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Aicardi-Goutières syndrome type 6: report of ADAR variant and clinical outcome after ruxolitinib treatment in the neonatal period.
Pediatr Rheumatol Online J
December 2024
Translational Genetics Research Group, La Fe Health Research Institute (IIS La Fe), Avenida Fernando Abril Martorell nº 106 Tower A, 7th Floor, Valencia, Spain.
Background: Aicardi-Goutières Syndrome is a monogenic type 1 interferonopathy with infantile onset, characterized by a variable degree of neurological damage. Approximately 7% of Aicardi-Goutières Syndrome cases are caused by pathogenic variants in the ADAR gene and are classified as Aicardi-Goutières Syndrome type 6. Here, we present a new homozygous pathogenic variant in the ADAR gene.
View Article and Find Full Text PDFMulti-Energy Evaluation of Image Quality in Spectral CT Pulmonary Angiography Using Different Strength Deep Learning Spectral Reconstructions.
Acad Radiol
December 2024
Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL (M.H-S., H.S.S., A.G.R., S.E.M., J.C.P., E.Y.A., B.H., R.F.); Department of Radiology, University of Florida College of Medicine, Gainesville, FL (M.H-S., H.S.S., A.G.R., J.C.P., E.Y.A., B.H., R.F.); Division of Medical Physics, University of Florida College of Medicine, Gainesville, FL (R.F.); Department of Neurology, Division of Movement Disorders, University of Florida College of Medicine, Gainesville, FL (R.F.); Department of Otolaryngology - Head and Neck Surgery, McGill University, Montreal, Quebec, Canada (R.F.); Department of Radiology, AdventHealth Medical Group, Maitland, FL (R.F.). Electronic address:
Rationale And Objectives: To evaluate and compare image quality of different energy levels of virtual monochromatic images (VMIs) using standard versus strong deep learning spectral reconstruction (DLSR) on dual-energy CT pulmonary angiogram (DECT-PA).
Materials And Methods: A retrospective study was performed on 70 patients who underwent DECT-PA (15 PE present; 55 PE absent) scans. VMIs were reconstructed at different energy levels ranging from 35 to 200 keV using standard and strong levels with deep learning spectral reconstruction.
Longitudinal CT Radiomics to Predict Progression-free Survival in Patients with Locally Advanced Gastric Cancer After Neoadjuvant Chemotherapy.
Acad Radiol
December 2024
Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong Province, China (B.W., X.H., Z.Z., Z.L., S.L.). Electronic address:
Rationale And Objectives: To develop and validate a radiomics signature, utilizing baseline and restaging CT, for preoperatively predicting progression-free survival (PFS) after neoadjuvant chemotherapy (NAC) in locally advanced gastric cancer (LAGC).
Methods: A total of 316 patients with LAGC who received NAC followed by gastrectomy were retrospectively included in this single-center study; these patients were split into two cohorts, one for training (n = 243) and the other for validation (n = 73), based on the different districts of our hospital. A total of 1316 radiomics features were extracted from the volume of interest of the gastric-cancer lesion on venous phase CT images.
Development and Validation of a Nomogram Based on Multiparametric MRI for Predicting Lymph Node Metastasis in Endometrial Cancer: A Retrospective Cohort Study.
Acad Radiol
December 2024
Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China (Y.T., Y.W., Y.Y., X.Q., Y.H., J.L.); Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning 530021, Guangxi Zhuang Autonomous Region, PR China (J.L.). Electronic address:
Rationale And Objectives: To develop a radiomics nomogram based on clinical and magnetic resonance features to predict lymph node metastasis (LNM) in endometrial cancer (EC).
Materials And Methods: We retrospectively collected 308 patients with endometrial cancer (EC) from two centers. These patients were divided into a training set (n=155), a test set (n=67), and an external validation set (n=86).
Monitoring low-value care in medical patients from Swiss university hospitals using a Findable, Accessible, Interoperable, Reusable (FAIR) national data stream and patient and public involvement: LUCID study protocol.
BMJ Open
December 2024
CHUV, Lausanne, Vaud, Switzerland.
Introduction: Healthcare practices providing minimal or no benefit to recipients have been estimated to represent 20% of healthcare costs. However, defining, measuring and monitoring low-value care (LVC) and its downstream consequences remain a major challenge. The purpose of the National Data Stream (LUCID NDS) is to identify and monitor LVC in medical inpatients using routinely collected hospital data.
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