Myelomeningoceles (MMCs) represent a localized failure of primary neurulation during the fourth week of embryonic development. There are a number of misconceptions concerning the proper identification, classification, and surgical repair of these lesions. To provide surgeons with a working knowledge of early neural embryology as it relates to MMC closure as a localized failure of primary neurulation. We review the embryology of early neural development as a means of providing neurosurgeons with a better understanding of MMC closure techniques. Early neural development predicts the anatomy of MMC and knowledge of embryology helps guide repair. Repair of MMC is enhanced by knowing early neural development.
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http://dx.doi.org/10.7759/cureus.9682 | DOI Listing |
Dev Sci
March 2025
Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, Vienna, Austria.
Newborns are able to neurally discriminate between speech and nonspeech right after birth. To date it remains unknown whether this early speech discrimination and the underlying neural language network is associated with later language development. Preterm-born children are an interesting cohort to investigate this relationship, as previous studies have shown that preterm-born neonates exhibit alterations of speech processing and have a greater risk of later language deficits.
View Article and Find Full Text PDFJ Transl Med
January 2025
Department of Neurosurgery, The Second Affiliated Hospital of Xi'an Jiao Tong University, Xi'an, China.
Background: Spinal cord injury (SCI) triggers a complex inflammatory response that impedes neural repair and functional recovery. The modulation of macrophage phenotypes is thus considered a promising therapeutic strategy to mitigate inflammation and promote regeneration.
Methods: We employed microarray and single-cell RNA sequencing (scRNA-seq) to investigate gene expression changes and immune cell dynamics in mice following crush injury at 3 and 7 days post-injury (dpi).
BMC Cancer
January 2025
Department of Data Science, Faculty of Interdisciplinary Science and Technology, Tarbiat Modares University, Tehran, Iran.
Background: Melanoma is a highly aggressive skin cancer, where early and accurate diagnosis is crucial to improve patient outcomes. Dermoscopy, a non-invasive imaging technique, aids in melanoma detection but can be limited by subjective interpretation. Recently, machine learning and deep learning techniques have shown promise in enhancing diagnostic precision by automating the analysis of dermoscopy images.
View Article and Find Full Text PDFSci Rep
January 2025
Institute of Atmospheric Physics, LAGEO, Chinese Academy of Sciences, Beijing, China.
Quickly identifying and classifying lightning waveforms is the foundation of lightning forecasting and early warning. In this paper, based on the electric field observation of the Beijing lightning location website of the Institute of Atmospheric Physics, Chinese Academy of Sciences, a recognition and classification method of pulse signal waveform based on Convolutional Neural Network(CNN) algorithm is designed and implemented. The CNN network model and its parameters were optimized from three aspects: dataset, model parameters, and network structure, achieving a recognition rate of over 90%.
View Article and Find Full Text PDFNeuroscience
January 2025
Department of Computer Engineering, Faculty of Engineering, Igdir University, 76000, Igdir, Turkey. Electronic address:
Neurological disorders, including cerebral vascular occlusions and strokes, present a major global health challenge due to their high mortality rates and long-term disabilities. Early diagnosis, particularly within the first hours, is crucial for preventing irreversible damage and improving patient outcomes. Although neuroimaging techniques like magnetic resonance imaging (MRI) have advanced significantly, traditional methods often fail to fully capture the complexity of brain lesions.
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