Mepacrine as successful monotherapy for refractory Jessner-Kanof disease: still an important drug in the dermatologic armamentarium

Introduction: Jessner–Kanof disease (JKD), a lymphocytic infiltration of the skin, can be difficult to treat. Mepacrine (quinacrine), an anti-malarial less available in Belgium, may be beneficial. Patients and methods: Two female patients with biopsy-proven and therapy-resistant JKD, not responding to topical and systemic corticosteroids, (hydroxy-)chloroquine and/or dapsone, were treated with mepacrine 100 mg daily. Results: In both patients an amelioration was observed during the first month of treatment, and clinical remission was obtained by the fourth month, without any side-effects. In both cases, the dose could be tapered to three times weekly. Discussion: JKD is strongly related to lupus erythematosus (tumidus), and although spontaneous remissions may occur, it is notoriously difficult to treat. Mepacrine may be initiated as an add-on therapy to (hydroxy-)chloroquine, but also as monotherapy. A dose of 100 mg a day, tapered to weekly doses once remission is obtained, seems feasible. Except for (mild) yellow skin discoloration, the drug has few side-effects, and offers the advantage of not displaying retinal toxicity. Conclusion: Mepacrine is still a useful and safe drug for treating cutaneous lupus erythematosus and related skin conditions, such as refractory JKD in particular. Its future availability, also in Belgium, is therefore important

Blood transcriptomics to facilitate diagnosis and stratification in pediatric rheumatic diseases - a proof of concept study.

Transcriptome profiling of blood cells is an efficient tool to study the gene expression signatures of rheumatic diseases. This study aims to improve the early diagnosis of pediatric rheumatic diseases by investigating patients' blood gene expression and applying machine learning on the transcriptome data to develop predictive models. RNA sequencing was performed on whole blood collected from children with rheumatic diseases. Random Forest classification models were developed based on the transcriptome data of 48 rheumatic patients, 46 children with viral infection, and 35 controls to classify different disease groups. The performance of these classifiers was evaluated by leave-one-out cross-validation. Analyses of differentially expressed genes (DEG), gene ontology (GO), and interferon-stimulated gene (ISG) score were also conducted. Our first classifier could differentiate pediatric rheumatic patients from controls and infection cases with high area-under-the-curve (AUC) values (AUC = 0.8 ± 0.1 and 0.7 ± 0.1, respectively). Three other classifiers could distinguish chronic recurrent multifocal osteomyelitis (CRMO), juvenile idiopathic arthritis (JIA), and interferonopathies (IFN) from control and infection cases with AUC ≥ 0.8. DEG and GO analyses reveal that the pathophysiology of CRMO, IFN, and JIA involves innate immune responses including myeloid leukocyte and granulocyte activation, neutrophil activation and degranulation. IFN is specifically mediated by antibacterial and antifungal defense responses, CRMO by cellular response to cytokine, and JIA by cellular response to chemical stimulus. IFN patients particularly had the highest mean ISG score among all disease groups. Our data show that blood transcriptomics combined with machine learning is a promising diagnostic tool for pediatric rheumatic diseases and may assist physicians in making data-driven and patient-specific decisions in clinical practice

Blood transcriptomics to facilitate diagnosis and stratification in pediatric rheumatic diseases : a proof of concept study

Transcriptome profiling of blood cells is an efficient tool to study the gene expression signatures of rheumatic diseases. This study aims to improve the early diagnosis of pediatric rheumatic diseases by investigating patients' blood gene expression and applying machine learning on the transcriptome data to develop predictive models. RNA sequencing was performed on whole blood collected from children with rheumatic diseases. Random Forest classification models were developed based on the transcriptome data of 48 rheumatic patients, 46 children with viral infection, and 35 controls to classify different disease groups. The performance of these classifiers was evaluated by leave-one-out cross-validation. Analyses of differentially expressed genes (DEG), gene ontology (GO), and interferon-stimulated gene (ISG) score were also conducted. Our first classifier could differentiate pediatric rheumatic patients from controls and infection cases with high area-under-the-curve (AUC) values (AUC = 0.8 ± 0.1 and 0.7 ± 0.1, respectively). Three other classifiers could distinguish chronic recurrent multifocal osteomyelitis (CRMO), juvenile idiopathic arthritis (JIA), and interferonopathies (IFN) from control and infection cases with AUC ≥ 0.8. DEG and GO analyses reveal that the pathophysiology of CRMO, IFN, and JIA involves innate immune responses including myeloid leukocyte and granulocyte activation, neutrophil activation and degranulation. IFN is specifically mediated by antibacterial and antifungal defense responses, CRMO by cellular response to cytokine, and JIA by cellular response to chemical stimulus. IFN patients particularly had the highest mean ISG score among all disease groups. Our data show that blood transcriptomics combined with machine learning is a promising diagnostic tool for pediatric rheumatic diseases and may assist physicians in making data-driven and patient-specific decisions in clinical practice