This work was conducted to evaluate the effect of early intervention with epoetin alfa (EPO) on transfusion requirements, hemoglobin level (Hb), quality of life (QOL) and to explore a possible relationship between the use of EPO and survival, in patients with solid tumors receiving platinum-based chemotherapy. Three hundred and sixteen patients with Hb12.1g/dL were randomised 2:1 to EPO 10000 IU thrice weekly subcutaneously (n = 211) or best supportive care (BSC) (n = 105). The primary end point was proportion of patients transfused while secondary end points were changes in Hb and QOL. The protocol was amended before the first patient was recruited to also prospectively collect survival data. EPO therapy significantly decreased transfusion requirements (P < 0.001) and increased Hb (P < 0.005). EPO-treated patients had significantly improved QOL compared with BSC patients (P < 0.05). Kaplan-Meier estimates showed no differences in 12-month survival (P = 0.39), despite a significantly greater number of patients with metastatic disease in the EPO group (78% vs. 61%, P = 0.001). EPO was well tolerated. This study has shown that early intervention with EPO can result in a significant reduction of transfusion requirements and increases in Hb and QOL in patients with mild anemia during platinum-based chemotherapy.
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http://dx.doi.org/10.1016/j.ejca.2005.03.024 | DOI Listing |
Infant Behav Dev
January 2025
Department of Psychology, Arizona State University, USA.
Background: Early intervention is effective for reducing ADHD symptoms and related impairments, yet methods of identifying young children in need of services are lacking. Most early predictors of ADHD previously identified are of limited clinical utility. This study examines several theoretically relevant predictors of ADHD in infancy and toddlerhood and whether assessment at multiple time points improves prediction.
View Article and Find Full Text PDFBackground: Noise-induced hearing loss (NIHL) in children is a growing public health concern due to increased exposure to high noise levels in various environments. Early intervention is crucial to prevent long-term developmental and social impacts. This study evaluates the effectiveness of earmuffs as a protective intervention in managing NIHL in children.
View Article and Find Full Text PDFJ Craniofac Surg
October 2024
Department of Biomedical and Surgical and Biomedical Sciences, Catania University, Catania, Italy.
Background: With the use of machine learning algorithms, artificial intelligence (AI) has become a viable diagnostic and treatment tool for oral cancer. AI can assess a variety of information, including histopathology slides and intraoral pictures.
Aim: The purpose of this systematic review is to evaluate the efficacy and accuracy of AI technology in the detection and diagnosis of oral cancer between 2020 and 2024.
PLoS Pathog
January 2025
Strategic Area: Protecting Crops and the Environment, Rothamsted Research, Harpenden, Hertfordshire, United Kingdom.
Filamentous plant pathogenic fungi pose significant threats to global food security, particularly through diseases like Fusarium Head Blight (FHB) and Septoria Tritici Blotch (STB) which affects cereals. With mounting challenges in fungal control and increasing restrictions on fungicide use due to environmental concerns, there is an urgent need for innovative control strategies. Here, we present a comprehensive analysis of the stage-specific infection process of Fusarium graminearum in wheat spikes by generating a dual weighted gene co-expression network (WGCN).
View Article and Find Full Text PDFPLoS One
January 2025
Institute for Physical Activity and Nutrition, Deakin University, Melbourne, VIC, Australia.
Heart disease remains a leading cause of mortality and morbidity worldwide, necessitating the development of accurate and reliable predictive models to facilitate early detection and intervention. While state of the art work has focused on various machine learning approaches for predicting heart disease, but they could not able to achieve remarkable accuracy. In response to this need, we applied nine machine learning algorithms XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector machine (SVM), gaussian naïve bayes (NB gaussian), adaptive boosting, and linear regression to predict heart disease based on a range of physiological indicators.
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