The Open Source GAITOR Suite for Rodent Gait Analysis

Locomotive changes are often associated with disease or injury, and these changes can be quantified through gait analysis. Gait analysis has been applied to preclinical studies, providing quantitative behavioural assessment with a reasonable clinical analogue. However, available gait analysis technology for small animals is somewhat limited. Furthermore, technological and analytical challenges can limit the effectiveness of preclinical gait analysis. The Gait Analysis Instrumentation and Technology Optimized for Rodents (GAITOR) Suite is designed to increase the accessibility of preclinical gait analysis to researchers, facilitating hardware and software customization for broad applications. Here, the GAITOR Suite’s utility is demonstrated in 4 models: a monoiodoacetate (MIA) injection model of joint pain, a sciatic nerve injury model, an elbow joint contracture model, and a spinal cord injury model. The GAITOR Suite identified unique compensatory gait patterns in each model, demonstrating the software’s utility for detecting gait changes in rodent models of highly disparate injuries and diseases. Robust gait analysis may improve preclinical model selection, disease sequelae assessment, and evaluation of potential therapeutics

Predicting beneficial effects of atomoxetine and citalopram on response inhibition in Parkinson's disease with clinical and neuroimaging measures.

Recent studies indicate that selective noradrenergic (atomoxetine) and serotonergic (citalopram) reuptake inhibitors may improve response inhibition in selected patients with Parkinson's disease, restoring behavioral performance and brain activity. We reassessed the behavioral efficacy of these drugs in a larger cohort and developed predictive models to identify patient responders. We used a double-blind randomized three-way crossover design to investigate stopping efficiency in 34 patients with idiopathic Parkinson's disease after 40 mg atomoxetine, 30 mg citalopram, or placebo. Diffusion-weighted and functional imaging measured microstructural properties and regional brain activations, respectively. We confirmed that Parkinson's disease impairs response inhibition. Overall, drug effects on response inhibition varied substantially across patients at both behavioral and brain activity levels. We therefore built binary classifiers with leave-one-out cross-validation (LOOCV) to predict patients' responses in terms of improved stopping efficiency. We identified two optimal models: (1) a "clinical" model that predicted the response of an individual patient with 77-79% accuracy for atomoxetine and citalopram, using clinically available information including age, cognitive status, and levodopa equivalent dose, and a simple diffusion-weighted imaging scan; and (2) a "mechanistic" model that explained the behavioral response with 85% accuracy for each drug, using drug-induced changes of brain activations in the striatum and presupplementary motor area from functional imaging. These data support growing evidence for the role of noradrenaline and serotonin in inhibitory control. Although noradrenergic and serotonergic drugs have highly variable effects in patients with Parkinson's disease, the individual patient's response to each drug can be predicted using a pattern of clinical and neuroimaging features.The BCNI is supported by the Wellcome Trust and Medical Research Council. We are grateful to Dr Gordon Logan for advice on stop-signal reaction time estimation and to Dr Marta Correia for advice on diffusion-weighted imaging data analysis. Conflict of interest: Prof. Sahakian has received grants from Janssen/J&J, personal fees from Cambridge Cognition, personal fees from Lundbeck, and personal fees from Servier, outside the submitted work. Prof. Robbins has received personal fees and royalties from Cambridge Cognition, personal fees and grants from Eli Lilly Inc, personal fees and grants from Lundbeck, grants from GSK, personal fees from Teva Pharmaceuticals, personal fees from Shire Pharmaceuticals, grants from Medical Research Council, editorial honorarium from Springer Verlag Germany, and personal fees from Chempartners, outside the submitted work. Prof. Rowe has received grant funding from AZ-Medimmune unrelated to the current work. Dr Housden is an employee of Cambridge Cognition. Other authors reported no biomedical financial interests or potential conflict of interest.This is the final version of the article. It was first available from Wiley via http://dx.doi.org/10.1002/hbm.2308

Synthetic Biology Open Language (SBOL) Version 1.1.0

In this BioBricks Foundation Request for Comments (BBF RFC), we specify the Synthetic Biology Open Language (SBOL) Version 1.1.0 to enable the electronic exchange of information describing DNA components used in synthetic biology. We define: 1. the vocabulary, a set of preferred terms and 2. the core data model, a common computational representation

Softening of the Chronic Hemi-Section Spinal Cord Injury Scar Parallels Dysregulation of Cellular and Extracellular Matrix Content

© 2020 Elsevier Ltd Regeneration following spinal cord injury (SCI) is challenging in part due to the modified tissue composition and organization of the resulting glial and fibrotic scar regions. Inhibitory cell types and biochemical cues present in the scar have received attention as therapeutic targets to promote regeneration. However, altered Young\u27s modulus of the scar as a readout for potential impeding factors for regeneration are not as well-defined, especially in vivo. Although the decreased Young\u27s modulus of surrounding tissue at acute stages post-injury is known, the causation and outcomes at chronic time points remain largely understudied and controversial, which motivates this work. This study assessed the glial and fibrotic scar tissue\u27s Young\u27s modulus and composition (scar morphometry, cell identity, extracellular matrix (ECM) makeup) that contribute to the tissue\u27s stiffness. The spatial Young\u27s modulus of a chronic (~18-wks, post-injury) hemi-section, including the glial and fibrotic regions, were significantly less than naïve tissue (~200 Pa; p \u3c 0.0001). The chronic scar contained cystic cavities dispersed in areas of dense nuclei packing. Abundant CNS cell types such as astrocytes, oligodendrocytes, and neurons were dysregulated in the scar, while epithelial markers such as vimentin were upregulated. The key ECM components in the CNS, namely sulfated proteoglycans (sPGs), were significantly downregulated following injury with concomitant upregulation of unsulfated glycosaminoglycans (GAGs) and hyaluronic acid (HA), likely altering the foundational ECM network that contributes to tissue stiffness. Our results reveal the Young\u27s modulus of the chronic SCI scar as well as quantification of contributing elastic components that can provide a foundation for future study into their role in tissue repair and regeneration

The Open Source GAITOR Suite for Rodent Gait Analysis

Locomotive changes are often associated with disease or injury, and these changes can be quantified through gait analysis. Gait analysis has been applied to preclinical studies, providing quantitative behavioural assessment with a reasonable clinical analogue. However, available gait analysis technology for small animals is somewhat limited. Furthermore, technological and analytical challenges can limit the effectiveness of preclinical gait analysis. The Gait Analysis Instrumentation and Technology Optimized for Rodents (GAITOR) Suite is designed to increase the accessibility of preclinical gait analysis to researchers, facilitating hardware and software customization for broad applications. Here, the GAITOR Suite’s utility is demonstrated in 4 models: a monoiodoacetate (MIA) injection model of joint pain, a sciatic nerve injury model, an elbow joint contracture model, and a spinal cord injury model. The GAITOR Suite identified unique compensatory gait patterns in each model, demonstrating the software’s utility for detecting gait changes in rodent models of highly disparate injuries and diseases. Robust gait analysis may improve preclinical model selection, disease sequelae assessment, and evaluation of potential therapeutics

Tunable Keratin Hydrogels for Controlled Erosion and Growth Factor Delivery

Tunable erosion of polymeric materials is an important aspect of tissue engineering for reasons that include cell infiltration, controlled release of therapeutic agents, and ultimately to tissue healing. In general, the biological response to proteinaceous polymeric hydrogels is favorable (e.g., minimal inflammatory response). However, unlike synthetic polymers, achieving tunable erosion with natural materials is a challenge. Keratins are a class of intermediate filament proteins that can be obtained from several sources, including human hair, and have gained increasing levels of use in tissue engineering applications. An important characteristic of keratin proteins is the presence of a large number of cysteine residues. Two classes of keratins with different chemical properties can be obtained by varying the extraction techniques: (1) keratose by oxidative extraction and (2) kerateine by reductive extraction. Cysteine residues of keratose are “capped” by sulfonic acid and are unable to form covalent cross-links upon hydration, whereas cysteine residues of kerateine remain as sulfhydryl groups and spontaneously form covalent disulfide cross-links. Here, we describe a straightforward approach to fabricate keratin hydrogels with tunable rates of erosion by mixing keratose and kerateine. SEM imaging and mechanical testing of freeze-dried materials showed similar pore diameters and compressive moduli, respectively, for each keratose–kerateine mixture formulation (∼1200 kPa for freeze-dried materials and ∼1.5 kPa for hydrogels). However, the elastic modulus (<i>G</i>′) determined by rheology varied in proportion with the keratose–kerateine ratios, as did the rate of hydrogel erosion and the release rate of thiol from the hydrogels. The variation in keratose–kerateine ratios also led to tunable control over release rates of recombinant human insulin-like growth factor 1