Sarcopenia is characterized by loss of muscle and reduction in muscle strength that contributes to higher mortality rate and increased incidence of fall and hospitalization in the elderly. Mitochondria dysfunction and age-associated inflammation in muscle are two of the main attributors to sarcopenia progression. Recent clinical trials on sarcopenia therapies such as physical exercise, nutraceutical, and pharmaceutical interventions have revealed that exercise is the only effective strategy shown to alleviate sarcopenia. Unlike nutraceutical and pharmaceutical interventions that showed controversial results in sarcopenia alleviation, exercise was found to restore mitochondria homeostasis and dampen inflammatory responses via a complex exchange of myokines and osteokines signalling between muscle and bone. However, as exercise have limited benefit to immobile patients, the use of stem cells and their secretome are being suggested to be novel therapeutics that can be catered to a larger patient population owing to their mitochondria restoration effects and immune modulatory abilities. As such, we reviewed the potential pros and cons associated with various stem cell types/secretome in sarcopenia treatment and the regulatory and production barriers that need to be overcome to translate such novel therapeutic agents into bedside application.

Cellular senescence is a hallmark of organismal aging but how it drives aging in human tissues is not fully understood. Here we leverage single nucleus multiomics to profile senescence in mononucleated cells of human skeletal muscle and provide the first senescence atlas. We demonstrate the intra- and inter-populational transcriptomic and epigenomic heterogeneity and dynamics of cellular senescence. We also identify commonalities and variations in senescence-associated secretory phenotypes (SASPs) among the cells and elucidate SASP mediated cellular interactions and niche deregulation. Furthermore, we identify targetable SASPs and demonstrate the possibility of using Maraviroc as a pharmacological senotherapeutic for treating age-associated sarcopenia. Lastly, we define transcription factors that govern senescence state and SASP induction in aging muscle and elucidate the key function and mechanism of JUNB in SASP activation. Altogether, our findings demonstrate the prevalence and function of cellular senescence in skeletal muscle and identify a novel pharmacological intervention for sarcopenia.

The prevalence of patellofemoral joint (PFJ) osteoarthritis (OA) after anterior cruciate ligament (ACL) injury was inconsistently reported in the literature. This review summarises the reported prevalence of PFJ OA and risk factors of PFJ OA after ACL injury. PubMed, Embase, WoS, and MEDLINE (OVID) were searched up to 1 March 2019. A modified version of the Coleman methodology score was used to assess the methodological quality of the included studies. Prevalence of PFJ OA was pooled depended on different interventions in ACL injured populations. Thirty-eight studies were included. Five different radiographic classification methods were used: the Kellgren and Lawrence Grade 2, IKDC Grade B, Fairbank Grade 1, joint space narrowing of Grade 2 based on OARSI, and Ahlbäck Grade 1. One included study used MRI Osteoarthritis Knee Score to evaluate PFJ degenerative changes. The overall prevalence of PFJ OA after ACL injury in included studies varied between 4.5% and 80%. The large variation of PFJ OA prevalence is mainly because of different follow-up period and surgical techniques. The pooled data showed that bone-patellar tendon-bone graft, single-bundle ACL reconstruction (ACLR), and delayed ACLR are likely associated with PFJ degenerative changes after ACL injury. ACLR, delayed ACLR, body mass index (BMI), meniscectomy, patellofemoral chondral lesions, age at surgery, and TFJ OA were identified in the literature inducing PFJ OA after ACL injury. Large variations of PFJ OA after ACL injury are associated with different follow-up period and surgical techniques. ACL reconstructed population with bone-patellar tendon-bone graft, single-bundle reconstruction, and delayed operation time has a high prevalence of PFJ OA. This review focuses more on the effect of surgical technique factors on the degenerative changes on PFJ. The results reveal that BPTB, single-bundle reconstruction, and delayed ACLR are more likely associated with PFJ degenerative changes after ACL injury. These findings imply that awareness of PFJ problems after surgical intervention will remind of surgeons taking PFJ into consideration in operations, which is likely to reduce the incidences of anterior knee pain, patellar maltracking, and over-constrained patella in the early stage after surgery.

Loosening is the leading cause of total knee arthroplasty (TKA) revision. This is a heavy burden toward the healthcare system owing to the difficulty in diagnosis and complications occurring from the delay management. Based on automatic analytical model building, machine learning, may potentially help to automatically recognize the risk of loosening based on radiographs alone. The aim of this study was to build an image-based machine-learning model for detecting TKA loosening. Image-based machine-learning model was developed based on ImageNet, Xception model and a TKA patient X-ray image dataset. Based on a dataset with TKA patient clinical parameters, another system was then created for developing the clinical-information-based machine learning model with random forest classifier. In addition, the Xception Model was pre-trained on the ImageNet database with python and TensorFlow deep learning library for the prediction of loosening. Class activation maps were also used to interpret the prediction decision made by model. Two senior orthopaedic specialists were invited to assess loosening from X-ray images for 3 attempts in setting up comparison benchmark. In the image-based machine learning loosening model, the precision rate and recall rate were 0.92 and 0.96, respectively. While for the accuracy rate, 96.3% for visualization classification was observed. However, the addition of clinical-information-based model, with precision rate of 0.71 and recall rate of 0.20, did not further showed improvement on the accuracy. Moreover, as class activation maps showed corresponding signals over bone-implant interface that is loosened radiographically, this confirms that the current model utilized a similar image recognition pattern as that of inspection by clinical specialists. The image-based machine learning model developed demonstrated high accuracy and predictability of knee arthroplasty loosening. And the class activation heatmap matched well with the radiographic features used clinically to detect loosening, which highlighting its potential role in assisting clinicians in their daily practice. However, addition of clinical-information-based machine-learning model did not offer further improvement in detection. As far as we know, this is the first report of pure image-based machine learning model with high detection accuracy. Importantly, this is also the first model to show relevant class activation heatmap corresponding to loosening location. The finding in this study indicated image-based machine learning model can detect knee arthroplasty loosening with high accuracy and predictability, which the class activation heatmap can potentially assist surgeons to identify the sites of loosening.

Large Language Models (LLMs) have gained much attention and, in part, have replaced common search engines as a popular channel for obtaining information due to their contextually relevant responses. Osteoarthritis (OA) is a common topic in skeletal muscle disorders, and patients often seek information about it online. Our study evaluated the ability of 3 LLMs (ChatGPT-3.5, ChatGPT-4.0, and Perplexity) to accurately answer common OA-related queries. We defined 6 themes (pathogenesis, risk factors, clinical presentation, diagnosis, treatment and prevention, and prognosis) based on a generalization of 25 frequently asked questions about OA. Three consultant-level orthopedic specialists independently rated the LLMs' replies on a 4-point accuracy scale. The final ratings for each response were determined using a majority consensus approach. Responses classified as "satisfactory" were evaluated for comprehensiveness on a 5-point scale. ChatGPT-4.0 demonstrated superior accuracy, with 64% of responses rated as "excellent", compared to 40% for ChatGPT-3.5 and 28% for Perplexity (Pearson's χ² test with Fisher's exact test, all p < 0.001). All 3 LLM-chatbots had high mean comprehensiveness ratings (Perplexity = 3.88; ChatGPT-4.0 = 4.56; ChatGPT-3.5 = 3.96, out of a maximum score of 5). The LLM-chatbots performed reliably across domains, except for "treatment and prevention" However, ChatGPT-4.0 still outperformed ChatGPT-3.5 and Perplexity, garnering 53.8% "excellent" ratings (Pearson's χ² test with Fisher's exact test, all p < 0.001). Our findings underscore the potential of LLMs, specifically ChatGPT-4.0 and Perplexity, to deliver accurate and thorough responses to OA-related queries. Targeted correction of specific misconceptions to improve the accuracy of LLMs remains crucial.

Platelet concentrates (PC) generally refers to a group of products that are prepared from autologous blood intended to enhance healing activities. PC therapy is now very popular in treating musculoskeletal injuries; however, inconsistent clinical results urge the need to understand the working mechanism of PC. It is generally believed that the platelet-derived bioactive factors are the active constituents, and their bioavailability in the vicinity of the lesion sites determines the treatment efficacies. Therefore, the composition, localisation, and duration of the action of PC would be key determinants. In this review, we discuss how different preparations and delivery methods of PC would affect the treatment outcomes with respect to clinical evidence about PC therapy for osteoarthritis, tendinopathies, rotator cuff tears, anterior cruciate ligament injuries, and bone fractures. This review can be used as a quick guide for the use of PC therapy and provide insights for the further optimisation of the therapy in the near future.