classification of all extracted voxels is performed by feeding two-dimensional 50 mm × 50 mm patches from three orthogonal planes into three concurrent CNNs. The networks consist of three convolutional layers and one fully-connected layer with 256 neurons. In the experiments, 1028 non-contrast-enhanced and non-ECG-triggered low-dose chest CT scans were used. The network was trained on 797 scans. In the remaining 231 test scans, the method detected on average 194.3 mm3 of 199.8 mm3 coronary calcifications per scan (sensitivity 97.2 %) with an average false-positive volume of 10.3 mm3. Subjects were assigned to one of five standard cardiovascular risk categories based on the Agatston score. Accuracy of risk category assignment was 84.4 % with a linearly weighted κ of 0.89. The proposed system can perform automatic coronary artery calcium scoring to identify subjects undergoing low-dose chest CT screening who are at risk of cardiovascular events with high accuracy