Osteosarcoma (OSA) is a rare, but disproportionately lethal cancer that predominantly affects children. Sadly, discovery of new therapies for OSA has largely been unsuccessful in the past 30 years; there is an urgent need to identify new treatments for OSA. Pet dogs with naturally-occurring OSA represent a unique comparative “model” to discover new treatments for OSA. Unlike humans, in which fewer than 1,000 cases of OSA occur each year, there are nearly 50,000 new cases each year of OSA in dogs. In addition, dogs have an intact immune system, a shared environment with humans, and more rapid progression of disease. Together these factors make dogs an important comparative model for new therapies for OSA. The purpose of this study was: 1) to validate this mouse-dog-human pipeline for drug discovery and 2) to validate CRM1 as a novel target for ostesoarcoma treatment. We developed patient-derived cell lines and xenografts of OSA from both dogs and humans and applied these models to identify new therapies for OSA using high-throughput drug screens in vitro followed by in vivo validation. Whole exome sequencing was performed on the patient-derived models and original tumors to identify potential driver mutations. A high-throughput screen in both dog and human OSA identified CRM1 inhibitors as effective at killing dog and human OSA patient-derived cell lines in vitro. In vivo, CRM1 inhibition led to significant tumor growth inhibition in patient-derived xenografts from dogs and humans. Western blotting demonstrated increased levels of CRM1 protein expression across nine different dog and human OSA cell lines compared to non-transformed human osteoblasts. CRM1 upregulation in OSA cells was further verified by immunofluorescence staining. Increased CRM1 expression was prognostic for poorer metastasis-free survival and poorer overall survival. Our cross-species personalized medicine pipeline identified CRM1 as a potential therapeutic target to treat OSA in both dogs and humans. Future studies are focused on testing CRM1 inhibitors in canine clinical trials.
Understanding spinopelvic mechanics is important for the success of total hip arthroplasty (THA). Despite significant advancements in appreciating spinopelvic balance, numerous challenges remain. It is crucial to recognize the individual variability and postoperative changes in spinopelvic parameters and their consequential impact on prosthetic component positioning to mitigate the risk of dislocation and enhance postoperative outcomes. This review describes the integration of advanced diagnostic approaches, enhanced technology, implant considerations, and surgical planning, all tailored to the unique anatomy and biomechanics of each patient. It underscores the importance of accurately predicting postoperative spinopelvic mechanics, selecting suitable imaging techniques, establishing a consistent nomenclature for spinopelvic stiffness, and considering implant-specific strategies. Furthermore, it highlights the potential of artificial intelligence to personalize care. Cite this article:
Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials. A systematic review was conducted in accordance with PRISMA guidelines. We searched MEDLINE and Embase on 5 May 2024 for studies utilizing ML to predict OA progression. Titles and abstracts were independently screened, followed by full-text reviews for studies that met the eligibility criteria. Key information was extracted and synthesized for analysis, including types of data (such as clinical, radiological, or biochemical), definitions of OA progression, ML algorithms, validation methods, and outcome measures.Aims
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The impact of a diaphyseal femoral deformity on knee alignment varies according to its severity and localization. The aims of this study were to determine a method of assessing the impact of diaphyseal femoral deformities on knee alignment for the varus knee, and to evaluate the reliability and the reproducibility of this method in a large cohort of osteoarthritic patients. All patients who underwent a knee arthroplasty from 2019 to 2021 were included. Exclusion criteria were genu valgus, flexion contracture (> 5°), previous femoral osteotomy or fracture, total hip arthroplasty, and femoral rotational disorder. A total of 205 patients met the inclusion criteria. The mean age was 62.2 years (SD 8.4). The mean BMI was 33.1 kg/m2 (SD 5.5). The radiological measurements were performed twice by two independent reviewers, and included hip knee ankle (HKA) angle, mechanical medial distal femoral angle (mMDFA), anatomical medial distal femoral angle (aMDFA), femoral neck shaft angle (NSA), femoral bowing angle (FBow), the distance between the knee centre and the top of the FBow (DK), and the angle representing the FBow impact on the knee (C’KS angle).Aims
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