Fc-gamma receptors are not involved in cartilage damage during experimental osteoarthritis

OBJECTIVE

Fc-gamma receptors (FcγRs) have been shown to play a crucial role in cartilage degradation during experimental arthritis. Although most of their effect on cartilage degradation has been attributed to their potential to promote inflammation in the presence of immunoglobulins, activating FcγRs promote cartilage degeneration in antigen-induced arthritis (AIA) independently of the level of inflammation. This prompted us to investigate, whether FcγRs may also play a role in osteoarthritis (OA)-related cartilage degradation.

METHODS

FcγR expression was measured by RT-PCR and FACS in murine cartilage tissue and chondrocytes. Experimental OA was induced by destabilisation of the medial meniscus (DMM) in WT mice and animals lacking either activating (Fc receptor γ-chain-deficient) or inhibitory (FcγRIIB-deficient) FcγRs. Cartilage damage was investigated histologically 8 weeks post-surgery by assessing proteoglycan loss and structural damage according to OARSI recommendations. Osteophyte size was measured to investigate alterations in bone turnover.

RESULTS

Expression analyses revealed significant levels for all four types of murine FcγRs in mouse chondrocytes and cartilage tissue from newborn and 8-week-old mice. Surprisingly, yet, ablation of either activating or inhibitory FcγRs did not affect cartilage damage or bone turnover during DMM-induced OA in mice.

CONCLUSION

While FcγRs appear to have a crucial role in cartilage degradation during inflammatory arthritis our data indicate that FcγRs do not influence cartilage destruction in experimental OA. This indicates that a certain threshold of inflammation is a prerequisite for FcγR-induced cartilage destruction in arthritis.

RECIPE COMPLETION USING MACHINE LEARNING TECHNIQUES

Completing a recipe is a non-trivial task, as the success of ingredient combinations depends on a multitude of factors such as taste, smell, texture, etc. The aim of our work is to build a model that adds one or more ingredients to a given number of ingredients. The idea is based on leftover ingredients in a fridge. A person could list the available ingredients in his or her fridge and the model would suggest some additional ingredients to create a full recipe.

Identification of Functionally Related Enzymes by Learning-to-Rank Methods

Enzyme sequences and structures are routinely used in the biological sciences as queries to search for functionally related enzymes in online databases. To this end, one usually departs from some notion of similarity, comparing two enzymes by looking for correspondences in their sequences, structures or surfaces. For a given query, the search operation results in a ranking of the enzymes in the database, from very similar to dissimilar enzymes, while information about the biological function of annotated database enzymes is ignored. In this work, we show that rankings of that kind can be substantially improved by applying kernel-based learning algorithms. This approach enables the detection of statistical dependencies between similarities of the active cleft and the biological function of annotated enzymes. This is in contrast to search-based approaches, which do not take annotated training data into account. Similarity measures based on the active cleft are known to outperform sequence-based or structure-based measures under certain conditions. We consider the Enzyme Commission (EC) classification hierarchy for obtaining annotated enzymes during the training phase. The results of a set of sizeable experiments indicate a consistent and significant improvement for a set of similarity measures that exploit information about small cavities in the surface of enzymes.

Performance validation of deformable image registration in the pelvic region

Patients undergoing radiotherapy will inevitably show anatomical changes during the course of treatment. These can be weight loss, tumour shrinkage, and organ motion or filling changes. For advanced and adaptive radiotherapy (ART) information about anatomical changes must be extracted from repeated images in order to be able to evaluate and manage these changes. Deformable image registration (DIR) is a tool that can be used to efficiently gather information about anatomical changes. The aim of the present study was to evaluate the performance of two DIR methods for automatic organ at risk (OAR) contour propagation. Datasets from ten gynaecological patients having repeated computed tomography (CT) and cone beam computed tomography (CBCT) scans were collected. Contours were delineated on the planning CT and on every repeated scan by an expert clinician. DIR using our in-house developed featurelet-based method and the iPlan(®) BrainLab treatment planning system software was performed with the planning CT as reference and a selection of repeated scans as the target dataset. The planning CT contours were deformed using the resulting deformation fields and compared to the manually defined contours. Dice's similarity coefficients (DSCs) were calculated for each fractional patient scan structure, comparing the volume overlap using DIR with that using rigid registration only. No significant improvement in volume overlap was found after DIR as compared with rigid registration, independent of which image modality or DIR method was used. DIR needs to be further improved in order to facilitate contour propagation in the pelvic region in ART approaches.