Autologous chondrocyte implantation-derived synovial fluids display distinct responder and non-responder proteomic profiles

Hulme, Charlotte H. & Wilson, Emma L. - Equal contributorsBackground Autologous chondrocyte implantation (ACI) can be used in the treatment of focal cartilage injuries to prevent the onset of osteoarthritis (OA). However, we are yet to understand fully why some individuals do not respond well to this intervention. Identification of a reliable and accurate biomarker panel that can predict which patients are likely to respond well to ACI is needed in order to assign the patient to the most appropriate therapy. This study aimed to compare the baseline and mid-treatment proteomic profiles of synovial fluids (SFs) obtained from responders and non-responders to ACI. Methods SFs were derived from 14 ACI responders (mean Lysholm improvement of 33 (17–54)) and 13 non-responders (mean Lysholm decrease of 14 (4–46)) at the two stages of surgery (cartilage harvest and chondrocyte implantation). Label-free proteome profiling of dynamically compressed SFs was used to identify predictive markers of ACI success or failure and to investigate the biological pathways involved in the clinical response to ACI. Results Only 1 protein displayed a ≥2.0-fold differential abundance in the preclinical SF of ACI responders versus non-responders. However, there is a marked difference between these two groups with regard to their proteome shift in response to cartilage harvest, with 24 and 92 proteins showing ≥2.0-fold differential abundance between Stages I and II in responders and non-responders, respectively. Proteomic data has been uploaded to ProteomeXchange (identifier: PXD005220). We have validated two biologically relevant protein changes associated with this response, demonstrating that matrix metalloproteinase 1 was prominently elevated and S100 calcium binding protein A13 was reduced in response to cartilage harvest in non-responders. Conclusions The differential proteomic response to cartilage harvest noted in responders versus non-responders is completely novel. Our analyses suggest several pathways which appear to be altered in non-responders that are worthy of further investigation to elucidate the mechanisms of ACI failure. These protein changes highlight many putative biomarkers that may have potential for prediction of ACI treatment success

Experimental study of dense pyroclastic density currents using sustained, gas-fluidized granular flows

© 2014, Springer-Verlag Berlin Heidelberg. We present the results of laboratory experiments on the behaviour of sustained, dense granular flows in a horizontal flume, in which high-gas pore pressure was maintained throughout the flow duration by continuous injection of gas through the flume base. The flows were fed by a sustained (0.5–30 s) supply of fine (75 ± 15 μm) particles from a hopper; the falling particles impacted an impingement surface at concentrations of ~3 to 45 %, where they densified rapidly to generate horizontally moving, dense granular flows. When the gas supplied through the flume base was below the minimum fluidization velocity of the particles (i.e. aerated flow conditions), three flow phases were identified: (i) an initial dilute spray of particles travelling at 1–2 m s−1, followed by (ii) a dense granular flow travelling at 0.5–1 m s−1, then by (iii) sustained aggradation of the deposit by a prolonged succession of thin flow pulses. The maximum runout of the phase 2 flow was linearly dependent on the initial mass flux, and the frontal velocity had a square-root dependence on mass flux. The frontal propagation speed during phase 3 had a linear relationship with mass flux. The total mass of particles released had no significant control on either flow velocity or runout in any of the phases. High-frequency flow unsteadiness during phase 3 generated deposit architectures with progradational and retrogradational packages and multiple internal erosive contacts. When the gas supplied through the flume base was equal to the minimum fluidization velocity of the particles (i.e. fluidized flow conditions), the flows remained within phase 2 for their entire runout, no deposit formed and the particles ran off the end of the flume. Sustained granular flows differ significantly from instantaneous flows generated by lock-exchange mechanisms, in that the sustained flows generate (by prolonged progressive aggradation) deposits that are much thicker than the flowing layer of particles at any given moment. The experiments offer a first attempt to investigate the physics of the sustained pyroclastic flows that generate thick, voluminous ignimbrites

A chromosome conformation capture ordered sequence of the barley genome

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In silico toxicology protocols

The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information