Introduction: Cleft lips and palates are the most common congenital orofacial anomaly. This type of clefts is the most severe from the orthodontic-surgical therapy aspect.
Case Report: A female newborn with a complete cleft of the primary and the secondary palate was admitted to the clinic, where a multiple-role orthodontic device was specially designed and applied to primarily manage the closure of the existing cleft and help to improve the suckling ability of the baby. Besides the fact that it allows breastfeeding, it has a significant orthodontic effect, too.
Conclusion: Specificity of this device is the lack of extraoral fixation. What can easily be observed is a progressive reduction of the cleft between the separated segments and the premaxilla retrusion. It, thus, allows the creation of much better conditions for further surgical management of the said defect.
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http://dx.doi.org/10.2298/vsp1407693r | DOI Listing |
J Craniofac Surg
January 2025
Division of Plastic & Reconstructive Surgery, John H. Stroger Hospital of Cook County, Chicago, IL.
Median craniofacial hypoplasia is characterized by tissue deficiency of the midline facial structures and/or brain. Patients can present with a wide variety of facial differences that may or may not require operative intervention. Common reconstructive procedures include cleft lip and/or palate repair, rhinoplasty, and orthognathic surgery, among others.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Cancer Innovation Laboratory, National Cancer Institute, Frederick, Maryland 21702, United States.
mTOR plays a crucial role in PI3K/AKT/mTOR signaling. We hypothesized that mTOR activation mechanisms driving oncogenesis can advise effective therapeutic designs. To test this, we combined cancer genomic analysis with extensive molecular dynamics simulations of mTOR oncogenic variants.
View Article and Find Full Text PDFClin Trials
January 2025
Liverpool Clinical Trials Centre, University of Liverpool, Liverpool, UK.
Background/aims: When conducting a randomised controlled trial in surgery, it is important to consider surgical learning, where surgeons' familiarity with one, or both, of the interventions increases during the trial. If present, learning may compromise trial validity. We demonstrate a statistical investigation into surgical learning within a trial of cleft palate repair.
View Article and Find Full Text PDFMed Image Comput Comput Assist Interv
October 2024
Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
Recent advancements in Contrastive Language-Image Pre-training (CLIP) [21] have demonstrated notable success in self-supervised representation learning across various tasks. However, the existing CLIP-like approaches often demand extensive GPU resources and prolonged training times due to the considerable size of the model and dataset, making them poor for medical applications, in which large datasets are not always common. Meanwhile, the language model prompts are mainly manually derived from labels tied to images, potentially overlooking the richness of information within training samples.
View Article and Find Full Text PDFCureus
December 2024
Department of Technology and Clinical Trials, Advanced Research, Deerfield Beach, USA.
This paper investigates the potential of artificial intelligence (AI) and machine learning (ML) to enhance the differentiation of cystic lesions in the sellar region, such as pituitary adenomas, Rathke cleft cysts (RCCs) and craniopharyngiomas (CP), through the use of advanced neuroimaging techniques, particularly magnetic resonance imaging (MRI). The goal is to explore how AI-driven models, including convolutional neural networks (CNNs), deep learning, and ensemble methods, can overcome the limitations of traditional diagnostic approaches, providing more accurate and early differentiation of these lesions. The review incorporates findings from critical studies, such as using the Open Access Series of Imaging Studies (OASIS) dataset (Kaggle, San Francisco, USA) for MRI-based brain research, highlighting the significance of statistical rigor and automated segmentation in developing reliable AI models.
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