Clinical management of lupus in the United States: A claims-based analysis.

Objectives: To understand the evaluation and management of patients coded with lupus in the broad clinical community in the United States.

Methods: Claims data for diagnoses, procedures, medications, and physician specialties were evaluated for three lupus cohorts [lupus nephritis (LN), systemic lupus erythematosus excluding LN (SLE), and cutaneous lupus erythematosus excluding SLE and LN (CLE)] using the EVERSANA claims databases. Identification of patients was based upon the occurrence of lupus-specific codes, with the requirement that a single patient receive a lupus-related ICD code twice within a six-month period.

Analysis of transcriptomic features reveals molecular endotypes of SLE with clinical implications.

Background: Systemic lupus erythematosus (SLE) is known to be clinically heterogeneous. Previous efforts to characterize subsets of SLE patients based on gene expression analysis have not been reproduced because of small sample sizes or technical problems. The aim of this study was to develop a robust patient stratification system using gene expression profiling to characterize individual lupus patients.

Molecular mechanisms governing the progression of nephritis in lupus prone mice and human lupus patients.

Introduction: Pathologic inflammation is a major driver of kidney damage in lupus nephritis (LN), but the immune mechanisms of disease progression and risk factors for end organ damage are poorly understood.

Methods: To characterize molecular profiles through the development of LN, we carried out gene expression analysis of microdissected kidneys from lupus-prone NZM2328 mice. We examined male mice and the congenic NZM2328.

Hypovitaminosis A Drives the Progression of Tubulointerstitial Lupus Nephritis through Potentiating Predisease Cellular Autoreactivity.

Vitamin A (VA) deficiency (VAD) is observed in both humans and mice with lupus nephritis. However, whether VAD is a driving factor for accelerated progression of lupus nephritis is unclear. In this study, we investigated the effect of VAD on the progression of lupus nephritis in a lupus-prone mouse model, MRL/lpr.

Recent advances in the use of machine learning and artificial intelligence to improve diagnosis, predict flares, and enrich clinical trials in lupus.

Purpose Of Review: Machine learning is a computational tool that is increasingly used for the analysis of medical data and has provided the promise of more personalized care.

Recent Findings: The frequency with which machine learning analytics are reported in lupus research is comparable with that of rheumatoid arthritis and cancer, yet the clinical application of these computational tools has yet to be translated into better care. Considerable work has been applied to the development of machine learning models for lupus diagnosis, flare prediction, and classification of disease using histology or other medical images, yet few models have been tested in external datasets and independent centers.

Machine learning reveals distinct gene signature profiles in lesional and nonlesional regions of inflammatory skin diseases.

Analysis of gene expression from cutaneous lupus erythematosus, psoriasis, atopic dermatitis, and systemic sclerosis using gene set variation analysis (GSVA) revealed that lesional samples from each condition had unique features, but all four diseases displayed common enrichment in multiple inflammatory signatures. These findings were confirmed by both classification and regression tree analysis and machine learning (ML) models. Nonlesional samples from each disease also differed from normal samples and each other by ML.

An introduction to machine learning and analysis of its use in rheumatic diseases.

Machine learning (ML) is a computerized analytical technique that is being increasingly employed in biomedicine. ML often provides an advantage over explicitly programmed strategies in the analysis of multidimensional information by recognizing relationships in the data that were not previously appreciated. As such, the use of ML in rheumatology is increasing, and numerous studies have employed ML to classify patients with rheumatic autoimmune inflammatory diseases (RAIDs) from medical records and imaging, biometric or gene expression data.

Altered expression of genes controlling metabolism characterizes the tissue response to immune injury in lupus.

To compare lupus pathogenesis in disparate tissues, we analyzed gene expression profiles of human discoid lupus erythematosus (DLE) and lupus nephritis (LN). We found common increases in myeloid cell-defining gene sets and decreases in genes controlling glucose and lipid metabolism in lupus-affected skin and kidney. Regression models in DLE indicated increased glycolysis was correlated with keratinocyte, endothelial, and inflammatory cell transcripts, and decreased tricarboxylic (TCA) cycle genes were correlated with the keratinocyte signature.

Comprehensive transcriptomic analysis of COVID-19 blood, lung, and airway.

SARS-CoV2 is a previously uncharacterized coronavirus and causative agent of the COVID-19 pandemic. The host response to SARS-CoV2 has not yet been fully delineated, hampering a precise approach to therapy. To address this, we carried out a comprehensive analysis of gene expression data from the blood, lung, and airway of COVID-19 patients.

Augmentation of brain tumor interstitial flow via focused ultrasound promotes brain-penetrating nanoparticle dispersion and transfection.

The delivery of systemically administered gene therapies to brain tumors is exceptionally difficult because of the blood-brain barrier (BBB) and blood-tumor barrier (BTB). In addition, the adhesive and nanoporous tumor extracellular matrix hinders therapeutic dispersion. We first developed the use of magnetic resonance image (MRI)-guided focused ultrasound (FUS) and microbubbles as a platform approach for transfecting brain tumors by targeting the delivery of systemically administered "brain-penetrating" nanoparticle (BPN) gene vectors across the BTB/BBB.

Drug repurposing to improve treatment of rheumatic autoimmune inflammatory diseases.

The past century has been characterized by intensive efforts, within both academia and the pharmaceutical industry, to introduce new treatments to individuals with rheumatic autoimmune inflammatory diseases (RAIDs), often by 'borrowing' treatments already employed in one RAID or previously used in an entirely different disease, a concept known as drug repurposing. However, despite sharing some clinical manifestations and immune dysregulation, disease pathogenesis and phenotype vary greatly among RAIDs, and limited understanding of their aetiology has made repurposing drugs for RAIDs challenging. Nevertheless, the past century has been characterized by different 'waves' of repurposing.

MRI analysis to map interstitial flow in the brain tumor microenvironment.

Glioblastoma (GBM), a highly aggressive form of brain tumor, is a disease marked by extensive invasion into the surrounding brain. Interstitial fluid flow (IFF), or the movement of fluid within the spaces between cells, has been linked to increased invasion of GBM cells. Better characterization of IFF could elucidate underlying mechanisms driving this invasion .

Convective forces increase CXCR4-dependent glioblastoma cell invasion in GL261 murine model.

Glioblastoma is the most common and malignant form of brain cancer. Its invasive nature limits treatment efficacy and promotes inevitable recurrence. Previous in vitro studies showed that interstitial fluid flow, a factor characteristically increased in cancer, increases glioma cell invasion through CXCR4-CXCL12 signaling.

Publisher Correction: Functional aspects of meningeal lymphatics in ageing and Alzheimer's disease.

Change history: In this Article, Extended Data Fig. 9 was appearing as Fig. 2 in the HTML, and in Fig.

Functional aspects of meningeal lymphatics in ageing and Alzheimer's disease.

Ageing is a major risk factor for many neurological pathologies, but its mechanisms remain unclear. Unlike other tissues, the parenchyma of the central nervous system (CNS) lacks lymphatic vasculature and waste products are removed partly through a paravascular route. (Re)discovery and characterization of meningeal lymphatic vessels has prompted an assessment of their role in waste clearance from the CNS.

Interstitial flow differentially increases patient-derived glioblastoma stem cell invasion via CXCR4, CXCL12, and CD44-mediated mechanisms.

Glioblastoma (GBM) prognosis remains dismal due in part to the invasiveness of GBM cells. Interstitial fluid flow (IFF) has been shown to increase invasion of glioma cells in vitro through the CXCR4 receptor interacting with autologous, pericellular gradients of CXCL12 (autologous chemotaxis) or through the CD44 receptor interactions with the extracellular matrix (hyaluronan-mediated mechanotransduction). These mechanisms have not been examined together and thus we hypothesized that both mechanisms contribute to invasion in populations of cancer cells.