@article{HaaseSunkaraKohletal., author = {Haase, Tobias and Sunkara, Vikram and Kohl, Benjamin and Meier, Carola and Bußmann, Patricia and Becker, Jessica and Jagielski, Michal and von Kleist, Max and Ertel, Wolfgang}, title = {Discerning the spatio-temporal disease patterns of surgically induced OA mouse models}, series = {PLOS One}, volume = {14}, journal = {PLOS One}, number = {4}, publisher = {PLOS One}, doi = {10.1371/journal.pone.0213734}, abstract = {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.}, language = {en} } @article{RettigHaasePletnyovetal., author = {Rettig, Anika and Haase, Tobias and Pletnyov, Alexandr and Kohl, Benjamin and Ertel, Wolfgang and von Kleist, Max and Sunkara, Vikram}, title = {SLCV - A Supervised Learning - Computer Vision combined strategy for automated muscle fibre detection in cross sectional images}, series = {PeerJ}, journal = {PeerJ}, publisher = {PeerJ}, address = {PeerJ}, doi = {10.7717/peerj.7053}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-72639}, abstract = {Muscle fibre cross sectional area (CSA) is an important biomedical measure used to determine the structural composition of skeletal muscle, and it is relevant for tackling research questions in many different fields of research. To date, time consuming and tedious manual delineation of muscle fibres is often used to determine the CSA. Few methods are able to automatically detect muscle fibres in muscle fibre cross sections to quantify CSA due to challenges posed by variation of bright- ness and noise in the staining images. In this paper, we introduce SLCV, a robust semi-automatic pipeline for muscle fibre detection, which combines supervised learning (SL) with computer vision (CV). SLCV is adaptable to different staining methods and is quickly and intuitively tunable by the user. We are the first to perform an error analysis with respect to cell count and area, based on which we compare SLCV to the best purely CV-based pipeline in order to identify the contribution of SL and CV steps to muscle fibre detection. Our results obtained on 27 fluorescence-stained cross sectional images of varying staining quality suggest that combining SL and CV performs signifi- cantly better than both SL based and CV based methods with regards to both the cell separation- and the area reconstruction error. Furthermore, applying SLCV to our test set images yielded fibre detection results of very high quality, with average sensitivity values of 0.93 or higher on different cluster sizes and an average Dice Similarity Coefficient (DSC) of 0.9778.}, language = {en} }