98%
921
2 minutes
20
Introduction: Hidden or submucosal cleft palate (SCP) is a rare form of isolated cleft that is characterized by rhinolalia in the presence of an apparently intact palate.
Objective: To analyze the age of detection of SCP in children and evaluate the impact of its presence on the middle ear and speech development of patients.
Material And Methods: 17 patients with SCP were examined and treated in the departments of otorhinolaryngology and maxillofacial surgery of the Children's Republican Clinical Hospital of the Ministry of Health of the Republic of Tatarstan. The duration of observation of patients ranged from 1 to 4 years.
Results: The median (Me) age of diagnosis of SCP was 7.18±3.83 years (95% CI 5.21-9.14), of which in 10 patients (58.8%) SCP was first suspected by an otorhinolaryngologist. When analyzing the age of detection of SCP depending on the detection of otitis media with effusion (OME) and epitympanitis, we established statistically significant differences (=0.007; =0.043, respectively). When assessing the dependence of the probability of OME on the age of detection of SCP using ROC analysis, an ROC curve was obtained that characterizes the dependence of the probability of OME on the age of detection of SCP. The resulting model was statistically significant for OME (=0.011). When analyzing the comparison of the age of detection of SCP depending on the presence of speech delay, statistically significant differences were established (=0.029).
Conclusions: The analyzed data from patients with SCP indicate serious changes in the middle ear: from OME to the presence of cholesteatoma. The presence of SCP affects not only the clarity of spoken speech, but also contributes to its delay. The data obtained demonstrate a direct relationship between the severity of manifestations in the middle ear and the age at which the diagnosis of SCP was made by an otorhinolaryngologist.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.17116/otorino2025900416 | DOI Listing |
Behav Res Methods
September 2025
Czech Technical University in Prague, Faculty of Electrical Engineering, Department of Cybernetics, Prague, Czech Republic.
Automatic markerless estimation of infant posture and motion from ordinary videos carries great potential for movement studies "in the wild", facilitating understanding of motor development and massively increasing the chances of early diagnosis of disorders. There has been a rapid development of human pose estimation methods in computer vision, thanks to advances in deep learning and machine learning. However, these methods are trained on datasets that feature adults in different contexts.
View Article and Find Full Text PDFGeroscience
September 2025
Department of Urology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
This study aims to investigate the predictive value of combined phenotypic age and phenotypic age acceleration (PhenoAgeAccel) for benign prostatic hyperplasia (BPH) and develop a machine learning-based risk prediction model to inform precision prevention and clinical management strategies. The study analyzed data from 784 male participants in the US National Health and Nutrition Examination Survey (NHANES, 2001-2008). Phenotypic age was derived from chronological age and nine serum biomarkers.
View Article and Find Full Text PDFClin Rheumatol
September 2025
Division of Rheumatology, Department of Internal Medicine, Mayo Clinic, 200 First St SW, Rochester, MN, 55906, USA.
Objectives: IgG4-related disease (IgG4-RD) can affect multiple organ systems, with coronary artery involvement being rare. Coronary periarteritis may lead to complications such as myocardial infarction and ischemic cardiomyopathy. This case series characterizes the clinical and radiological features, complications, and treatment strategies in patients with IgG4-RD-associated coronary periarteritis.
View Article and Find Full Text PDFJ Imaging Inform Med
September 2025
Department of Biomedical Engineering, Gachon University, Seongnam-Si 13120, Gyeonggi-Do, Republic of Korea.
To develop and validate a deep-learning-based algorithm for automatic identification of anatomical landmarks and calculating femoral and tibial version angles (FTT angles) on lower-extremity CT scans. In this IRB-approved, retrospective study, lower-extremity CT scans from 270 adult patients (median age, 69 years; female to male ratio, 235:35) were analyzed. CT data were preprocessed using contrast-limited adaptive histogram equalization and RGB superposition to enhance tissue boundary distinction.
View Article and Find Full Text PDFEur J Orthop Surg Traumatol
September 2025
Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden.
Background: To analyze penetrating extremity injuries at a Scandinavian urban Level-1 trauma center regarding incidence, mechanism of injury, imaging approach and clinical outcome.
Methods: A retrospective study (2013-2016) of penetrating injuries to the extremities based on a Trauma Registry. Retrieved variables included patient demographics, injury characteristics, time to CT and 30-day morbidity.