Background: Macrophage activation syndrome (MAS) is a serve complication of juvenile idiopathic inflammatory myopathies (JIIMs). This study delineates the clinical manifestations and genetic underpinnings of JIIM-MAS patients.
Methods: We retrospectively analysed clinical and UNC13D gene from JIIM patients admitted to our centre between 2011 and 2021 to identify cases of MAS. Additionally, a literature review summarising reported cases of JIIMs and MAS was performed.
Results: Of 773 JIIM patients, 10 (1.3%) were diagnosed with MAS. All patients presented with persistent fever and hyperferritinaemia. Seventy percent of patients met the HLH-2004 criteria, while 90% met the 2016 sJIA-MAS criteria. Most patients received combined treatment of corticosteroids and immunosuppressants. UNC13D gene analysis was performed in six patients. A homozygous pathogenic mutation (c.2588G>A) was detected in one patient with recurrent MAS, and twenty-eight single-nucleotide polymorphisms (SNPs) were detected. Eighty percent of patients exhibiting a consistent combination of ten SNPs compared to JIIM patients without MAS (35%).
Conclusion: MAS is an early and often overlooked complication of JIIMs. The 2016 sJIA-MAS criteria may facilitate early diagnosis. Combined corticosteroid and immunosuppressant therapy prove effective. An increased prevalence of UNC13D gene polymorphisms was observed in JIIM-MAS patients, highlighting the necessity for further investigations.
Impact: This study aimed to delineate the clinical manifestations and genetic underpinnings of macrophage activation syndrome (MAS) in ten patients with juvenile idiopathic inflammatory myopathies (JIIMs). MAS has been recognised as a complication of JIIMs. However, only a few case reports provide comprehensive descriptions of MAS in JIIM patients, and there are few reports related to UNC13D mutations in these patients. This article offers single-centre clinical insights to enhance the identification and management of MAS in JIIM patients, while also highlighting the potential association between MAS occurrence and UNC13D gene polymorphisms.
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http://dx.doi.org/10.1038/s41390-024-03515-7 | DOI Listing |
J Imaging Inform Med
January 2025
School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan, China.
The automated diagnosis of low-resolution and difficult-to-recognize breast ultrasound images through multi-modal fusion holds significant clinical value. However, prevailing fusion methods predominantly rely on image modalities, neglecting the textual pathology information, and only benign and malignant diagnosis of breast tumors is not satisfying for clinical applications. Consequently, this paper proposes a novel multi-modal fusion interactive diagnostic framework, termed the MIC framework, to achieve the multi-label classification of breast cancer, namely benign-malignant classification and breast imaging reporting and data system (BI-RADS) 3, 4a, 4b, 4c, and 5 gradings.
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January 2025
Laboratory of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece.
A scoping review was conducted to investigate the role of radiological imaging, particularly high-resolution computed tomography (HRCT), and artificial intelligence (AI) in diagnosing and prognosticating idiopathic pulmonary fibrosis (IPF). Relevant studies from the PubMed database were selected based on predefined inclusion and exclusion criteria. Two reviewers assessed study quality and analyzed data, estimating heterogeneity and publication bias.
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January 2025
College of Computer, Chongqing University, No. 55 Daxuecheng South Rd, Shapingba, 401331, Chongqing, China.
Convolutional neural networks (CNNs) have become indispensable to medical image diagnosis research, enabling the automated differentiation of diseased images from extensive medical image datasets. Due to their efficacy, these methods raise significant privacy concerns regarding patient images and diagnostic models. To address these issues, some researchers have explored privacy-preserving medical image diagnosis schemes using fully homomorphic encryption (FHE).
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January 2025
Monash Imaging, Monash Health, 246 Clayton Rd, Clayton, VIC, 3168, Australia.
We extend existing techniques by using generative adversarial network (GAN) models to reduce the appearance of cast shadows in radiographs across various age groups. We retrospectively collected 11,500 adult and paediatric wrist radiographs, evenly divided between those with and without casts. The test subset consisted of 750 radiographs with cast and 750 without cast.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Independent Consultant, Kirkland, WA, USA.
Point-of-care ultrasound (POCUS) has emerged as a standard of care across a variety of healthcare settings due to its ability to provide critical clinical information and as well as procedural guidance to clinicians directly at the bedside. Implementation of enterprise imaging (EI) strategies is needed such that POCUS images can be appropriately captured, indexed, managed, stored, distributed, viewed, and analyzed. Because of its unique workflow and educational requirements, reliance on traditional order-based workflow solutions may be insufficient.
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