Premature return to play for the concussed pediatric athlete may result in devastating neurological injury. Identification of at-risk patients and ideal management of the concussed athlete remain challenging for the pediatrician. The authors review a case of second impact syndrome in which neuroimaging was obtained between the first and second impacts, a circumstance which to their knowledge has not been previously reported. This case offers new insights into the underlying pathophysiology of this disease process and potential risk factors for its development.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.3171/2012.11.PEDS12343 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
The role of myocardial energy metabolism perturbations in diabetic cardiomyopathy: from the perspective of novel protein post-translational modifications.
Clin Epigenetics
January 2025
Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China.
Diabetic cardiomyopathy (DbCM), a significant chronic complication of diabetes, manifests as myocardial hypertrophy, fibrosis, and other pathological alterations that substantially impact cardiac function and elevate the risk of cardiovascular diseases and patient mortality. Myocardial energy metabolism disturbances in DbCM, encompassing glucose, fatty acid, ketone body and lactate metabolism, are crucial factors that contribute to the progression of DbCM. In recent years, novel protein post-translational modifications (PTMs) such as lactylation, β-hydroxybutyrylation, and succinylation have been demonstrated to be intimately associated with the myocardial energy metabolism process, and in conjunction with acetylation, they participate in the regulation of protein activity and gene expression activity in cardiomyocytes.
View Article and Find Full Text PDFSleep characteristics and intervertebral disc degeneration risk: an observational and Mendelian randomization study.
Eur Spine J
January 2025
Department of Orthopedics, the Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518000, Guangdong, China.
Objectives: Sleep disorders are considered a risk factor for aging and skeletal degeneration, but their impact on intervertebral disc degeneration (IDD) remains unclear. The aim of this study was to assess associations between sleep characteristics and IDD, and to identify potential causal relationships.
Methods: Exposure factors included six unhealthy sleep characteristics: insomnia, short sleep duration (< 7 h), long sleep duration (≥ 9 h), evening chronotype, daytime sleepiness, and snoring.
Colon cancer poses a significant threat to global health, and studies have shown a correlation between physical activity (PA) and the incidence of colon cancer. However, existing research has not quantitatively analyzed PA to evaluate its impact on the risk of colon cancer comprehensively. Data related to the study were obtained from the NHANES database for participants aged 20 and above between 2007 and 2018.
View Article and Find Full Text PDFAccuracy of full arch scans performed with nine different scanning patterns- an in vitro study.
Clin Oral Investig
January 2025
Department of Prosthetic Dentistry, LMU University Hospital, LMU Munich, Goethestrasse 70, 80336, Munich, Germany.
Objective: Evaluation of the accuracy of direct digitization of maxillary scans depending on the scanning strategy.
Materials And Methods: A maxillary model with a metal bar as a reference structure fixed between the second molars was digitized using the CEREC Primescan AC scanner (N = 225 scans). Nine scanning strategies were selected (n = 25 scans per strategy), differing in scan area segmentation (F = full jaw, H = half jaw, S = sextant) and scan movement pattern (L = linear, Z = zig-zag, C = combined).
Interpretable Machine Learning Model for Predicting Postpartum Depression: Retrospective Study.
JMIR Med Inform
January 2025
Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China.
Background: Postpartum depression (PPD) is a prevalent mental health issue with significant impacts on mothers and families. Exploring reliable predictors is crucial for the early and accurate prediction of PPD, which remains challenging.
Objective: This study aimed to comprehensively collect variables from multiple aspects, develop and validate machine learning models to achieve precise prediction of PPD, and interpret the model to reveal clinical implications.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!