Toll is a cell surface receptor with well described roles in the developmental patterning of invertebrates and innate immunity in adult Drosophila. Mammalian toll-like receptors represent a family of Toll orthologs that function in innate immunity by recognizing molecular motifs unique to pathogens or injured tissue. One member in this family of pattern recognition receptors, toll-like receptor 3 (TLR3), recognizes viral double-stranded RNA and host mRNA. We examined the expression and function of TLRs in the nervous system and found that TLR3 is expressed in the mouse central and peripheral nervous systems and is concentrated in the growth cones of neurons. Activation of TLR3 by the synthetic ligand polyinosine:polycytidylic acid (poly I:C) or by mRNA rapidly causes growth cone collapse and irreversibly inhibits neurite extension independent of nuclear factor B. Mice lacking functional TLR3 were resistant to the neurodegenerative effects of poly I:C. Neonatal mice injected with poly I:C were found to have fewer axons exiting dorsal root ganglia and displayed related sensorimotor deficits. No effect of poly I:C was observed in mice lacking functional TLR3. Together, these findings provide evidence that an innate immune pattern recognition receptor functions autonomously in neurons to regulate axonal growth and advances a novel hypothesis that this class of receptors may contribute to injury and limited CNS regeneration.
Background and Purpose Because robotic devices record the kinematics and kinetics of human movements with high resolution, we hypothesized that robotic measures collected longitudinally in patients after stroke would bear a significant relationship to standard clinical outcome measures and, therefore, might provide superior biomarkers. Methods In patients with moderate-to-severe acute ischemic stroke, we used clinical scales and robotic devices to measure arm movement 7, 14, 21, 30, and 90 days after the event at 2 clinical sites. The robots are interactive devices that measure speed, position, and force so that calculated kinematic and kinetic parameters could be compared with clinical assessments. Results Among 208 patients, robotic measures predicted well the clinical measures (cross-validated R2 of modified Rankin scale=0.60; National Institutes of Health Stroke Scale=0.63; Fugl-Meyer=0.73; Motor Power=0.75). When suitably scaled and combined by an artificial neural network, the robotic measures demonstrated greater sensitivity in measuring the recovery of patients from day 7 to day 90 (increased standardized effect=1.47). Conclusions These results demonstrate that robotic measures of motor performance will more than adequately capture outcome, and the altered effect size will reduce the required sample size. Reducing sample size will likely improve study efficiency.
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