5 research outputs found
Multi-modal biomarkers of low back pain: A machine learning approach
Chronic low back pain (LBP) is a very common health problem worldwide and a major cause of disability. Yet, the lack of quantifiable metrics on which to base clinical decisions leads to imprecise treatments, unnecessary surgery and reduced patient outcomes. Although, the focus of LBP has largely focused on the spine, the literature demonstrates a robust reorganization of the human brain in the setting of LBP. Brain neuroimaging holds promise for the discovery of biomarkers that will improve the treatment of chronic LBP. In this study, we report on morphological changes in cerebral cortical thickness (CT) and resting-state functional connectivity (rsFC) measures as potential brain biomarkers for LBP. Structural MRI scans, resting state functional MRI scans and self-reported clinical scores were collected from 24 LBP patients and 27 age-matched healthy controls (HC). The results suggest widespread differences in CT in LBP patients relative to HC. These differences in CT are correlated with self-reported clinical summary scores, the Physical Component Summary and Mental Component Summary scores. The primary visual, secondary visual and default mode networks showed significant age-corrected increases in connectivity with multiple networks in LBP patients. Cortical regions classified as hubs based on their eigenvector centrality (EC) showed differences in their topology within motor and visual processing regions. Finally, a support vector machine trained using CT to classify LBP subjects from HC achieved an average classification accuracy of 74.51%, AUC = 0.787 (95% CI: 0.66-0.91). The findings from this study suggest widespread changes in CT and rsFC in patients with LBP while a machine learning algorithm trained using CT can predict patient group. Taken together, these findings suggest that CT and rsFC may act as potential biomarkers for LBP to guide therapy
ORAI1 and ORAI2 modulate murine neutrophil calcium signaling, cellular activation, and host defense.
Multi-modal biomarkers of low back pain: A machine learning approach
Chronic low back pain (LBP) is a very common health problem worldwide and a major cause of disability. Yet, the lack of quantifiable metrics on which to base clinical decisions leads to imprecise treatments, unnecessary surgery and reduced patient outcomes. Although, the focus of LBP has largely focused on the spine, the literature demonstrates a robust reorganization of the human brain in the setting of LBP. Brain neuroimaging holds promise for the discovery of biomarkers that will improve the treatment of chronic LBP. In this study, we report on morphological changes in cerebral cortical thickness (CT) and resting-state functional connectivity (rsFC) measures as potential brain biomarkers for LBP. Structural MRI scans, resting state functional MRI scans and self-reported clinical scores were collected from 24 LBP patients and 27 age-matched healthy controls (HC). The results suggest widespread differences in CT in LBP patients relative to HC. These differences in CT are correlated with self-reported clinical summary scores, the Physical Component Summary and Mental Component Summary scores. The primary visual, secondary visual and default mode networks showed significant age-corrected increases in connectivity with multiple networks in LBP patients. Cortical regions classified as hubs based on their eigenvector centrality (EC) showed differences in their topology within motor and visual processing regions. Finally, a support vector machine trained using CT to classify LBP subjects from HC achieved an average classification accuracy of 74.51%, AUC = 0.787 (95% CI: 0.66-0.91). The findings from this study suggest widespread changes in CT and rsFC in patients with LBP while a machine learning algorithm trained using CT can predict patient group. Taken together, these findings suggest that CT and rsFC may act as potential biomarkers for LBP to guide therapy
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ORAI1 and ORAI2 modulate murine neutrophil calcium signaling, cellular activation, and host defense
Calcium signals are initiated in immune cells by the process of store-operated calcium entry (SOCE), where receptor activation triggers transient calcium release from the endoplasmic reticulum, followed by opening of plasma-membrane calcium-release activated calcium (CRAC) channels. ORAI1, ORAI2, and ORAI3 are known to comprise the CRAC channel; however, the contributions of individual isoforms to neutrophil function are not well understood. Here, we show that loss of ORAI1 partially decreases calcium influx, while loss of both ORAI1 and ORAI2 completely abolishes SOCE. In other immune-cell types, loss of ORAI2 enhances SOCE. In contrast, we find that ORAI2-deficient neutrophils display decreased calcium influx, which is correlated with measurable differences in the regulation of neutrophil membrane potential via KCa3.1. Decreased SOCE in ORAI1-, ORAI2-, and ORAI1/2-deficient neutrophils impairs multiple neutrophil functions, including phagocytosis, degranulation, leukotriene, and reactive oxygen species (ROS) production, rendering ORAI1/2-deficient mice highly susceptible to staphylococcal infection. This study demonstrates that ORAI1 and ORAI2 are the primary components of the neutrophil CRAC channel and identifies subpopulations of neutrophils where cell-membrane potential functions as a rheostat to modulate the SOCE response. These findings have implications for mechanisms that modulate neutrophil function during infection, acute and chronic inflammatory conditions, and cancer