Although about 35 years have elapsed since the discovery of the Helicobacter pylori, its diagnosis and the choice of optimal eradication therapy are still to be defined. Over time, there has been an increase in interest, publications, recommendations and guidelines. Moreover, management of the disease in pediatric subjects differs somewhat to that of adults and requires a more delicate approach leading to alternative strategies for both diagnosis and treatment. Which patient should be investigated for H. pylori, when to perform noninvasive or invasive tests, what are the proper therapeutic options and best antibiotics regimen to eradicate the infection are practices changing with evidences through time. Therefore, an updated guideline was published by the European Society of Pediatric Gastroenterology, Hepatology and Nutrition (ESPGHAN) and the North American Society for Pediatric Gastroenterology, Hepatology and Nutrition (NASPGHAN) in 2017. The aim of this review is to highlight what is new and what differs between adult and pediatric population regarding the management of H. pylori infection after the ESPGHAN/NASPGHAN guidelines, enriched with updates from literature reviews published over the last two years.
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http://dx.doi.org/10.23736/S0026-4946.18.05346-X | DOI Listing |
Ann Surg Oncol
January 2025
Department of Otolaryngology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Background: Anaplastic thyroid cancer (ATC) is a highly lethal disease, often diagnosed with advanced locoregional and distant metastases, resulting in a median survival of just 3-5 months. This study determines the stratified effectiveness of baseline treatments in all combinations, enabling precise prognoses prediction and establishing benchmarks for advanced therapeutic options.
Methods: The study extracted a cohort of pathologically confirmed ATC patients from the Surveillance, Epidemiology, and End Results program.
Z Gerontol Geriatr
January 2025
Geriatrie, Universität Witten-Herdecke, Alfred Herrhausenstraße 50, 58455, Witten, Germany.
Chronic obstructive pulmonary disease (COPD) is a frequent disease from which approximately 8% of individuals aged 40 years and above suffer. The prevalence increases up to fivefold as age advances. Following an introduction including the etiology, measurement, characteristic features and classification of COPD, this article presents the consensus recommendations of the German Working Group on Pneumology in Older Patients.
View Article and Find Full Text PDFBrain Imaging Behav
January 2025
Macquarie Medical School, Macquarie University, Sydney, NSW, Australia.
Magnetic resonance imaging (MRI) is frequently used to monitor disease progression in multiple sclerosis (MS). This study aims to systematically evaluate the correlation between MRI measures and histopathological changes, including demyelination, axonal loss, and gliosis, in the central nervous system of MS patients. We systematically reviewed post-mortem histological studies evaluating myelin density, axonal loss, and gliosis using quantitative imaging in MS.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Disease, Shanghai, 200080, China.
The objectives of this study are to construct a deep convolutional neural network (DCNN) model to diagnose and classify meibomian gland dysfunction (MGD) based on the in vivo confocal microscope (IVCM) images and to evaluate the performance of the DCNN model and its auxiliary significance for clinical diagnosis and treatment. We extracted 6643 IVCM images from the three hospitals' IVCM database as the training set for the DCNN model and 1661 IVCM images from the other two hospitals' IVCM database as the test set to examine the performance of the model. Construction of the DCNN model was performed using DenseNet-169.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
College of Engineering, Department of Computer Engineering, Koç University, Rumelifeneri Yolu, 34450, Sarıyer, Istanbul, Turkey.
This study explores a transfer learning approach with vision transformers (ViTs) and convolutional neural networks (CNNs) for classifying retinal diseases, specifically diabetic retinopathy, glaucoma, and cataracts, from ophthalmoscopy images. Using a balanced subset of 4217 images and ophthalmology-specific pretrained ViT backbones, this method demonstrates significant improvements in classification accuracy, offering potential for broader applications in medical imaging. Glaucoma, diabetic retinopathy, and cataracts are common eye diseases that can cause vision loss if not treated.
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