Discerning the spatio-temporal disease patterns of surgically induced OA mouse models

Osteoarthritis (OA) is the most common cause of disability in ageing societies, with no effective therapies available to date. Two preclinical models are widely used to validate novel OA interventions (MCL-MM and DMM). Our aim is to discern disease dynamics in these models to provide a clear timeline in which various pathological changes occur. OA was surgically induced in mice by destabilisation of the medial meniscus. Analysis of OA progression revealed that the intensity and duration of chondrocyte loss and cartilage lesion formation were significantly different in MCL-MM vs DMM. Firstly, apoptosis was seen prior to week two and was narrowly restricted to the weight bearing area. Four weeks post injury the magnitude of apoptosis led to a 40–60% reduction of chondrocytes in the non-calcified zone. Secondly, the progression of cell loss preceded the structural changes of the cartilage spatio-temporally. Lastly, while proteoglycan loss was similar in both models, collagen type II degradation only occurred more prominently in MCL-MM. Dynamics of chondrocyte loss and lesion formation in preclinical models has important implications for validating new therapeutic strategies. Our work could be helpful in assessing the feasibility and expected response of the DMM- and the MCL-MM models to chondrocyte mediated therapies

Online oxygen monitoring using integrated inkjet-printed sensors in a liver-on-a-chip system

The demand for real-time monitoring of cell functions and cell conditions has dramatically increased with the emergence of organ-on-a-chip (OOC) systems. However, the incorporation of co-cultures and microfluidic channels in OOC systems increases their biological complexity and therefore makes the analysis and monitoring of analytical parameters inside the device more difficult. In this work, we present an approach to integrate multiple sensors in an extremely thin, porous and delicate membrane inside a liver-on-a-chip device. Specifically, three electrochemical dissolved oxygen (DO) sensors were inkjet-printed along the microfluidic channel allowing local online monitoring of oxygen concentrations. This approach demonstrates the existence of an oxygen gradient up to 17.5% for rat hepatocytes and 32.5% for human hepatocytes along the bottom channel. Such gradients are considered crucial for the appearance of zonation of the liver. Inkjet printing (IJP) was the selected technology as it allows drop on demand material deposition compatible with delicate substrates, as used in this study, which cannot withstand temperatures higher than 130 °C. For the deposition of uniform gold and silver conductive inks on the porous membrane, a primer layer using SU-8 dielectric material was used to seal the porosity of the membrane at defined areas, with the aim of building a uniform sensor device. As a proof-of-concept, experiments with cell cultures of primary human and rat hepatocytes were performed, and oxygen consumption rate was stimulated with carbonyl-cyanide-4-(trifluoromethoxy)phenylhydrazone (FCCP), accelerating the basal respiration of 0.23 ± 0.07 nmol s-1/106 cells up to 5.95 ± 0.67 nmol s-1/106 cells s for rat cells and the basal respiration of 0.17 ± 0.10 nmol s-1/106 cells by up to 10.62 ± 1.15 nmol s-1/106 cells for human cells, with higher oxygen consumption of the cells seeded at the outflow zone. These results demonstrate that the approach of printing sensors inside an OOC has tremendous potential because IJP is a feasible technique for the integration of different sensors for evaluating metabolic activity of cells, and overcomes one of the major challenges still remaining on how to tap the full potential of OOC systems.Peer ReviewedPostprint (author's final draft

An Activity-Based Nanosensor for Traumatic Brain Injury.

Currently, traumatic brain injury (TBI) is detected by medical imaging; however, medical imaging requires expensive capital equipment, is time- and resource-intensive, and is poor at predicting patient prognosis. To date, direct measurement of elevated protease activity has yet to be utilized to detect TBI. In this work, we engineered an activity-based nanosensor for TBI (TBI-ABN) that responds to increased protease activity initiated after brain injury. We establish that a calcium-sensitive protease, calpain-1, is active in the injured brain hours within injury. We then optimize the molecular weight of a nanoscale polymeric carrier to infiltrate into the injured brain tissue with minimal renal filtration. A calpain-1 substrate that generates a fluorescent signal upon cleavage was attached to this nanoscale polymeric carrier to generate an engineered TBI-ABN. When applied intravenously to a mouse model of TBI, our engineered sensor is observed to locally activate in the injured brain tissue. This TBI-ABN is the first demonstration of a sensor that responds to protease activity to detect TBI

Estimation of time delay by coherence analysis

Using coherence analysis (which is an extensively used method to study the correlations in frequency domain, between two simultaneously measured signals) we estimate the time delay between two signals. This method is suitable for time delay estimation of narrow band coherence signals for which the conventional methods cannot be reliably applied. We show by analysing coupled R\"ossler attractors with a known delay, that the method yields satisfactory results. Then, we apply this method to human pathologic tremor. The delay between simultaneously measured traces of Electroencephalogram (EEG) and Electromyogram (EMG) data of subjects with essential hand tremor is calculated. We find that there is a delay of 11-27 milli-seconds ($ms$) between the tremor correlated parts (cortex) of the brain (EEG) and the trembling hand (EMG) which is in agreement with the experimentally observed delay value of 15 $ms$ for the cortico-muscular conduction time. By surrogate analysis we calculate error-bars of the estimated delay.Comment: 21 pages, 8 figures, elstart.cls file included. Accepted for publication in Physica

Inferring Trajectories of Psychotic Disorders Using Dynamic Causal Modeling

INTRODUCTION: Illness course plays a crucial role in delineating psychiatric disorders. However, existing nosologies consider only its most basic features (e.g., symptom sequence, duration). We developed a Dynamic Causal Model (DCM) that characterizes course patterns more fully using dense timeseries data. This foundational study introduces the new modeling approach and evaluates its validity using empirical and simulated data. METHODS: A three-level DCM was constructed to model how latent dynamics produce symptoms of depression, mania, and psychosis. This model was fit to symptom scores of nine patients collected prospectively over four years, following first hospitalization. Simulated subjects based on these empirical data were used to evaluate model parameters at the subject-level. At the group-level, we tested the accuracy with which the DCM can estimate the latent course patterns using Parametric Empirical Bayes (PEB) and leave-one-out cross-validation. RESULTS: Analyses of empirical data showed that DCM accurately captured symptom trajectories for all nine subjects. Simulation results showed that parameters could be estimated accurately (correlations between generative and estimated parameters >= 0.76). Moreover, the model could distinguish different latent course patterns, with PEB correctly assigning simulated patients for eight of nine course patterns. When testing any pair of two specific course patterns using leave-one-out cross-validation, 30 out of 36 pairs showed a moderate or high out-of-samples correlation between the true group-membership and the estimated group-membership values. CONCLUSION: DCM has been widely used in neuroscience to infer latent neuronal processes from neuroimaging data. Our findings highlight the potential of adopting this methodology for modeling symptom trajectories to explicate nosologic entities, temporal patterns that define them, and facilitate personalized treatment

Concerns, challenges and promises of high-content analysis of 3D cellular models

Peer reviewe

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 197, September 1979

This bibliography lists 193 reports, articles, and other documents introduced into the NASA scientific and technical information system in August 1979