Immune-pineal axis protects rat lungs exposed to polluted air

Environmental pollution in the form of particulate matter <2.5 μm (PM_2.5) is a major risk factor for diseases such as lung cancer, chronic respiratory infections, and major cardiovascular diseases. Our goal was to show that PM_2.5 eliciting a proinflammatory response activates the immune-pineal axis, reducing the pineal synthesis and increasing the extrapineal synthesis of melatonin. Herein, we report that the exposure of rats to polluted air for 6 hours reduced nocturnal plasma melatonin levels and increased lung melatonin levels. Melatonin synthesis in the lung reduced lipid peroxidation and increased PM_2.5 engulfment and cell viability by activating high-affinity melatonin receptors. Diesel exhaust particles (DEPs) promoted the synthesis of melatonin in a cultured cell line (RAW 264.7 cells) and rat alveolar macrophages via the expression of the gene encoding for AANAT through a mechanism dependent on activation of the NFκB pathway. Expression of the genes encoding AANAT, MT1, and MT2 was negatively correlated with cellular necroptosis, as disclosed by analysis of Gene Expression Omnibus (GEO) microarray data from the human alveolar macrophages of nonsmoking subjects. The enrichment score for antioxidant genes obtained from lung gene expression data (GTEx) was significantly correlated with the levels of AANAT and MT1 but not the MT2 melatonin receptor. Collectively, these data provide a systemic and mechanistic rationale for coordination of the pineal and extrapineal synthesis of melatonin by a standard damage-associated stimulus, which activates the immune-pineal axis and provides a new framework for understanding the effects of air pollution on lung diseases.     

Correction to: Association of rheumatoid arthritis-related autoantibodies with pulmonary function test abnormalities in a rheumatoid arthritis registry

Clinical Rheumatology - The publisher regrets that the two sections under the Results omitted inadvertently on the original published version of the above article.     

Presence of active MRI lesions in patients suspected of non-radiographic axial spondyloarthritis with high disease activity and chance at conversion after a 6-month follow-up period

Clinical Rheumatology - The primary aim is to evaluate signs of inflammation on MRI of sacroiliac joints (SIJ)/spine in inflammatory back pain (IBP) patients suspected of nr-axSpA with high disease...     

Evaluation of a special concept of physical therapy in spondyloarthritis: German multimodal rheumatologic complex treatment for spondyloarthritis

Clinical Rheumatology - Multimodal rheumatologic complex treatment (MRCT) is a specific concept of German inpatient care focusing on physical therapy for patients with rheumatic diseases suffering...     

Generalizability of machine learning for classification of schizophrenia based on resting‐state functional MRI data

Machine learning has increasingly been applied to classification of schizophrenia in neuroimaging research. However, direct replication studies and studies seeking to investigate generalizability are scarce. To address these issues, we assessed within‐site and between‐site generalizability of a machine learning classification framework which achieved excellent performance in a previous study using two independent resting‐state functional magnetic resonance imaging data sets collected from different sites and scanners. We established within‐site generalizability of the classification framework in the main data set using cross‐validation. Then, we trained a model in the main data set and investigated between‐site generalization in the validated data set using external validation. Finally, recognizing the poor between‐site generalization performance, we updated the unsupervised algorithm to investigate if transfer learning using additional unlabeled data were able to improve between‐site classification performance. Cross‐validation showed that the published classification procedure achieved an accuracy of 0.73 using majority voting across all selected components. External validation found a classification accuracy of 0.55 (not significant) and 0.70 (significant) using the direct and transfer learning procedures, respectively. The failure of direct generalization from one site to another demonstrates the limitation of within‐site cross‐validation and points toward the need to incorporate efforts to facilitate application of machine learning across multiple data sets. The improvement in performance with transfer learning highlights the importance of taking into account the properties of data when constructing predictive models across samples and sites. Our findings suggest that machine learning classification result based on a single study should be interpreted cautiously.