Search Results (2)

Skeletal dysplasias (SDs) are a large, heterogeneous group of mostly genetic disorders that affect the bones and cartilage, resulting in abnormal growth and development of skeletal structures. The high clinical and genetic diversity in SDs cause difficulties in prenatal diagnosis. To establish a correct prognosis and better management, it is very important to distinguish SDs with poor life-limiting prognosis or lethal SDs from other ones. Bad prognosis in foetuses is assessed on the basis of the size of the thorax, lung volumes, long bones’ length, bones’ echogenicity, bones’ angulation or presented fractures, and the concomitant presence of non-immune hydrops or visceral abnormalities. To confirm SD diagnosis and perform family genetic consultation, rapid molecular diagnostics are needed; therefore, the NGS method using a panel of genes corresponding to SD or whole-exome sequencing (WES) is commonly used. We report a case of a foetus showing long bones’ shortening and a narrow chest with short ribs, diagnosed prenatally with asphyxiating thoracic dystrophy, also known as Jeune syndrome (ATD; OMIM 208500), caused by compound heterozygous variants in the DYNC2H1 gene, identified by prenatally performed rapid-WES analysis. The missense variants in the DYNC2H1 gene were inherited from the mother (c.7289T>C; p.Ile2430Thr) and from the father (c.12716T>G; p.Leu4239Arg). The DYNC2H1 gene is one of at least 17 ATD-associated genes. This disorder belongs to the ninth group of SD, ciliopathies with major skeletal involvement. An extremely narrow, bell-shaped chest, and abnormalities of the kidneys, liver, and retinas were observed in most cases of ATD. Next to lethal and severe forms, clinically mild forms have also been reported. A diagnosis of ATD is important to establish the prognosis and management for the patient, as well as the recurrence risk for the family. Full article

The main goal of this study is to estimate the pullout forces by developing various modelling technique like feedforward neural network (FFNN), radial basis functions neural networks (RBNN), general regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS). A hybrid learning algorithm, including a back-propagation and least square estimation, is utilized to train ANFIS in MATLAB (software). Accordingly, 432 samples have been applied, through which 300 samples have been considered as training dataset with 132 ones for testing dataset. All results have been analyzed by ANFIS, in which the reliability has been confirmed through the comparing of the results. Consequently, regarding FFNN, RBNN, GRNN, and ANFIS, statistical indexes of coefficient of determination (R²), variance account for (VAF) and root mean square error (RMSE) in the values of (0.957, 0.968, 0.939, 0.902, 0.998), (95.677, 96.814, 93.884, 90.131, 97.442) and (2.176, 1.608, 3.001, 4.39, 0.058) have been achieved for training datasets and the values of (0.951, 0.913, 0.729, 0.685 and 0.995), (95.04, 91.13, 72.745, 66.228, 96.247) and (2.433, 4.032, 8.005, 10.188 and 1.252) are for testing datasets indicating a satisfied reliability of ANFIS in estimating of pullout behavior of belled piles. Full article