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Although glucocorticoids (GCs) are the cornerstone for the treatment for systemic lupus erythematosus (SLE), they are associated with a number of adverse effects. Recently, an open randomized controlled trial (RCT) conducted in France showed that flares of SLE in 12 months were significantly more common with GCs withdrawal than continuation in stable patients. However, the study did not separately report results on subsets of SLE patients with organ threatening disease such as lupus nephritis (LN). We hereby present a literature review on GCs withdrawal in SLE and propose a protocol for a multicenter RCT in Hong Kong to evaluate the feasibility of withdrawal of low-dose GCs in stable LN patients who are in clinical remission.

Systemic lupus erythematosus (SLE) is an autoimmune disease with involvement of multiple systems. Despite the therapeutic advances in the past few decades, refractory SLE causing organ damage and life-threatening complications still poses a therapeutic challenge. Therapeutic plasma exchange is considered as one of the rescue therapies used in refractory SLE. However, the level of evidence supporting its use is low. This article reviews the current evidence of the application of plasmapheresis in the treatment of SLE.

Patient empowerment in lupus is a process wherein patients actively participate in decision-making with healthcare professionals and take responsibility for their own condition. In order to effectively be involved in their medical care and achieve the best treatment outcomes, patients have to be equipped with a sufficient body of knowledge and understanding of lupus and its intricacies. Furthermore, the significant roles of families and caregivers, other healthcare professionals and support systems, and policymakers in each country have an immense impact on health outcomes and the overall quality of life.

Systemic lupus erythematosus (SLE) is a complicated multisystem autoimmune disease that carries substantial mortality and morbidity. The development of new therapies is partly hindered by the lack of a flawless instrument to gauge disease activity in different organs for the primary efficacy outcome. Until a global consensus is reached regarding the most preferred assessment tool, application of any of the existing disease activity indices is acceptable in clinical practice and research. However, one should have a more thorough understanding of the strength and limitations of individual disease activity instruments. This article updates the SLE disease activity indices commonly used in clinical trials and their latest stage of development.

Coronavirus disease 2019 (COVID-19) was one of the most important infections in the past few years. Although most cases of COVID-19 are mild, serious complications may arise. The multisystem inflammatory syndrome (MIS) is a rare but severe, yet poorly understood complication of COVID-19. It was first described in childhood patients with COVID-19 (MIS-C) but is increasingly reported in adults. MIS-A may lead to diagnostic and therapeutic dilemma as its manifestations may mimic a flare of the underlying disease and the use of immunosuppressive therapy may further increase the risk of other infective complications in these patients. However, MIS-A has rarely been reported in patients with pre-existing autoimmune conditions. We hereby present a patient with systemic lupus erythematosus who developed severe MIS-A 6 weeks after a recent SARS-CoV-2 infection.

Single-cell RNA sequencing (scRNA-seq) has the potential to provide powerful, high-resolution signatures to inform disease prognosis and precision medicine. This paper takes an important first step towards this goal by developing an interpretable machine learning algorithm, CloudPred, to predict individuals’ disease phenotypes from their scRNA-seq data. Predicting phenotype from scRNA-seq is challenging for standard machine learning methods—the number of cells measured can vary by orders of magnitude across individuals and the cell populations are also highly heterogeneous. Typical analysis creates pseudo-bulk samples which are biased toward prior annotations and also lose the single cell resolution. CloudPred addresses these challenges via a novel end-to-end differentiable learning algorithm which is coupled with a biologically informed mixture of cell types model. CloudPred automatically infers the cell subpopulation that are salient for the phenotype without prior annotations. We developed a systematic simulation platform to evaluate the performance of CloudPred and several alternative methods we propose, and find that CloudPred outperforms the alternative methods across several settings. We further validated CloudPred on a real scRNA-seq dataset of 142 lupus patients and controls. CloudPred achieves AUROC of 0.98 while identifying a specific subpopulation of CD4 T cells whose presence is highly indicative of lupus. CloudPred is a powerful new framework to predict clinical phenotypes from scRNA-seq data and to identify relevant cells.