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While current guidelines recommend R2* method as the first-line method for liver iron concentration (LIC) measurement, its diagnostic accuracy is debatable. A prior meta-analysis suggested limited accuracy of R2* method for identifying patients with iron overload. However, substantial advances in R2* method over the past decade may have improved its diagnostic performance. The purpose of this study is to explore the accuracy of the R2* method in identifying patients at clinically relevant LIC thresholds (1.8, 3.2, 7.0, and 15.0 mg/g). This is a meta-analysis. On February 16, 2024, databases including PubMed, Medline, Embase, Web of Science, and the Cochrane Library were searched for studies using the R2* method to quantify LIC. R2* values were derived from gradient echo sequence, with LIC from the FerriScan® (R2) or biopsy as reference standard. The true positive, false positive, true negative, and false negative at each LIC threshold (1.8, 3.2, 7.0, and 15.0 mg/g) were extracted from each study by two reviewers. Summary receiver operating characteristic (SROC) curves were generated using the hierarchical SROC (HSROC) model. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Twenty-six studies with 1687 patients were included. In identifying patients at each LIC threshold (1.8, 3.2, 7.0, and 15.0 mg/g), the area under the SROC curves (AUCs) were 0.96 or higher, with pooled sensitivities of 0.95 or higher. Pooled specificities were 0.93 or higher; however, studies using biopsy as a reference showed a wide range 95% confidence interval (CI) (0.51-0.99) for identifying patients at the 1.8 mg/g threshold. The R2* method accurately identified patients at different clinically relevant LIC thresholds (1.8, 3.2, 7.0, and 15.0 mg/g). However, when biopsy is used as a reference, the method may be unstable for the 1.8 mg/g threshold (95% CI for pooled specificity, 0.51-0.99). LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.
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http://dx.doi.org/10.1002/jmri.29707 | DOI Listing |
J Cataract Refract Surg
July 2025
Department of Ophthalmology, West China Hospital of Sichuan University, Chengdu City, Sichuan Province, China.
Purpose: To develop and validate a multimodal deep-learning model for predicting postoperative vault height and selecting implantable collamer lens (ICL) sizes using Anterior Segment Optical Coherence Tomography (AS-OCT) and Ultrasound Biomicroscope (UBM) images combined with clinical features.
Setting: West China Hospital of Sichuan University, China.
Design: Deep-learning study.
PLoS One
September 2025
Remiza AI, Poughkeepsie, New York, United States of America.
Background: Hybrid entertainment formats combining competitive and comedic elements present opportunities to investigate factors driving audience engagement. I analyzed Taskmaster UK (2015-2023), a BAFTA-winning comedy panel show where comedians compete in creative tasks judged by a host, to quantify relationships between scoring mechanics, performer characteristics, and viewer ratings.
Methods: I analyzed 154 episodes encompassing 917 tasks performed by 90 contestants, with audience reception measured through 32,607 IMDb votes.
J AOAC Int
September 2025
Analytical Development Division, Senores Pharmaceuticals, Ahmedabad, India.
Background: Molnupiravir, an FDA-approved antiviral for the treatment of COVID-19, requires reliable analytical methods to ensure its quality and safety due to its therapeutic importance.
Objectives: This study presents the development of a stability-indicating RP-HPLC method for estimating molnupiravir-related impurities in capsule formulations. An unknown impurity is structurally elucidated using LC-TQ/MS and 1H and 1³C NMR spectroscopy.
PLoS One
September 2025
Faculty of Health Sciences, Department of Nursing and Integrated Health Sciences, Kristianstad University, Kristianstad, Sweden.
Several non-communicable diseases are strongly linked to lifestyle factors, making preventive measures essential. One effective approach is lifestyle counselling, which has demonstrated promising results in the prevention, treatment, and management of these diseases. However, despite its potential, patients often do not receive lifestyle counselling to the extent required.
View Article and Find Full Text PDFPsychol Health
September 2025
Institute of Applied Health Sciences, School of Medicine, Medical Sciences & Nutrition, University of Aberdeen, Aberdeen, UK.
Objective: There is a lack of research on how illness representations as represented in the Common Sense Self-Regulation Model (CS-SRM) emerge and develop. We aimed to describe the evolution of COVID-19 illness representations over time, and to explore associations with sociodemographic characteristics and protective behaviours.
Methods And Measures: This study (June 2020 release from lockdown to February 2021 after vaccine roll-out) used 17 independently recruited cross-sectional cohorts.