Purpose: The characteristic features of prematurely fused craniosynostosis in plain radiographs have already been described in literature, but there is no clinical trial investigating the individual features of every single form of craniosynostosis. We described suture-specific characteristics as well as its frequency of appearance in plain radiographs in every different form of craniosynostosis. Intraoperative findings served as control to confirm the diagnosis.
Methods: One hundred twenty-seven children with prematurely fused cranial sutures who underwent a skull X-ray from 2008 to 2012 were investigated in the present study. In detail, 34 children with frontal, 60 with sagittal, 13 with unilateral and 14 with bilateral coronal synostosis and 3 with unilateral lambdoid craniosynostosis as well as 3 children with a bilateral lambdoid synostosis were included.
Results: Typical radiological characteristics in craniosynostosis exist. These features as well as its frequency in craniosynostosis in plain skull radiographs are presented. In all cases, these typical features enabled a correct diagnosis, which was confirmed by intraoperative findings.
Conclusion: The frequency of the appearance of typical features is listed and may serve as a "mental internal check list" in the radiological approach to craniosynostosis. The study points out the value of plain skull X-rays as it enabled proper diagnosis in all investigated 127 cases.
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http://dx.doi.org/10.1007/s00381-015-2890-4 | DOI Listing |
Biochem Biophys Res Commun
December 2024
Université Franche-Comté, INSERM, EFS BFC, UMR1098, Interactions Hôte-Greffon-Tumeur/Ingénierie Cellulaire et Génique, F-25000, Besançon, France. Electronic address:
Nonsense-Mediated mRNA Decay (NMD) is a key control mechanism of RNA quality widely described to target mRNA harbouring Premature Termination Codon (PTC). However, recent studies suggested the existence of non-canonical pathways which remain unresolved. One of these alternative pathways suggested that specific mRNA could be targeted through their 3' UTR (Untranslated Region), which contain various elements involved in mRNA stability regulation.
View Article and Find Full Text PDFJACC Case Rep
November 2024
Division of Cardiology, Tokyo Women's Medical University, Tokyo, Japan.
Marked first-degree atrioventricular block with a PR interval ≥500 ms is rare, leading to unusual P-wave placement. In this case, the P waves immediately after the QRS waves complicated rhythm interpretation. Close attention to P-wave morphology and fused premature ventricular complexes can be important for a proper diagnosis.
View Article and Find Full Text PDFIn the common classification practices, feature selection is an important aspect that highly impacts the computation efficacy of the model, while implementing complex computer vision tasks. The metaheuristic optimization algorithms gain popularity to obtain optimal feature subset. However, the feature selection using metaheuristics suffers from two common stability problems, namely premature convergence and slow convergence rate.
View Article and Find Full Text PDFCraniosynostosis (CS) is the premature fusion of skull sutures, with all sutures except the metopic suture typically fusing in adulthood. Premature fusion constrains brain growth, leading to abnormal skull shape and potential neurocognitive or neurological issues, along with syndromic features in some cases. While CS is rare, its occurrence in siblings is exceptionally uncommon and holds significant academic importance.
View Article and Find Full Text PDFBiomed Phys Eng Express
November 2024
Department of Biomedical Engineering, College of Engineering & Technology, SRM Institute of Science & Technology, Kattankulathur, Tamil Nadu, India.
Retinopathy of Prematurity (ROP) is a retinal disorder affecting preterm babies, which can lead to permanent blindness without treatment. Early-stage ROP diagnosis is vital in providing optimal therapy for the neonates. The proposed study predicts early-stage ROP from neonatal fundus images using Machine Learning (ML) classifiers and Convolutional Neural Networks (CNN) based pre-trained networks.
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