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Diagnosis and treatment of patients with gastroesophageal reflux disease - a systematic review of cost-effectiveness and economic burden.
BMC Health Serv Res
November 2024
Betthera s.r.o, Hradec Kralove, Czech Republic.
Background: This study aims to review the existing knowledge on the cost-effectiveness and item costs related to the diagnosis and treatment of gastroesophageal reflux disease (GERD) patients at different stages.
Methods: The study adhered to the PRISMA guidelines. The systematic search involved several steps: finding and identifying relevant articles, filtering them according to the set criteria, and examining the final number of selected articles to obtain the primary information.
A method for predicting remaining useful life using enhanced Savitzky-Golay filter and improved deep learning framework.
Sci Rep
October 2024
Centre for Sustainable Engineering, Teesside University, Middlesbrough, TS1 3BA, UK.
Ensuring operational integrity in large-scale equipment hinges on effective fault prediction and health management. Prognostics and health management (PHM) face the challenge of accurately predicting remaining useful life (RUL) using multivariate sensor data. Traditional methods often require extensive prior knowledge for indicator construction and processing.
View Article and Find Full Text PDFAn integrated blockchain-enabled multi-channel vaccine supply chain network under hybrid uncertainties.
Sci Rep
October 2024
Department of Industrial Engineering, University of Torbat Heydarieh, Torbat Heydarieh, Iran.
Article Synopsis
- The COVID-19 pandemic has caused big problems, and creating a good way to distribute vaccines is really important for helping people.
- Scientists are using advanced technology, like blockchain, to make the vaccine supply process clearer and more reliable.
- A new math model was made to help plan the vaccine distribution, focusing on costs and improving how well the vaccines are assigned based on age and population needs.
Focal cortical dysplasia lesion segmentation using multiscale transformer.
Insights Imaging
September 2024
Shenzhen Children's Hospital, Shenzhen, 518000, Guangdong, China.
Article Synopsis
- Accurate segmentation of focal cortical dysplasia (FCD) lesions from MR images is crucial for surgical planning, but it's a challenging task for radiologists; a new transformer-based model has been developed to assist in this process.
- The model combines a convolutional neural network (CNN) encoder-decoder with a multiscale transformer to enhance feature representation, allowing it to identify long-range dependencies in the MR images effectively.
- Experimental results show that the model successfully segmented lesions in 82.4% of patients with a low false-positive rate and achieved a higher Dice coefficient compared to existing methods, highlighting its potential as a valuable tool for physicians in diagnosing and planning treatment for FCD.
PCAlign: a general data augmentation framework for point clouds.
Sci Rep
September 2024
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, People's Republic of China.
With the advancement of 3D scanning technologies and deep learning theories, point cloud-based deep learning networks have gained considerable attention in the fields of 3D vision and computer graphics. Leveraging the rich geometric information present in 3D point clouds, these networks facilitate more accurate feature learning tasks. However, existing networks often suffer from generalization defects caused by variations in pose and inconsistent representations of training data.
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