Objectives: To investigate the usefulness of severity classification for predicting outcomes in patients with adult-onset Still's disease (AOSD).
Methods: This was a multi-centre retrospective cohort study. AOSD patients were classified into mild, moderate, and severe groups based on severity classification (Japanese Ministry of Health, Labour and Welfare) during the initial treatment, and clinical features were compared among these groups. The primary endpoints were the AOSD-related mortality and drug-free remission rate. For comparison, the same analysis was performed in parallel for patient groups stratified by the modified Pouchot systemic score.
Results: According to severity classification, 49 (35%), 37 (26%), and 56 patients (39%) were classified into mild, moderate, and severe groups, respectively. Patients in the severe group showed higher frequency of severe complications and the use of biological agents. Although AOSD-related survival was not significantly different (p = .0776), four of the five fatal cases were classified into the severe group. The severe group showed a reduced rate of drug-free remission (p = .0125). Patient groups classified by systemic score did not correlate with survival or drug-free remission.
Conclusions: Severity classification is useful for predicting outcomes in patients with AOSD.
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http://dx.doi.org/10.1093/mr/roab083 | DOI Listing |
Biomed Phys Eng Express
January 2025
Department of Ophthalmology, Hospital Universitario de Canarias, Carretera Ofra S/N, La Laguna, Santa Cruz de Tenerife, 38320, SPAIN.
This paper systematically evaluates saliency methods as explainability tools for convolutional neural networks trained to diagnose glaucoma using simplified eye fundus images that contain only disc and cup outlines. These simplified images, a methodological novelty, were used to relate features highlighted in the saliency maps to the geometrical clues that experts consider in glaucoma diagnosis. Despite their simplicity, these images retained sufficient information for accurate classification, with balanced accuracies ranging from 0.
View Article and Find Full Text PDFPLoS Negl Trop Dis
January 2025
Sustainable Sciences Institute, Managua, Nicaragua.
Background: Dengue virus, a major global health threat, consists of four serotypes (DENV1-4) that cause a range of clinical manifestations from mild to severe and potentially fatal disease.
Methods: This study, based on 19 years of data from the Pediatric Dengue Cohort Study and Pediatric Dengue Hospital-based Study in Managua, Nicaragua, investigates the relationship of serotype and immune status with dengue severity. Dengue cases were confirmed by molecular, serological, and/or virological methods, and study participants 6 months to 17 years old were followed during their hospital stay or as ambulatory patients.
Arch Virol
January 2025
Molecular Bioassay Laboratory, Institute of Advanced Virology, Bio 360 Life Sciences Park, Thonnakkal, Thiruvananthapuram, Kerala, India.
Human bocaviruses (HBoVs) can cause respiratory illness in young children. Although the first HBoV infection in India was reported in 2010, very little information is available about its prevalence, clinical features, or geographic distribution in this country. This study was conducted using 136 respiratory samples from paediatric patients in a tertiary care hospital in Kerala, 21 of which tested positive for HBoV1 and were further characterized through VP1/VP2 gene sequencing.
View Article and Find Full Text PDFEur Radiol
January 2025
Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands.
Objectives: The use of deep learning models for quantitative measurements on coronary computed tomography angiography (CCTA) may reduce inter-reader variability and increase efficiency in clinical reporting. This study aimed to investigate the diagnostic performance of a recently updated deep learning model (CorEx-2.0) for quantifying coronary stenosis, compared separately with two expert CCTA readers as references.
View Article and Find Full Text PDFJ Med Microbiol
January 2025
Field Service - South East and London, UK Health Security Agency, London, UK.
Shiga toxin-producing (STEC) infections are of public health concern as STEC can cause large national foodborne outbreaks of severe gastrointestinal disease, particularly in the young and elderly. In recent years, the implementation of PCR by diagnostic microbiology laboratories has improved the detection of STEC, and there has been an increase in notifications of cases of non-O157 STEC. However, the extent this increase in caseload can be attributed to the improved detection by PCR, or a true increase in non-O157 STEC infections, is unknown.
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