A systematic review demonstrating correlation of MRI compositional parameters with clinical outcomes following articular cartilage repair interventions in the knee.

Objective: Compositional-MRI parameters enable the assessment of cartilage ultrastructure. Correlation of these parameters with clinical outcomes is unclear. This systematic review investigated the correlation of various compositional- MRI parameters with clinical outcome measures following cartilage repair or regeneration interventions in the knee. Design: This study was registered with PROSPERO and reported in accordance with PRISMA. PubMed, Institute of Science Index, Scopus, Cochrane Central Register of Controlled Trials, and Embase databases were searched. All studies, regardless of type, that presented correlation of compositional- MRI parameters with clinical outcome measures were included. Two researchers independently performed data extraction and QUADAS-2 analysis. Compositional-MRI parameter change following intervention and correlation with clinical outcome measures were evaluated. Results: 19 studies were included. Risk of bias was generally low. 5 different compositional parameters were observed from the included studies. However, due to the significant variability in the reporting of compositional-MRI parameters across studies, meta-analyses were possible only for T2 values and T2 index values (T2 value of repair cartilage relative to normal cartilage). Correlation of T2 values of repair cartilage with clinical outcome score was r â€‹= â€‹0.33 [0.15, 0.52]. Correlation of T2 index with clinical outcome score was r â€‹= â€‹0.52 [0.32, 0.77]. Conclusions: Correlation between T2 values and clinical outcome scores following knee cartilage repair were found. The heterogeneity of the correlations extracted from the included studies limited the scope for the meta-analysis. Thus, standardised, high-quality studies are required for better assessment of correlation between compositional MRI parameters and clinical outcome measures after cartilage repair. Registration number: PROSPERO CRD42021287364.Study protocol available on PROSPERO website.

A biomimetic self-assembling peptide promotes bone regeneration in vivo: A rat cranial defect study.

Rationally designed, pH sensitive self-assembling ß-peptides (SAPs) which are capable of reversibly switching between fluid and gel phases in response to environmental triggers are potentially useful injectable scaffolds for skeletal tissue engineering applications. SAP P11-4 (CH3COQQRFEWEFEQQNH2) has been shown to nucleate hydroxyapatite mineral de novo and has been used in dental enamel regeneration. We hypothesised that addition of mesenchymal stromal cells (MSCs) would enhance the in vivo effects of P11-4 in promoting skeletal tissue repair. Cranial defects were created in athymic rats and filled with either Bio-Oss® (anorganic bone chips) or P11-4 ±â€¯human dental pulp stromal cells (HDPSCs). Unfilled defects served as controls. After 4 weeks, only those defects filled with P11-4 alone showed significantly increased bone regeneration (almost complete healing), compared to unfilled control defects, as judged using quantitative micro-CT, histology and immunohistochemistry. In silico modelling indicated that fibril formation may be essential for any mineral nucleation activity. Taken together, these data suggest that self-assembling peptides are a suitable scaffold for regeneration of bone tissue in a one step, cell-free therapeutic approach.

Histopathology in 3D: From three-dimensional reconstruction to multi-stain and multi-modal analysis.

Light microscopy applied to the domain of histopathology has traditionally been a two-dimensional imaging modality. Several authors, including the authors of this work, have extended the use of digital microscopy to three dimensions by stacking digital images of serial sections using image-based registration. In this paper, we give an overview of our approach, and of extensions to the approach to register multi-modal data sets such as sets of interleaved histopathology sections with different stains, and sets of histopathology images to radiology volumes with very different appearance. Our approach involves transforming dissimilar images into a multi-channel representation derived from co-occurrence statistics between roughly aligned images.