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Alpha/beta values in pediatric medulloblastoma: implications for tailored approaches in radiation oncology.
Radiat Oncol
January 2025
Department of Radiotherapy and Radiooncology, Medical Faculty, Heinrich Heine University, Moorenstr. 5, 40225, Dusseldorf, Germany.
Background: Medulloblastoma is the most common malignant pediatric brain tumor, typically treated with normofractionated craniospinal irradiation (CSI) with an additional boost over about 6 weeks in children older than 3 years. This study investigates the sensitivity of pediatric medulloblastoma cell lines to different radiation fractionation schedules. While extensively studied in adult tumors, these ratios remain unknown in pediatric cases due to the rarity of the disease.
View Article and Find Full Text PDFInvestigating BLS instructors' ability to evaluate CPR performance: focus on compression depth, rate, and recoil.
BMC Emerg Med
January 2025
Department of Emergency Medicine, College of Medicine, National Cheng Kung University Hospital, National Cheng Kung University, No.138, Sheng Li Road, Tainan city, 704, Taiwan.
Background: Out-of-hospital cardiac arrest (OHCA) presents significant challenges with low survival rates, emphasizing the need for effective bystander CPR training. In Basic Life Support (BLS) training, the role of instructors is pivotal as they assess and correct learners' cardiopulmonary resuscitation (CPR) techniques to ensure proficiency in life-saving skills. This study evaluates the concordance between CPR quality assessments by Basic Life Support (BLS) instructors and those determined through Quantitative CPR (QCPR) devices, utilizing data from BLS courses conducted at National Cheng Kung University Hospital from October 2017 to April 2018.
View Article and Find Full Text PDFPrediction of mortality in heart failure by machine learning. Comparison with statistical modeling.
Eur J Intern Med
January 2025
Istituti Clinici Scientifici Maugeri, IRCCS, Institute of Bari, Bari, Italy.
Background: Assessing the relative performance of machine learning (ML) methods and conventional statistical methods in predicting prognosis in heart failure (HF) still remains a challenging research field.
Methods: The primary outcome was three-year mortality. The following 5 machine learning approaches were used for modeling: Random Forest (RF), Gradient Boosting, Extreme Gradient Boosting (XGBoost), Support Vector Machine, and Multilayer perceptron.
On QSPR analysis of glaucoma drugs using machine learning with XGBoost and regression models.
Comput Biol Med
January 2025
Department of Mathematics and Statistics, The University of Lahore, Lahore Campus, Pakistan. Electronic address:
Glaucoma is an irreversible, progressive, degenerative eye disorder arising because of increased intraocular pressure, resulting in eventual vision loss if untreated. The QSPR relates, mathematically, by employing various algorithms, a specified property of a molecule that arises either from physical, chemical, or biological phenomena using various aspects of its structure. Here in, a similar application based on topological indices and inferences derived from the structure for the calculation of different drug properties like molar refractivity, refractive index, enthalpy, boiling points, molecular weight, and polarizability is presented.
View Article and Find Full Text PDFA multi-scale information fusion medical image segmentation network based on convolutional kernel coupled updata mechanism.
Comput Biol Med
January 2025
State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, China. Electronic address:
Medical image segmentation is pivotal in disease diagnosis and treatment. This paper presents a novel network architecture for medical image segmentation, termed TransDLNet, which is engineered to enhance the efficiency of multi-scale information utilization. TransDLNet integrates convolutional neural networks and Transformers, facilitating cross-level multi-scale information fusion for complex medical images.
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