Nerve growth factor (NGF) is involved in the development and maintenance of the nervous system. NGF binds with high affinity to the extracellular region of the tyrosine kinase receptor TrkA. This domain comprises leucine and cysteine rich motifs, followed by two immunoglobulin like (Ig-like) domains. We describe the expression and purification of recombinant Ig-like domains. Fluorescence and circular dichroism spectroscopy show that the protein is folded into a compact globular structure and contains mainly beta-sheet secondary structure. Recombinant protein binds to NGF and can inhibit NGF bioactivity both in vitro and in vivo.

Our findings suggest that medical schools may be currently failing to ensure that medical students have a basic competence in musculoskeletal medicine. Further investigation is warranted to fully assess the current training provided by U.K. medical schools in musculoskeletal medicine, and appropriate steps must be taken to improve the quantity and quality of training in musculoskeletal medicine in the United Kingdom.

Background: Meniscal allograft transplantation (MAT) may improve symptoms and function, and may limit premature knee degeneration in patients with symptomatic meniscal loss. The aim of this retrospective study was to examine patient outcomes after MAT and to explore the different potential definitions of 'success' and 'failure'. Methods: Sixty patients who underwent MAT between 2008 and 2014, aged 18-50 were identified. Six validated outcome measures for knee pathologies, patient satisfaction and return to sport were incorporated into a questionnaire. Surgical failure (removal of most/all the graft, revision MAT or conversion to arthroplasty), clinical failure (Lysholm < 65), complication rates (surgical failure plus repeat arthroscopy for secondary allograft tears) and whether patients would have the procedure again were recorded. Statistics analysis included descriptive statistics, with patient-reported outcome measures reported as median and range. A binomial logistic regression was performed to assess factors contributing to failure. Results: Forty-three patients (72%) responded, mean age 35.6 (±7.5). 72% required concomitant procedures, and 44% had Outerbridge III or IV chondral damage. The complication rate was 21% (9). At mean follow-up of 3.4 (±1.6) years, 9% (4) were surgical failures and 21% (9) were clinical failures. Half of those patients considered a failure stated they would undergo MAT again. In the 74% (32) reporting they would undergo MAT again, median KOOS, IKDC and Lysholm scores were 82.1, 62.1 and 88, compared to 62.2, 48.5 and 64 in patients who said they would not. None of the risk factors significantly contributed to surgical or clinical failure, although female gender and number of concomitant procedures were nearly significant. Following MAT, 40% were dissatisfied with type/level of sport achieved, but only 14% would not consider MAT again. Conclusions: None of the risk factors examined were linked to surgical or clinical failure. Whilst less favourable outcomes are seen with Outerbridge Grade IV, these patients should not be excluded from potential MAT. Inability to return to sport is not associated with failure since 73% of these patients would undergo MAT again. The disparity between 'clinical failure' and 'surgical failure' outcomes means these terms may need redefining using a specific/ bespoke MAT scoring system.

Aims Total hip arthroplasty (THA) and total knee arthroplasty (TKA) are common orthopaedic procedures requiring postoperative radiographs to confirm implant positioning and identify complications. Artificial intelligence (AI)-based image analysis has the potential to automate this postoperative surveillance. The aim of this study was to prepare a scoping review to investigate how AI is being used in the analysis of radiographs following THA and TKA, and how accurate these tools are. Methods The Embase, MEDLINE, and PubMed libraries were systematically searched to identify relevant articles. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews and Arksey and O’Malley framework were followed. Study quality was assessed using a modified Methodological Index for Non-Randomized Studies tool. AI performance was reported using either the area under the curve (AUC) or accuracy. Results Of the 455 studies identified, only 12 were suitable for inclusion. Nine reported implant identification and three described predicting risk of implant failure. Of the 12, three studies compared AI performance with orthopaedic surgeons. AI-based implant identification achieved AUC 0.992 to 1, and most algorithms reported an accuracy > 90%, using 550 to 320,000 training radiographs. AI prediction of dislocation risk post-THA, determined after five-year follow-up, was satisfactory (AUC 76.67; 8,500 training radiographs). Diagnosis of hip implant loosening was good (accuracy 88.3%; 420 training radiographs) and measurement of postoperative acetabular angles was comparable to humans (mean absolute difference 1.35° to 1.39°). However, 11 of the 12 studies had several methodological limitations introducing a high risk of bias. None of the studies were externally validated. Conclusion These studies show that AI is promising. While it already has the ability to analyze images with significant precision, there is currently insufficient high-level evidence to support its widespread clinical use. Further research to design robust studies that follow standard reporting guidelines should be encouraged to develop AI models that could be easily translated into real-world conditions. Cite this article: Bone Joint J 2022;104-B(8):929–937.

There are advocates of both two-dimensional (2D) and three-dimensional (3D) templating methods for planning total hip replacement. The aim of this study was to compare the accuracy of implant size prediction when using 2D and 3D templating methods for total hip arthroplasty, as well as to compare the inter- and intra-observer reliability in order to determine whether currently available methods are sufficiently reliable and reproducible. Medline, EMBASE and PubMed were searched to identify studies that compared the accuracy of 2D and 3D templating for total hip replacement. Results were screened using the PRISMA flowchart and included studies were assessed for their level of evidence using the Oxford CEBM criteria. Non-randomized trials were critically appraised using the MINORS tool, whilst randomized trials were assessed using the CASP RCT checklist. A series of meta-analyses of the data for accuracy were also conducted. Ten studies reported that 3D templating is an accurate and reliable method of templating for total hip replacement. Six studies compared 3D templating with 2D templating, all of which concluded that 3D templating was more accurate, with three finding a statistically significant difference. The meta-analyses showed that 3D CT templating is the most accurate method. This review supports the hypothesis that 3D templating is an accurate and reliable method of preoperative planning, which is more accurate than 2D templating for predicting implant size. However, further research is needed to ascertain the significance of this improved accuracy and whether it will yield any clinical benefit.