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NELL-1 is a secreted, osteoinductive protein whose expression rheostatically controls skeletal ossification. Overexpression of NELL-1 results in craniosynostosis in humans and mice, whereas lack of Nell-1 expression is associated with skeletal undermineralization. Here we show that Nell-1 -haploinsufficient mice have normal skeletal development but undergo age-related osteoporosis, characterized by a reduction in osteoblast:osteoclast (OB:OC) ratio and increased bone fragility. Recombinant NELL-1 binds to integrin β1 and consequently induces Wnt/β-catenin signalling, associated with increased OB differentiation and inhibition of OC-directed bone resorption. Systemic delivery of NELL-1 to mice with gonadectomy-induced osteoporosis results in improved bone mineral density. When extended to a large animal model, local delivery of NELL-1 to osteoporotic sheep spine leads to significant increase in bone formation. Altogether, these findings suggest that NELL-1 deficiency plays a role in osteoporosis and demonstrate the potential utility of NELL-1 as a combination anabolic/antiosteoclastic therapeutic for bone loss. The growth factor NELL-1 induces bone formation during development, but its role in osteoporosis is unknown. This study shows that NELL-1 binding to integrin ß1 induces Wnt/ß-catenin signalling in the bone and restores bone mineral density in osteoporotic mice and sheep, suggesting the therapeutic potential of NELL-1 for the treatment of bone loss.

A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-20933-x .

Synovial chondromatosis is a rare condition of metaplastic cartilage development in the synovial membrane of joints. These foci can form free bodies and lead to mechanical arthrosis. The precise etiology is unknown. All synovial joints can be affected, with the hip and knee being most common. Patients with synovial chondromatosis are usually in their fifth decade and typically present with mechanical pain and diffuse swelling. Due to its rarity and nonspecific manifestations, diagnosis of synovial chondromatosis is often delayed until significant mechanical damage and arthrosis has occurred. Controversy exists regarding surgical treatment of synovial chondromatosis, especially with regard to the hip. Localized disease can be treated with resection of affected synovium (possibly arthroscopically), whereas patients with generalized disease require complete, open synovectomy. With articular damage, total hip replacement has been advocated along with synovectomy. This article presents a case of a young man with painful hip synovial chondromatosis who was successfully treated with hip resurfacing and synovectomy after failed hip arthroscopy. Hip resurfacing provides the surgeon with the opportunity to address not only local or generalized synovial disease, but articular damage as well. Furthermore, its bone-preserving properties make this option attractive in the management of this younger patient population. We feel resurfacing has a role in the surgical treatment of synovial chondromatosis. To our knowledge, this is the first case of hip resurfacing arthroplasty for the definitive treatment of synovial chondromatosis.

Tandem scanning confocal microscopy (TSCM) is a noninvasive form of vital microscopy that can be used to evaluate superficial uriniferous tubules in living kidneys. Because TSCM has a number of advantages over conventional microscopic examination of renal biopsies, the present study was undertaken to determine whether the histopathological images obtained by TSCM correlate with post-transplant renal function. The kidneys of New Zealand male rabbits were harvested, flushed with Euro-Collins solution, and stored at 0–2°C for periods of 24, 48, 67 and 72 h prior to transplantation. TSCM observation of the kidneys prior to their transplantation revealed characteristic histopathological changes of the superficial proximal convoluted tubules that correlated closely with subsequent post-transplant renal function. These observations indicate that TSCM may be of significant value in evaluating the status of donor kidneys prior to their transplantation.

Using deep learning techniques, anomalies in the paranasal sinus system can be detected automatically in MRI images and can be further analyzed and classified based on their volume, shape and other parameters like local contrast. However due to limited training data, traditional supervised learning methods often fail to generalize. Existing deep learning methods in paranasal anomaly classification have been used to diagnose at most one anomaly. In our work, we consider three anomalies. Specifically, we employ a 3D CNN to separate maxillary sinus volumes without anomalies from maxillary sinus volumes with anomalies. To learn robust representations from a small labelled dataset, we propose a novel learning paradigm that combines contrastive loss and cross-entropy loss. Particularly, we use a supervised contrastive loss that encourages embeddings of maxillary sinus volumes with and without anomaly to form two distinct clusters while the cross-entropy loss encourages the 3D CNN to maintain its discriminative ability. We report that optimising with both losses is advantageous over optimising with only one loss. We also find that our training strategy leads to label efficiency. With our method, a 3D CNN classifier achieves an AUROC of 0.85 while a 3D CNN classifier optimised with cross-entropy loss achieves an AUROC of 0.66.