Functional disruptions of the brain in low back pain: A potential imaging biomarker of functional disability

Chronic low back pain (LBP) is one of the leading causes of disability worldwide. While LBP research has largely focused on the spine, many studies have demonstrated a restructuring of human brain architecture accompanying LBP and other chronic pain states. Brain imaging presents a promising source for discovering noninvasive biomarkers that can improve diagnostic and prognostication outcomes for chronic LBP. This study evaluated graph theory measures derived from brain resting-state functional connectivity (rsFC) as prospective noninvasive biomarkers of LBP. We also proposed and tested a hybrid feature selection method (Enet-subset) that combines Elastic Net and an optimal subset selection method. We collected resting-state functional MRI scans from 24 LBP patients and 27 age-matched healthy controls (HC). We then derived graph-theoretical features and trained a support vector machine (SVM) to classify patient group. The degree centrality (DC), clustering coefficient (CC), and betweenness centrality (BC) were found to be significant predictors of patient group. We achieved an average classification accuracy of 83.1%

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

Tuning the aggregation behavior of pH-responsive micelles by copolymerization

YesAmphiphilic diblock copolymers, poly(2-(diethylamino)ethyl methacrylate-co-2-(dimethylamino)ethyl methacrylate)-b-poly(2-(dimethylamino)ethyl methacrylate), P(DEAEMA-co-DMAEMA)-b-PDMAEMA with various amounts of DEAEMA have been synthesized by RAFT polymerization. Their micellization in water has been investigated by scattering measurements over a wide pH range. It appeared that the polymers self-assembled into pH sensitive star like micelles. For a given composition, when the pH is varied the extent of aggregation can be tuned reversibly by orders of magnitude. By varying the copolymer composition in the hydrophobic block, the onset and extent of aggregation were shifted with respect to pH. This class of diblock copolymer offers the possibility to select the range of stimuli-responsiveness that is useful for a given application, which can rarely be achieved with conventional diblock copolymers consisting of homopolymeric blocks.European Science Foundation (ESF), Engineering and Physical Sciences Research Council (EPSRC), BP (Firm), Birmingham Science City, Advantage West Midlands (AWM), European Regional Development Fund (ERDF

Complementary light scattering and synchrotron small-angle X-ray scattering studies of the micelle-to-unimer transition of polysulfobetaines

YesAB and ABA di- and triblock copolymers where A is the hydrophilic poly(oligoethylene glycol methacrylate) (POEGMA) block and B is a thermo-responsive sulfobetaine block [2-(methacryloyloxy) ethyl] dimethyl-(3-sulfopropyl) ammonium hydroxide (PDMAPS) were synthesised by aqueous RAFT polymerisation with narrow dispersity (ĐM ≤ 1.22), as judged by aqueous SEC analysis. The di- and triblock copolymers self-assembled in salt-free water to form micelles with a PDMAPS core and the self-assembly of these polymers was explored by SLS and TEM analysis. The micelles were shown, by DLS analysis, to undergo a micelle-to-unimer transition at a critical temperature, which was dependent upon the length of the POEGMA block. Increasing the length of the third, POEGMA, block decreased the temperature at which the micelle-to-unimer transition occurred as a result of the increased hydrophilicity of the polymer. The dissociation of the micelles was further studied by SLS and synchrotron SAXS. SAXS analysis revealed that the micelle dissociation began at temperatures below that indicated by DLS analysis and that both micelles and unimers coexist. This highlights the importance of using multiple complementary techniques in the analysis of self-assembled structures. In addition the micelle-to-unimer morphology transition was employed to encapsulate and release a hydrophobic dye, Nile Red, as shown by fluorescence spectroscopy.Engineering and Physical Sciences Research Council (EPSRC), University of Warwic

Social learning in a policy-mandated collaboration: community wildfire protection planning in the eastern United States

Policies such as the US Healthy Forests Restoration Act (HFRA) mandate collaboration in planning to create benefits such as social learning and shared understanding among partners. However, some question the ability of top-down policy to foster successful local collaboration. Through in-depth interviews and document analysis, this paper investigates social learning and transformative learning in three case studies of Community Wildfire Protection Planning (CWPP), a policy-mandated collaboration under HFRA. Not all CWPP groups engaged in social learning. Those that did learned most about organisational priorities and values through communicative learning. Few participants gained new skills or knowledge through instrumental learning. CWPP groups had to commit to learning, but the design of the collaborative-mandate influenced the type of learning that was most likely to occur. This research suggests a potential role for top-down policy in setting the structural context for learning at the local level, but also confirms the importance of collaborative context and process in fostering social learning.social learning, mandated collaboration, collaborative planning, wildfire planning, wildfire policy,

Community Wildfire Protection Plans: Enhancing Collaboration and Building Social Capacity

The Healthy Forest Restoration Act of 2003 (HFRA) was enacted to reduce wildfire risk to communities and other at-risk lands through a collaborative process of planning, prioritizing and implementing hazardous fuel reduction projects. One of the key features of HFRA is the development of community wildfire protection plans (CWPPs). We studied the development of CWPPs in order to identify those factors and processes that consistently lead to effective collaborative fire and fuels management as defined by HFRA, and enhance local social capacity to sustain wildfire protection activities into the future. Findings from this research highlight the importance of: (1) drawing on local knowledge and skills; (2) building learning communities; (3) accessing networks and involving intermediaries; and (4) building on local capacities and developing new capacities to successful wildfire planning

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