Sarcopenia is a risk factor for morbidity and preventable mortality in old age, with consequent high costs for the national health system. Its diagnosis requires costly radiological examinations, such as the DEXA, which complicate screening in medical centers with a high prevalence of sarcopenia. Developing a nearly zero-cost screening tool to emulate the performance of DEXA in identifying patients with muscle mass loss. This can crucially help the early diagnosis of sarcopenia at large-scale, contributing to reduce its prevalence and related complications with timely treatments. We exploit cross-sectional data for about 14,500 patients and 38 non-laboratory variables from successive NHANES over 7 years (1999–2006). Data are analyzed through a state-of-the-art artificial intelligence approach based on decision trees. A reduced number of anthropometric parameters allows to predict the outcome of DEXA with AUC between 0.92 and 0.94. The most complex model derived in this paper exploits 6 variables, related to the circumference of key corporal segments and to the evaluation of body fat. It achieves an optimal trade-off sensitivity of 0.89 and a specificity of 0.82. Restricting exclusively to variables related to lower limb, we obtain an even simpler tool with only slightly lower accuracy (AUC 0.88–0.90). Anthropometric data seem to contain the entire informative content of a more complex set of non-laboratory variables, including anamnestic and/or morbidity factors. Compared to previously published screening tools for muscle mass loss, the newly developed models are less complex and achieve a better accuracy. The new results might suggest a possible inversion of the standard diagnostic algorithm of sarcopenia. We conjecture a new diagnostic scheme, which requires a dedicated clinical validation that goes beyond the scope of the present study.

Background and Purpose: Assessing appendicular skeletal muscle (ASM) mass is crucial for the diagnosis of numerous pathologies related to the decline of muscle mass in old age, such as sarcopenia, malnutrition, or cachexia. The dual-energy X-ray absorptiometer (DEXA) radiological technique, which is the gold standard for its assessment, is particularly costly and not routinely used in clinical practice. The aim of this study was to derive computationally simple equations capable of estimating the DEXA-measured ASM at zero cost in older adult populations. Methods: We used the cross-sectional data collected by the National Health and Nutrition Examination Survey (NHANES) over 7 years (1999-2006). The study sample included 16,477 individuals aged 18 years and over, of which 4401 were over 60 years old. We considered 38 nonlaboratory variables. For the derivation of the equations, we employed the Brain Project, an innovative artificial intelligence tool that combines genetic programming and neural networks. The approach searches simultaneously for the mathematical expression and the variables to use in the equation. The derived equations are useful to estimate the DEXA-measured ASM. Results and Discussion: A simple equation that includes the body weight of the patient as the sole variable can estimate the outcome of DEXA with an accuracy equivalent to previously published equations. When used to identify individuals over 60 years old with muscle mass loss, it achieved an area under the curve (AUC) value of 0.85 for both males and females. The inclusion of sex and anthropometric data (thigh and arm circumference) improved the accuracy for male individuals (AUC 0.89). The model is also suitable to be applied to the general adult population of 18 years of age or older. Using more than 3 variables does not lead to better accuracy. Conclusions: The newly proposed equations have better diagnostic accuracy than previous equations for the estimation of DEXA-measured ASM. They are readily applicable in clinical practice for the screening of muscle mass loss in the over 60-year-old population with nearly zero-cost variables. The most complex model proposed in this study requires only the inspection of a simple diagnostic chart to estimate the status of muscle mass loss.

Background: The evolution of technology has continuously redefined the landscape of rehabilitation medicine. Researchers have long incorporated virtual reality (VR) as a promising intervention, providing immersive therapeutic environments for patients. The emergence of the metaverse has recently further expanded the potential applications of VR to augment the possibilities in rehabilitation. Rehabilitation is a crucial aspect of healthcare, and technological advancements have allowed new approaches to aid in this process. One such approach is the metaverse, a virtual world where users can interact with each other and their surroundings in a simulated environment. This comprehensive review aimed to analyze the scientific evidence using the term “metaverse” in rehabilitation and its potential patient benefits. Methods: We conducted a comprehensive literature search from the inception to September 2024 in PubMed, Scopus, Web of Science, and Cochrane Database to identify studies investigating the term “metaverse” and its role in rehabilitation. We then assessed these studies based on their methodology, patient population, technology used, and therapeutic outcomes. Results: Out of 81 articles, 55 remained after removing duplicates. After screening the title, abstract, and full text, we included five articles. Conclusions: Results from these studies suggested potential benefits in various rehabilitative areas, such as cerebral palsy, intellectual disabilities, pain management, and physical performance improvement among the elderly. The metaverse presents promising avenues for enhancing rehabilitation outcomes. While VR’s effectiveness is well established, the metaverse, being a newer concept, necessitates further studies for a more comprehensive understanding.

Background: Knee osteoarthritis (OA) is a prevalent condition among adults that leads to knee joint pain and dysfunction. Over the past two decades, local intra-articular knee injection therapy has gained popularity due to the advent of platelet-rich plasma (PRP), hyaluronic acid (HA), and the novel peripheral blood-derived mononuclear cells (PBMNCs). This study aimed to compare the therapeutic efficacy of intra-articular injections of PBMNCs, HA, and PRP combined with hyaluronic acid (PRP-HA) for treating degenerative knee OA classified as stages II and III, according to the Kellgren and Lawrence (KL) scale. Methods: This retrospective observational study involved adults with moderate-to-moderately severe knee OA treated at the University Hospital of Catania, Italy. The subjects were divided into three groups and treated with intra-articular injections of HA, PRP-HA, or PBMNCs. The outcome measures assessed were as follows: the Numerical Rating Scale, the Western Ontario and McMaster Universities Arthritis Index, the Timed Up and Go, the International Knee Documentation Committee score, a 10-meter walking test, and the Physical score and the Mental score on the SF-12. This study included a total of 46 adults, 30 females and 16 males, with a mean age of 63.7 ± 10.9 years. Results: HA, PRP-HA, and PBMNCs demonstrated comparable effectiveness for improving the NRS score and all the other outcomes at 6 months. Additionally, PRP-HA and PBMNCs also enhanced knee flexion and the International Knee Documentation Committee score. However, none of the three treatments led to a significant improvement on the 10-meter walking test. No serious adverse effects were reported. Conclusions: In this study, injections of HA, PRP-HA, and PBMNCs all demonstrated positive outcomes for up to 6 months post-treatment in the subjects suffering from knee OA.