A nomogram for the prediction of co-infection in MDA5 dermatomyositis: A rapid clinical assessment model.

Background: Patients with anti-melanoma differentiation-associated gene 5-positive dermatomyositis (MDA5 DM) are prone to infections, but there is a lack of rapid methods to assess infection risk, which greatly affects patient prognosis. This study aims to analyze the clinical features of MDA5 DM patients systematically and develop a predictive model for infections.

Methods: Retrospective analysis was performed on clinical data from 118 hospitalized patients with MDA5 DM.

COVID-19 in Systemic Lupus Erythematosus patients treated with belimumab: a retrospective clinical study.

Background: Routine use of immunosuppressive agents in systemic lupus erythematosus (SLE) patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) potentially increases the risk of adverse outcomes. belimumab, a monoclonal antibody for the treatment of SLE, remains untested for its specific impact on coronavirus disease 2019 (COVID-19) symptoms in these patients. Here, this research investigated the effect of belimumab on COVID-19 symptoms in SLE patients infected with SARS-CoV-2.

Interplay between COVID-19 and Secukinumab treatment in Spondylarthritis patients during the omicron surge: a retrospective cohort study.

The objective of this retrospective cohort study was to assess the relationship between Corona Disease 2019 (COVID-19) and Secukinumab treatment in patients with Spondylarthritis (SpA) in China during the omicron surge. Researchers retrieved 1018 medical records of Secukinumab-treated patients between January 2020 and January 2023 from the West China Hospital of Sichuan University. Out of these, 190 SpA patients from the rheumatology clinic were selected for the study.

Modeling annotator preference and stochastic annotation error for medical image segmentation.

Manual annotation of medical images is highly subjective, leading to inevitable annotation biases. Deep learning models may surpass human performance on a variety of tasks, but they may also mimic or amplify these biases. Although we can have multiple annotators and fuse their annotations to reduce stochastic errors, we cannot use this strategy to handle the bias caused by annotators' preferences.

Gut microbiota dysbiosis characterized by abnormal elevation of in patients with immune-mediated necrotizing myopathy.

Aim: The gut microbiota plays an important role in human health. In this study, we aimed to investigate whether and how gut microbiota communities are altered in patients with immune-mediated necrotizing myopathy (IMNM) and provide new ideas to further explore the pathogenesis of IMNM or screen for its clinical therapeutic targets in the future.

Methods: The gut microbiota collected from 19 IMNM patients and 23 healthy controls (HCs) were examined by using 16S rRNA gene sequencing.

Effects of Microecological Regulators on Rheumatoid Arthritis: A Systematic Review and Meta-Analysis of Randomized, Controlled Trials.

In this study, the available data from published randomized, controlled trials (RCTs) of the use of intestinal microecological regulators as adjuvant therapies to relieve the disease activity of rheumatoid arthritis (RA) are systematically compared. An English literature search was performed using PubMed, Embase, Scopus, Web of Science and the Cochrane Central Registry of Controlled Trials and supplemented by hand searching reference lists. Three independent reviewers screened and assessed the quality of the studies.

Risk of Early Infection in Idiopathic Inflammatory Myopathies: Cluster Analysis Based on Clinical Features and Biomarkers.

Patients with idiopathic inflammatory myopathies (IIMs), referred to as myositis, are prone to infectious complications, which hinder the treatment of the disease and worsen the outcome of patients. The purpose of this study was to explore the different types of infectious complications in patients with myositis and to determine the predisposing factors for clinical reference. A retrospective study was conducted on 66 patients with IIM who were divided into different subpopulations by an unsupervised analysis of their clinical manifestations, laboratory features, and autoantibody characteristics.

Domain and Content Adaptive Convolution Based Multi-Source Domain Generalization for Medical Image Segmentation.

The domain gap caused mainly by variable medical image quality renders a major obstacle on the path between training a segmentation model in the lab and applying the trained model to unseen clinical data. To address this issue, domain generalization methods have been proposed, which however usually use static convolutions and are less flexible. In this paper, we propose a multi-source domain generalization model based on the domain and content adaptive convolution (DCAC) for the segmentation of medical images across different modalities.

Learning From Ambiguous Labels for Lung Nodule Malignancy Prediction.

Lung nodule malignancy prediction is an essential step in the early diagnosis of lung cancer. Besides the difficulties commonly discussed, the challenges of this task also come from the ambiguous labels provided by annotators, since deep learning models have in some cases been found to reproduce or amplify human biases. In this paper, we propose a multi-view 'divide-and-rule' (MV-DAR) model to learn from both reliable and ambiguous annotations for lung nodule malignancy prediction on chest CT scans.