The prevalence of musculoskeletal diseases such as osteoarthritis, osteoporosis, sarcopenia, and rheumatoid arthritis is rising sharply with global ageing, increasing disability rates among older adults (aged ≥60 years), diminishing quality of life, and burdening health-care systems. Current musculoskeletal care for older adults faces multiple limitations, including comorbidities, frailty, and fragmented care. High osteoarthritis prevalence in individuals older than 55 years, the mounting economic burden of osteoporotic fractures, the growing concern of muscle mass decline, and insufficient guideline implementation collectively underscore these challenges. In the USA, musculoskeletal diseases affect over 121 million people and account for the highest rate of disability among all disease groups, underscoring the need for targeted strategies. Although promising solutions encompassing advanced pharmacological therapies, regenerative medicine, and digital health technologies (including artificial intelligence) are available, they remain underutilised in existing care models. This Personal View discusses the need for personalised, multidisciplinary approaches to address these issues, advocating for collaboration among the orthopaedic, geriatric, and health-care sectors in the USA. We propose that prevention of musculoskeletal diseases is key to its effective management in ageing populations, alongside a holistic, scalable approach that integrates diagnostics, therapy, and telemedicine. Early intervention, interdisciplinary collaboration, and personalised care are essential to improving patient outcomes and addressing the growing musculoskeletal disease burden in the USA.

Exacerbated by an aging population, musculoskeletal diseases are a chronic and growing problem in the United States that impose significant health and economic burdens. The objective of this study was to analyze the correlation between the burden of diseases and the federal funds assigned to health-related research through the National Institutes of Health (NIH). An ecological study design was used to examine the relationship between NIH research funding and disease burden for 60 disease categories. We used the Global Burden of Disease (GBD) Study 2019 to measure disease burden and the NIH Research, Condition, and Disease Categories (RCDC) data to identify 60 disease categories aligned with available GBD data. NIH funding data was obtained from the RCDC system and the NIH Office of Budget. Using linear regression models, we observed that musculoskeletal diseases were among the most underfunded (i.e., negative residuals from the model) with respect to disease burden. Musculoskeletal diseases were underfunded, with neck pain being the most underfunded at only 0.83% of expected funding. Low back pain, osteoarthritis, and rheumatoid arthritis were also underfunded at 13.88%, 35.08%, and 66.26%, respectively. Musculoskeletal diseases were the leading cause of years lived with disability and the third leading cause in terms of prevalence and disability-adjusted life years. Despite the increasing burden of these diseases, the allocation of NIH funding to the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) has remained low compared to other institutes. Despite the increasing health burden and economic cost of $980 billion annually, the allocation of NIH funding to the NIAMS has remained low compared to other institutes. These findings suggest that the NIH may need to reassess its allocation of research funding to align with the current health challenges of our country. Furthermore, these clinically relevant observations highlight the need to increase research funding for musculoskeletal diseases and improve their prevention, diagnosis, and treatment. No funding.

Traditional antibiotic therapy has encountered significant challenges for clinical treatment of infections for multiple reasons, including antimicrobial resistance (AMR) and poor efficacy against biofilms, demanding research into alternative therapeutic agents. Because of their unique antimicrobial mechanisms as well as their target specificity, diversity, exponential self-amplification, and anti-biofilm activity, combined with recent advances in genomics and synthetic biology, bacteriophages have attracted increased interest as potential alternatives or therapeutic adjuncts to antibiotics. However, obstacles such as phage-host specificity, bacterial resistance, and the selection of optimal phages, amongst other factors, impede clinical adoption of phage therapy. Here, machine learning (ML) and artificial intelligence (AI) tools have the opportunity to revolutionize phage therapy by enhancing scalability, efficiency and precision of these therapies. This article highlights potential key applications of ML/AI in the study, development and deployment of phage therapy.

Background: There are no studies currently in the literature that assesses complications following revision total shoulder arthroplasty (TSA) in patients with varying severity of anemia. The purpose of this study was to determine the impact of preoperative anemia severity on postoperative complications following revision TSA. Methods: Patients undergoing revision TSA from 2013 to 2019 were queried in a national database. Based on previous studies’ definitions of anemia, three subgroups were stratified: patients without anemia (hematocrit >36% for women, hematocrit >39% for men), patients with mild anemia (hematocrit 33% to 36% for women, hematocrit 33% to 39% for men) and patients with moderate to severe anemia (hematocrit <33% for both women and men). In this analysis, patient demographics, comorbidities, and postoperative complications were compared between the three groups. Results: Of 1559 total patients undergoing revision TSA, 1178 patients (75.6%) did not have anemia, 255 (16.3%) had mild anemia, and 126 (8.1%) had moderate/severe anemia. Following adjustment on multivariate analysis, patients with mild anemia were more likely to have postoperative transfusion and extended length of stay compared to non-anemic patients. Patients with moderate/severe anemia were at increased risk of postoperative transfusion, sepsis, extended length of stay, and reoperation compared to non-anemic patients. Discussion: From mild anemia to moderate/severe anemia, there was a stepwise increase in the risk of postoperative complications. Our study showed that there is clinical value in the preoperative correction of anemia for these patients as it relates to complications and hospital stay. Level of Evidence: III.