Objective: To identify the frequency of disordered eating (DE) and unhealthy weight control behaviors (UWCB) among adolescents and associations with age, sex, actual weight status, perceived weight status, and body image dissatisfaction.
Methods: Cross-sectional study of 1,156 adolescents. DE was assessed using a specific self-report questionnaire, UWCB by specific behaviors that were not typically recommended for weight management, and body dissatisfaction by Stunkard's silhouettes.
Results: The frequency of DE was 17.3%, and that of UWCB, 31.9%; 80.1% of participants were dissatisfied with body image. Perception of oneself as overweight was associated with 1.795-fold odds of DE. Those with UWCB had 7.389-fold odds of DE, while DE increased the odds of UWCB 7.280-fold. Girls, participants who perceived themselves as overweight, and those who reported body dissatisfaction were 2.266, 2.381, and 1.752 times more likely to have UWCB, respectively.
Conclusion: A high prevalence of UWCB and a moderate prevalence of DE behaviors was found in adolescents from the city of São Paulo, Brazil. Those who perceived themselves as overweight had more DE and UWCB, and both behaviors were related. UWCB was more common in girls and among those dissatisfied with their bodies.
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http://dx.doi.org/10.1590/1516-4446-2019-0437 | DOI Listing |
Med Biol Eng Comput
January 2025
Faculty of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel.
Positron emission tomography (PET) imaging plays a pivotal role in oncology for the early detection of metastatic tumors and response to therapy assessment due to its high sensitivity compared to anatomical imaging modalities. The balance between image quality and radiation exposure is critical, as reducing the administered dose results in a lower signal-to-noise ratio (SNR) and information loss, which may significantly affect clinical diagnosis. Deep learning (DL) algorithms have recently made significant progress in low-dose (LD) PET reconstruction.
View Article and Find Full Text PDFEur J Nucl Med Mol Imaging
January 2025
Department of Nuclear Medicine, School of Medicine, Technical University of Munich, Munich, Germany.
Purpose: This retrospective analysis evaluates baseline F-flotufolastat positron emission tomography (PET) parameters as prognostic parameters for treatment response and outcome in patients with metastatic castration-resistant prostate cancer (mCRPC) undergoing treatment with [Lu]Lu-PSMA-I&T.
Methods: A total of 188 mCRPC patients with baseline F-flotufolastat PET scans were included. Tumor lesions were semiautomatically delineated, with imaging parameters including volume-based and standardized uptake value (SUV)-based metrics.
J Funct Morphol Kinesiol
January 2025
Department of Physical Medicine and Rehabilitation, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan.
Pars fractures are a common cause of lower back pain, especially among young individuals. Although computed tomography (CT) and magnetic resonance imaging (MRI) scanning are commonly used in developed regions, traditional radiography remains the main diagnostic method in many developing countries. This study assessed whether the standard radiographic angles suggested in textbooks are optimal for an Asian population since Asian groups have lower lumbar lordosis.
View Article and Find Full Text PDFBackground And Aims: Pectus carinatum (PC) is the second most common deformity of the anterior chest wall, resulting in detrimental effects on body image and quality of life. This study evaluated the safety, effectiveness, and factors associated with the treatment of PC using a sandwiched bar and screw fixation system, first performed in Vietnam at the University Medical Center Ho Chi Minh City in 2016.
Methods: This retrospective cohort study was conducted from March 2016 to February 2023 in patients with PC and PC-mixed pectus excavatum (PE) deformities.
Front Robot AI
January 2025
Department of Materials and Production, Aalborg University, Aalborg, Denmark.
Object pose estimation is essential for computer vision applications such as quality inspection, robotic bin picking, and warehouse logistics. However, this task often requires expensive equipment such as 3D cameras or Lidar sensors, as well as significant computational resources. Many state-of-the-art methods for 6D pose estimation depend on deep neural networks, which are computationally demanding and require GPUs for real-time performance.
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