Musculoskeletal conditions include more than 150 diagnoses that affect the locomotor system. These conditions are characterized by pain and reduced physical function, often leading to significant mental health decline, increased risk of developing other chronic health conditions and increased all-cause mortality.1 Many musculoskeletal conditions share risk factors common to other chronic health conditions, such as obesity, poor nutrition and a sedentary lifestyle. Musculoskeletal conditions account for the greatest proportion of persistent pain across geographies and ages.2 Back and neck pain, osteoarthritis, rheumatoid arthritis and fractures are among the most disabling musculoskeletal conditions and pose major threats to healthy ageing by limiting physical and mental capacities and functional ability. Although the prevalence of major musculoskeletal conditions increases with age, they are not just conditions of older age. Regional pain conditions, low back and neck pain, musculoskeletal injury sequelae and inflammatory arthritides commonly affect children, adolescents and middle-aged people during their formative and peak income-earning years, establishing trajectories of decline in intrinsic capacity in later years. While point prevalence estimates vary with respect to age and musculoskeletal condition, approximately one in three people worldwide live with a chronic, painful musculoskeletal condition. Notably, recent data suggest that one in two adult Americans live with a musculoskeletal condition, a prevalence comparable to that of cardiovascular and chronic respiratory diseases combined, which cost 213 billion United States dollars in 2011 (or 1.4% of gross domestic product).3 Data from lowand middle-income countries are fewer, yet comparable.4 Musculoskeletal health is critical for human function, enabling mobility, dexterity and the ability to work and actively participate in all aspects of life. Musculoskeletal health is therefore essential for maintaining economic, social and functional independence, as well as human capital across the life course. Impaired musculoskeletal health is responsible for the greatest loss of productive life years in the workforce compared with other noncommunicable diseases,5 commonly resulting in early retirement and reduced financial security. In subsistence communities and lowand middle-income economies, impaired musculoskeletal health has profound consequences on an individual’s ability to participate in social roles and in the prosperity of communities.4
Campbell Menzies, Richard Bowtell, Natalie Shur
et al.
ABSTRACT Sarcopenia describes the loss of muscle mass and function with age. The increase in prevalence of sarcopenia in women appears to coincide with the onset of menopause, which is characterized by large changes to the hormonal milieu such as decreased oestrogen and progesterone concentrations. Although the timing of menopause and sarcopenia may coincide, there is a lack of high‐quality evidence demonstrating a link between the two. This narrative review aims to assess evidence for the effects of menopause on muscle mass and muscle protein turnover. Longitudinal (n = 4/5) and cross‐sectional (n = 7/11) studies demonstrate a reduction in lean or muscle mass across the menopausal transition with −2.5% and −5.7% reductions in perimenopausal and postmenopausal women, respectively, compared to premenopausal women. Most of this evidence (n = 10/11) is taken through assessment of lean body mass via dual‐energy x‐ray absorptiometry (DXA), which may underestimate changes in muscle mass. Assessment on changes to muscle protein turnover is largely limited to short‐term measures of muscle protein synthesis (MPS), which may be elevated in older women versus younger women (n = 3/7) or age‐matched males (n = 4/5). MPS responses to anabolic stimuli, such as resistance exercise (n = 3/4) or protein ingestion (n = 3/6), may be blunted in older women. Evidence assessing muscle protein breakdown (MPB) is lacking; however, evidence from animal and cell models demonstrates the role of estradiol in suppressing MPB, which may contribute to an increase in MPB following menopause. Advancements in understanding the role of the menopausal transition in the regulation of muscle mass, and subsequent effectiveness of interventions such as exercise or exogenous hormone provision will enable healthy ageing and sarcopenia prevention in older women.
Diseases of the musculoskeletal system, Human anatomy
Stijn J M Niessen, Ellen N. Behrend, F. Fracassi
et al.
Simple Summary To make progress in the field of hormonal diseases in companion animals, it helps when researchers, clinicians, and educators use the same language. Currently, there is no consensus on basic concepts such as what constitutes the correct definition of diseases affecting the adrenal glands, important hormone-producing glands situated next to the kidneys. This publication reports on the second cycle of a novel project called “Agreeing Language in Veterinary Endocrinology” (ALIVE) that brings experts and those interested in the field together to try and achieve consensus on such disease definitions. The cycle’s methods were adapted from previous ones to improve efficiency and were completed successfully, accomplishing a majority-based consensus. It also delivered agreement on diagnostic criteria for adrenal diseases in companion animals. It is hoped the work will improve education, diagnosis, and treatment in this field, ultimately leading to improvements in the quality of life of animals suffering from adrenal disease.
Andrea Fava, Catriona A. Wagner, Carla J. Guthridge
et al.
Objective Lupus nephritis (LN) management remains challenging, and novel noninvasive biomarkers are needed. This study quantified serum soluble mediators in the Accelerating Medicines Partnership (AMP) LN cohort to identify biomarkers of histologic features and treatment response. Methods Patients with systemic lupus erythematosus (SLE) (n = 268) undergoing clinically indicated kidney biopsies (urine protein/creatinine ratio [UPCR] ≥ 0.5) were recruited through the AMP Rheumatoid Arthritis and SLE Network. Serum was collected at biopsy and 3‐, 6‐, and 12‐month postbiopsy, alongside samples from 22 healthy controls. Concentrations of 66 immune mediators were quantified using xMAP multiplex assays, and (TACE) measured by enzyme‐linked immunosorbent assay. Seven mediators with >95% values below detection limits were excluded from analyses. Bootstrapped least absolute shrinkage and selection operator (LASSO) regression identified proliferative LN (class III/IV ± V) predictors from baseline mediators. Associations with 12‐month treatment response (complete/partial vs no response) were tested using three‐month changes in LASSO‐selected mediators and UPCR via logistic regression. Molecular clustering of mediator profiles was performed to identify LN subgroups. Results Proliferative patients with LN (class [III or IV] ± V; n = 160) displayed a distinct mediator profile compared with nonproliferative LN (class I, II, or V; n = 96). LASSO regression identified 20 mediators predictive of proliferative LN (areas under the curve, 0.82; 95% confidence interval [CI], 0.81–0.91), including elevated syndecan‐1, tumor necrosis factor receptor type I, tumor necrosis factor receptor type II, and vascular cell adhesion molecule 1 (VCAM‐1), as well as decreased CCL3//macrophage inflammatory protein 1α, CD40 ligand, and interleukin‐5 levels. Among proliferative patients with LN, 3‐month reductions in syndecan‐1 and VCAM‐1, mediators associated with intrarenal LN activity and/or chronicity, predicted the 12‐month treatment response. A model incorporated these reductions and a decline in UPCR‐predicted treatment response in proliferative LN (0.90; 95% CI, 0.82–0.98). Molecular clustering revealed four distinct LN subgroups with unique soluble mediator signatures and clinical features not captured by histology alone. Conclusion Serum soluble mediators, particularly syndecan‐1 and VCAM‐1, reflect LN histologic activity, and early decreases predict treatment response, supporting their potential use as noninvasive longitudinal biomarkers. The substantial heterogeneity within LN highlights the potential for biomarker‐guided reclassification to advance precision medicine approaches.
Chronic pain is a common problem in rheumatology. A distinction is made between nociceptive pain and nociplastic pain. Nociceptive pain is, for example, mechanistically explained by persistent inflammation. Neuropathic pain is caused by nerve damage of various possible causes. In contrast, nociplastic pain is not due to tissue damage or a lesion in the somatosensory nervous system—at least not with the currently available techniques. Nociplastic pain is based on an altered perception of pain through modulation of stimulus processing. The concept of central sensitization, together with other neurobiological and psychosocial mechanisms, is considered to be the best explanation for such pain conditions. The syndrome of fibromyalgia (FM), considered to be due to central sensitization, plays a major role in rheumatology—both in terms of differential diagnosis and because the management of inflammatory rheumatic diseases can be made more difficult by the simultaneous presence of FM. During the coronavirus pandemic, persistent pain syndromes with similarities to FM were described following a COVID-19 infection. There is a growing scientific controversy as to whether the so-called long COVID syndrome (LCS) is a separate entity or just a variant of FM.
Musculoskeletal disorders pose significant risks to athletes, and assessing risk early is important for prevention. However, most existing methods are designed for controlled settings and fail to reliably assess risk in complex environments due to their reliance on a single type of data. This research introduces ViSK-GAT (Visual-Skeletal Geometric Attention Transformer), a novel multimodal deep learning framework that classifies musculoskeletal risk using both visual and skeletal coordinate-based features. A custom multimodal dataset (MusDis-Sports) was created by combining images and skeletal coordinates, with each sample labeled into eight risk categories based on the Rapid Entire Body Assessment (REBA) system. ViSK-GAT integrates two innovative modules: the Fine-Grained Attention Module (FGAM), which refines inter-modal features via cross-attention between visual and skeletal inputs, and the Multimodal Geometric Correspondence Module (MGCM), which enhances cross-modal alignment between image features and coordinates. The model achieved robust performance, with all key metrics exceeding 93%. Regression results also indicated a low RMSE of 0.1205 and MAE of 0.0156. ViSK-GAT consistently outperformed nine popular transfer learning backbones and showed its potential to advance AI-driven musculoskeletal risk assessment and enable early, impactful interventions in sports.
Manuela Daniela Danu, George Marica, Constantin Suciu
et al.
The rapidly increasing volume of electronic health record (EHR) data underscores a pressing need to unlock biomedical knowledge from unstructured clinical texts to support advancements in data-driven clinical systems, including patient diagnosis, disease progression monitoring, treatment effects assessment, prediction of future clinical events, etc. While contextualized language models have demonstrated impressive performance improvements for named entity recognition (NER) systems in English corpora, there remains a scarcity of research focused on clinical texts in low-resource languages. To bridge this gap, our study aims to develop multiple deep contextual embedding models to enhance clinical NER in the cardiology domain, as part of the BioASQ MultiCardioNER shared task. We explore the effectiveness of different monolingual and multilingual BERT-based models, trained on general domain text, for extracting disease and medication mentions from clinical case reports written in English, Spanish, and Italian. We achieved an F1-score of 77.88% on Spanish Diseases Recognition (SDR), 92.09% on Spanish Medications Recognition (SMR), 91.74% on English Medications Recognition (EMR), and 88.9% on Italian Medications Recognition (IMR). These results outperform the mean and median F1 scores in the test leaderboard across all subtasks, with the mean/median values being: 69.61%/75.66% for SDR, 81.22%/90.18% for SMR, 89.2%/88.96% for EMR, and 82.8%/87.76% for IMR.
Frederico Belmonte Klein, Zhaoyuan Wan, Huawei Wang
et al.
Musculoskeletal modeling and simulations enable the accurate description and analysis of the movement of biological systems with applications such as rehabilitation assessment, prosthesis, and exoskeleton design. However, the widespread usage of these techniques is limited by costly sensors, laboratory-based setups, computationally demanding processes, and the use of diverse software tools that often lack seamless integration. In this work, we address these limitations by proposing an integrated, real-time framework for musculoskeletal modeling and simulations that leverages OpenSimRT, the robotics operating system (ROS), and wearable sensors. As a proof-of-concept, we demonstrate that this framework can reasonably well describe inverse kinematics of both lower and upper body using either inertial measurement units or fiducial markers. Additionally, we show that it can effectively estimate inverse dynamics of the ankle joint and muscle activations of major lower limb muscles during daily activities, including walking, squatting and sit to stand, stand to sit when combined with pressure insoles. We believe this work lays the groundwork for further studies with more complex real-time and wearable sensor-based human movement analysis systems and holds potential to advance technologies in rehabilitation, robotics and exoskeleton designs.
Yunyue Wei, Shanning Zhuang, Vincent Zhuang
et al.
Controlling high-dimensional nonlinear systems, such as those found in biological and robotic applications, is challenging due to large state and action spaces. While deep reinforcement learning has achieved a number of successes in these domains, it is computationally intensive and time consuming, and therefore not suitable for solving large collections of tasks that require significant manual tuning. In this work, we introduce Model Predictive Control with Morphology-aware Proportional Control (MPC^2), a hierarchical model-based learning algorithm for zero-shot and near-real-time control of high-dimensional complex dynamical systems. MPC^2 uses a sampling-based model predictive controller for target posture planning, and enables robust control for high-dimensional tasks by incorporating a morphology-aware proportional controller for actuator coordination. The algorithm enables motion control of a high-dimensional human musculoskeletal model in a variety of motion tasks, such as standing, walking on different terrains, and imitating sports activities. The reward function of MPC^2 can be tuned via black-box optimization, drastically reducing the need for human-intensive reward engineering.
Pembe Gizem Özdil, Chuanfang Ning, Jasper S. Phelps
et al.
Computational models are critical to advance our understanding of how neural, biomechanical, and physical systems interact to orchestrate animal behaviors. Despite the availability of near-complete reconstructions of the Drosophila melanogaster central nervous system, musculature, and exoskeleton, anatomically and physically grounded models of fly leg muscles are still missing. These models provide an indispensable bridge between motor neuron activity and joint movements. Here, we introduce the first 3D, data-driven musculoskeletal model of Drosophila legs, implemented in both OpenSim and MuJoCo simulation environments. Our model incorporates a Hill-type muscle representation based on high-resolution X-ray scans from multiple fixed specimens. We present a pipeline for constructing muscle models using morphological imaging data and for optimizing unknown muscle parameters specific to the fly. We then combine our musculoskeletal models with detailed 3D pose estimation data from behaving flies to achieve muscle-actuated behavioral replay in OpenSim. Simulations of muscle activity across diverse walking and grooming behaviors predict coordinated muscle synergies that can be tested experimentally. Furthermore, by training imitation learning policies in MuJoCo, we test the effect of different passive joint properties on learning speed and find that damping and stiffness facilitate learning. Overall, our model enables the investigation of motor control in an experimentally tractable model organism, providing insights into how biomechanics contribute to generation of complex limb movements. Moreover, our model can be used to control embodied artificial agents to generate naturalistic and compliant locomotion in simulated environments.
Gabrielle Hoyer, Michelle W Tong, Rupsa Bhattacharjee
et al.
Precision medicine in musculoskeletal imaging requires scalable measurement infrastructure. We developed a modular system that converts routine MRI into standardized quantitative biomarkers suitable for clinical decision support. Promptable foundation segmenters (SAM, SAM2, MedSAM) were fine-tuned across heterogeneous musculoskeletal datasets and coupled to automated detection for fully automatic prompting. Fine-tuned segmentations yielded clinically reliable measurements with high concordance to expert annotations across cartilage, bone, and soft tissue biomarkers. Using the same measurements, we demonstrate two applications: (i) a three-stage knee triage cascade that reduces verification workload while maintaining sensitivity, and (ii) 48-month landmark models that forecast knee replacement and incident osteoarthritis with favorable calibration and net benefit across clinically relevant thresholds. Our model-agnostic, open-source architecture enables independent validation and development. This work validates a pathway from automated measurement to clinical decision: reliable biomarkers drive both workload optimization today and patient risk stratification tomorrow, and the developed framework shows how foundation models can be operationalized within precision medicine systems.
Martin Thißen, Thi Ngoc Diep Tran, Barbara Esteve Ratsch
et al.
It is well-established that more data generally improves AI model performance. However, data collection can be challenging for certain tasks due to the rarity of occurrences or high costs. These challenges are evident in our use case, where we apply AI models to a novel approach for visually documenting the musculoskeletal condition of dogs. Here, abnormalities are marked as colored strokes on a body map of a dog. Since these strokes correspond to distinct muscles or joints, they can be mapped to the textual domain in which large language models (LLMs) operate. LLMs have demonstrated impressive capabilities across a wide range of tasks, including medical applications, offering promising potential for generating synthetic training data. In this work, we investigate whether LLMs can effectively generate synthetic visual training data for canine musculoskeletal diagnoses. For this, we developed a mapping that segments visual documentations into over 200 labeled regions representing muscles or joints. Using techniques like guided decoding, chain-of-thought reasoning, and few-shot prompting, we generated 1,000 synthetic visual documentations for patellar luxation (kneecap dislocation) diagnosis, the diagnosis for which we have the most real-world data. Our analysis shows that the generated documentations are sensitive to location and severity of the diagnosis while remaining independent of the dog's sex. We further generated 1,000 visual documentations for various other diagnoses to create a binary classification dataset. A model trained solely on this synthetic data achieved an F1 score of 88% on 70 real-world documentations. These results demonstrate the potential of LLM-generated synthetic data, which is particularly valuable for addressing data scarcity in rare diseases. While our methodology is tailored to the medical domain, the insights and techniques can be adapted to other fields.
Abstract Background Cerebral palsy (CP) is the most common physical disability of childhood, affecting movement and posture, resulting from a neurological insult during pregnancy or the neonatal period. While the brain lesion is static, the musculoskeletal sequelae in CP are often progressive and lifelong, associated with pain and can impact the lives of children with CP, their families and the healthcare system. The Australasian Cerebral Palsy Musculoskeletal Health Network (AusCP MSK) study will conduct comprehensive, population-based surveillance of children with moderate to severe functional mobility limitations (Gross Motor Function Classification System (GMFCS) levels III–V) to explore the early biomarkers of, and interactions between, musculoskeletal complications related to CP, including hip displacement, scoliosis and skeletal fragility. Methods The AusCP MSK study involves three cohorts of children. Cohort A (n=500) is a multicentre retrospective (3 years) and prospective (4 years) cohort study in children aged 4–9 years that will be implemented at five sites across Australia and New Zealand. Retrospective data will include clinical history, information on CP diagnosis and other investigations (previous X-rays and biochemistry). Primary prospective outcomes will involve measures of hip displacement (migration percentage, acetabular index, femoral head orientation, Hilgenreiner’s epiphyseal angle), scoliosis (Anteroposterior/Posteroanterior and lateral spine X-ray), skeletal fragility (Dual Energy X-ray Absorptiometry, peripheral quantitative computed tomography), motor function (GMFCS, Manual Ability Classification System (MACS) and Communication Function Classification System (CFCS)) and range of movement (lower limb and spine). Cohort B (n=4000) is a retrospective analysis of data to evaluate fractures in children up to 18 years of age with CP (GMFCS I–V) from the New South Wales (NSW)/Australian Capital Territory CP Registers linked with corresponding records from NSW administrative health data (n=3000), and a New Zealand cohort of linked data from the New Zealand Cerebral Palsy Register to the Accident Compensation Corporation data for fracture claims (n=1000). Cohort C (n=30) will cross-sectionally examine bone quality through a transiliac bone biopsy in children undergoing scheduled hip surgery. Relationships between early biomarkers, early brain structure and musculoskeletal complications will be explored using multilevel mixed-effect models. Ethics and dissemination Ethical approval for this study was granted by Children’s Health Queensland Hospital and Health Service Human Research Ethics Committee, The University of Queensland Human Research Ethics Committee and the New Zealand Health and Disability Ethics Committee. Research outcomes will be disseminated via scientific conferences and publications in peer-reviewed journals; to the National Bodies and Clinicians; and to people with CP and their families. Trial registration number Australian New Zealand Clinical Trials Registry number: ACTRN12622000788774p
Thaís C Freire, M. S. Ferreira, Kátia De Angelis
et al.
BACKGROUND Progressive exercise intolerance is a hallmark of pulmonary hypertension (pH), severely impacting patients' independence and quality of life (QoL). Accumulating evidence over the last decade shows that combined abnormalities in peripheral reflexes and target organs contribute to disease progression and exercise intolerance. OBJECTIVE The aim of this study was to review the literature of the last decade on the contribution of the cardiovascular, respiratory, and musculoskeletal systems to pathophysiology and exercise intolerance in pH. METHODS A systematic literature search was conducted using specific terms in PubMed, SciELO, and the Cochrane Library databases for original pre-clinical or clinical studies published between 2013 and 2023. Studies followed randomized controlled/non-randomized controlled and pre-post designs. RESULTS The systematic review identified 25 articles reporting functional or structural changes in the respiratory, cardiovascular, and musculoskeletal systems in pH. Moreover, altered biomarkers in these systems, lower cardiac baroreflex, and heightened peripheral chemoreflex activity seemed to contribute to functional changes associated with poor prognosis and exercise intolerance in pH. Potential therapeutic strategies acutely explored involved manipulating the baroreflex and peripheral chemoreflex, improving cardiovascular autonomic control via cardiac vagal control, and targeting specific pathways such as GPER1, GDF-15, miR-126, and the JMJD1C gene. CONCLUSION Information published in the last 10 years advances the notion that pH pathophysiology involves functional and structural changes in the respiratory, cardiovascular, and musculoskeletal systems and their integration with peripheral reflexes. These findings suggest potential therapeutic targets, yet unexplored in clinical trials, that could assist in improving exercise tolerance and QoL in patients with pH.
Elay Dahan, Hedda Cohen Indelman, Angeles M. Perez-Agosto
et al.
The use of synthetic images in medical imaging Artificial Intelligence (AI) solutions has been shown to be beneficial in addressing the limited availability of diverse, unbiased, and representative data. Despite the extensive use of synthetic image generation methods, controlling the semantics variability and context details remains challenging, limiting their effectiveness in producing diverse and representative medical image datasets. In this work, we introduce a scalable semantic and context-conditioned generative model, coined CSG (Context-Semantic Guidance). This dual conditioning approach allows for comprehensive control over both structure and appearance, advancing the synthesis of realistic and diverse ultrasound images. We demonstrate the ability of CSG to generate findings (pathological anomalies) in musculoskeletal (MSK) ultrasound images. Moreover, we test the quality of the synthetic images using a three-fold validation protocol. The results show that the synthetic images generated by CSG improve the performance of semantic segmentation models, exhibit enhanced similarity to real images compared to baseline methods, and are undistinguishable from real images according to a Turing test. Furthermore, we demonstrate an extension of the CSG that allows for the realism of the space of images’ variability by synthetically generating augmentations of anatomical geometries and textures.Clinical relevance— The generation of clinically valid synthetic pathological anomalies, such as in musculoskeletal ultrasound images, is a promising use case for developing robust and unbiased AI models. These models have potential applications in cloud-based systems, clinical decision support tools, and the early detection of musculoskeletal diseases.
Abstract Background Surgical site infection (SSI) is a common and serious complication of elective clean orthopedic surgery that can lead to severe adverse outcomes. However, the prognostic efficacy of the current staging systems remains uncertain for patients undergoing elective aseptic orthopedic procedures. This study aimed to identify high-risk factors independently associated with SSI and develop a nomogram prediction model to accurately predict the occurrence of SSI. Methods A total of 20,960 patients underwent elective clean orthopedic surgery in our hospital between January 2020 and December 2021, of whom 39 developed SSI; we selected all 39 patients with a postoperative diagnosis of SSI and 305 patients who did not develop postoperative SSI for the final analysis. The patients were randomly divided into training and validation cohorts in a 7:3 ratio. Univariate and multivariate logistic regression analyses were conducted in the training cohort to screen for independent risk factors of SSI, and a nomogram prediction model was developed. The predictive performance of the nomogram was compared with that of the National Nosocomial Infections Surveillance (NNIS) system. Decision curve analysis (DCA) was used to assess the clinical decision-making value of the nomogram. Results The SSI incidence was 0.186%. Univariate and multivariate logistic regression analysis identified the American Society of Anesthesiology (ASA) class (odds ratio [OR] 1.564 [95% confidence interval (CI) 1.029–5.99, P = 0.046]), operative time (OR 1.003 [95% CI 1.006–1.019, P < 0.001]), and D-dimer level (OR 1.055 [95% CI 1.022–1.29, P = 0.046]) as risk factors for postoperative SSI. We constructed a nomogram prediction model based on these independent risk factors. In the training and validation cohorts, our predictive model had concordance indices (C-indices) of 0.777 (95% CI 0.672–0.882) and 0.732 (95% CI 0.603–0.861), respectively, both of which were superior to the C-indices of the NNIS system (0.668 and 0.543, respectively). Calibration curves and DCA confirmed that our nomogram model had good consistency and clinical predictive value, respectively. Conclusions Operative time, ASA class, and D-dimer levels are important clinical predictive indicators of postoperative SSI in patients undergoing elective clean orthopedic surgery. The nomogram predictive model based on the three clinical features demonstrated strong predictive performance, calibration capabilities, and clinical decision-making abilities for SSI.
Orthopedic surgery, Diseases of the musculoskeletal system
Background: As compared to endocrine responsive breast cancer bone is less frequent site of distant recurrence in triple-negative breast cancer (TNBC). A biomarker which predicts bone recurrence would allow a more personalized treatment approach with adjuvant bisphosphonates in TNBC. Here we hypothesised that tumour expression of androgen receptor (AR) is associated with bone recurrence in TNBC. Materials and methods: Patients with operable TNBC who were treated at the Institute of Oncology Ljubljana between 2005 and 2015 and developed distant recurrence were included into our study. Nuclear expression of AR in the tissue of primary tumours was determined immunohistochemically by using the Androgen Receptor (SP107) Rabbit Monoclonal Antibody. We applied a logistic regression model to test the association between expression of AR and development of bone metastases. The model was adjusted for selected known prognostic factors and possible confounders in TNBC, including the level of the stromal tumour-infiltrating lymphocytes (sTILs). Results: At recurrence 45 (45 %) out of 100 patients presented with bone metastases. Additionally, seven (7 %) developed bone metastases metachronously. AR was expressed in primary tumours of 35 (35 %) women and 19 (54.3 %) developed bone recurrence. In 25 (25 %) patients sTILs were absent. Neither the proportion of AR positive cancer cells (OR = 1.00; 95 % CI 0.96–1.03; p = 1.00) nor the intensity of AR positive reaction (OR = 0.71; 95 % CI 0.02–21.4; p = 1.00) were significantly associated with bone recurrence. However, women with at least mild level of the sTILs were at significantly lower risk for bone recurrence as compared to those without any sTILs (OR = 0.01; 95 % CI < 0.01–0.08; p = 0.01). Conclusions: Expression of AR is not significantly associated with the development of bone metastases in TNBC. However, patients with absent sTILs in their primary tumours are highly susceptible for recurrence in the bone and might particularly benefit from adjuvant bisphosphonates.
Diseases of the musculoskeletal system, Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Yuya Mawarikado, Yusuke Inagaki, Tadashi Fujii
et al.
Abstract Background Factors associated with falls after total knee arthroplasty (TKA) have been rarely reported. The aim of this study was to identify factors that influence the incidence of falls after TKA, focusing on toe grip strength (TGS) in particular, which has been associated with falls in older adults. Methods 217 patients who underwent TKA were included and followed up for 1 year. Main study outcome measures were the presence or absence of falls within 1 year after TKA. Multiple logistic regression analysis was used with postoperative falls as the dependent variable and preoperative falls and postoperative TGS on the affected sides as independent variables. Results 170 (43 and 127 in the fall and non‐fall groups) patients were included in the analysis. The presence of a preoperative falls history before TKA and a weak postoperative affected TGS indicated an increased susceptibility of the patient to fall postoperatively. Conclusions Results of the current study revealed the association between postoperative TGS and postoperative falls. We highlight the importance of preoperative fall monitoring and postoperative TGS evaluation to prevent falls after TKA.