Background: Hyaluronan (HA) metabolism by chondrocytes is important for cartilage development and homeostasis. However, information about the function of circular RNAs (circRNAs) in HA metabolism is limited. We therefore profiled the role of the novel HA-related circRNA circHYBID in the progression of osteoarthritis (OA). Methods: CircHYBID function in HA metabolism in chondrocytes was investigated using gain-of-function experiments, and circHYBID mechanism was confirmed via bioinformatics analysis and luciferase assays. The expression of circHYBID–hsa-miR-29b-3p–transforming growth factor (TGF)-β1 axis was examined by quantitative real-time polymerase chain reaction (qRT-PCR) and Western blotting. CircHYBID, TGF-β1, and HA levels in cartilage samples were evaluated using qRT-PCR and pathological examination. Enzyme-linked immunosorbent assay was used to assess HA accumulation in chondrocyte supernatant. Results: CircHYBID expression was significantly downregulated in damaged cartilage samples compared with that in the corresponding intact cartilage samples. CircHYBID expression was positively correlated with alcian blue score. Interleukin-1β stimulation in chondrocytes downregulated circHYBID expression and decreased HA accumulation. Gain-of-function experiments revealed that circHYBID overexpression in chondrocytes increased HA accumulation by regulating HA synthase 2 and HYBID expression. Further mechanism analysis showed that circHYBID upregulated TGF-β1 expression by sponging hsa-miR-29b-3p. Conclusions: Our results describe a novel HA-related circRNA that could promote HA synthesis and accumulation. The circHYBID–hsa-miR-29b-3p–TGF-β1 axis may play a powerful regulatory role in HA metabolism and OA progression. Thus, these findings will provide new perspectives for studies on OA pathogenesis, and circHYBID may serve as a potential target for OA therapy.

Combining disinfection and bone regeneration in a one-step treatment is of significant clinical importance for chronic osteomyelitis, yet it remains a considerable challenge. To address this, we developed a dual stimulus-responsive decellularized extracellular matrix (dECM) cryogel (GC-dECM@CPN). The cryogel is composed of methacrylate gelatin (GelMA), carboxymethyl chitosan (CMCS), dECM, and temperature-sensitive phase-transition copper peroxide nanoparticles (CPNs). During the infection phase of chronic osteomyelitis, the cryogel exhibits a strong photothermal conversion efficiency under near-infrared (NIR) light irradiation, triggering the release of CPNs to perform chemodynamic therapy (CDT) in the acidic, infected environment. This process enhances macrophage polarization toward the M1 phenotype, promoting the phagocytosis of residual bacteria. Following bacterial eradication, the dECM within the cryogel facilitates the differentiation of bone marrow mesenchymal stem cells (BMSCs) into osteoblasts while reprogramming macrophages into the M2 phenotype, thereby establishing a conducive environment for bone regeneration. The GC-dECM@CPN cryogel exhibited desirable therapeutic efficacy in both in vitro and in vivo studies, with critical stages of chronic osteomyelitis—including early infection, initial bone regeneration, and recovery—investigated and discussed in detail. This study introduces an innovative dual stimulus-responsive dECM cryogel that effectively treats chronic osteomyelitis through a one-step surgical approach, offering valuable insights for clinical therapeutic strategies in this field.

Emerging evidence suggests that gut microbiota imbalances may influence the onset of musculoskeletal disorders (MSDs), yet conclusive evidence establishing causation remains limited. This study investigates the causal relationship between gut microbiota and a range of MSDs, aiming to identify potential therapeutic targets. Using data on 211 gut microbiome taxa from a genome-wide association study (GWAS) and summary statistics for 26 MSDs from the Finnish Biobank, we employed Mendelian randomization (MR) with inverse-variance weighting (IVW) as the primary analytical approach, complemented by Bayesian model validation to ensure robust results. Our MR analyses revealed significant causal associations between gut microbiota and nine MSDs within four categories, including osteoporosis (IVW-Beta = 0.011, P = 0.025), rheumatoid arthritis (IVW-Beta = − 0.016, P < 0.001), rotator cuff syndrome (IVW-Beta = − 0.007, P = 0.022), and calcific tendonitis of the shoulder (IVW-Beta = − 0.021, P = 0.034). Bayesian validation underscored the plausibility of these relationships, supporting the potential causal role of gut microbiota in the development of these disorders. Our findings present a library of causal associations that underscore the gut microbiome's role in MSD pathogenesis, providing genetic evidence that highlights specific gut microbiota taxa as prospective therapeutic targets. This research offers novel insights into the pathogenic mechanisms underlying MSDs and points toward new directions for future investigation into microbiome-based therapies.

Background: Methods for improving the safety of medical treatments for patients, reducing the occurrence of postoperative complications and optimizing medical resources for primary hip replacement are needed. Previous literature has mostly analysed the risk factors and constructed and models to predict a transfer to the ICU after surgery, and no reports on preoperative ICU reservations have been found. This study evaluated the risk factors for preoperative ICU reservation and considered the necessity of preoperative ICU reservations to optimize preoperative communication, enable a seamless transfer between the operating room and ICU, reduce postoperative complications and shorten hospital stays. Methods: We extracted the data of 1488 patients who underwent hip replacement from the hospital case database from November 2017 to May 2021 and used the case–control test to divide the patients into the case group (scheduled ICU admission, 134 cases) and the control group (Not scheduled ICU admission, 213 cases). The general conditions of the patients before surgery, including sex, age, Charlson comorbidity index, laboratory test results, and anaesthesia methods, were collected and used as independent variables. The t test, rank sum test, and X² test were used to analyse and identify significant factors with a P < 0.05. Then, these factors were entered into binary logistic regression analysis, and a ROC curve was used to test the efficacy of the regression model. Results: In the data we collected, 134 patients were planned to be transferred to the ICU, and 213 patients were not transferred to the ICU. The two groups of data were analyzed by logistic regression. We defined the risk factors for preoperative ICU appointment in patients with primary hip arthroplasty, including age. (odds ratio (OR) 1.066, 95% (confidence interval) CI (1.039, 1.093), P < 0.001), general anesthesia ( (OR) 1.821, 95%CI (1.165, 2.845), P = 0.008), preoperative C-reactive protein ((OR) 1.016, 95%CI (1.010, 1.022), P < 0.05), preoperative alanine aminotransferase ((OR) 1.042, 95%CI ((1.016, 1.070)), P = 0.002). These were promoting factors for preoperative ICU appointment,and preoperative albumin ((OR) 0.0839, 95%CI (0.792, 0.889)), P < 0.05) was a protective factor for ICU appointment. Conclusion: For patients requiring primary hip replacement. Age, general anesthesia, preoperative C-reactive protein, preoperative alanine aminotransferase and preoperative albumin are the key points of our preoperative assessment. Paying attention to the changes of these indicators will help surgeons assess the patient's condition and contact the ICU in advance.These data can be fully understood by the patients' families, reduce the unnecessary use of medical resources, and optimize perioperative management.

Background: With the development of hip arthroplasty technology and rapid rehabilitation theory, the number of hip arthroplasties in elderly individuals is gradually increasing, and their satisfaction with surgery is also gradually improving. However, for elderly individuals, many basic diseases, poor nutritional status, the probability of surgery, anaesthesia and postoperative complications cannot be ignored. How to reduce the incidence of postoperative complications, optimize medical examination for elderly patients, and reasonably allocate medical resources. This study focuses on the construction of a clinical prediction model for planned transfer to the ICU after hip arthroplasty in elderly individuals. Methods: We retrospectively analysed 325 elderly patients who underwent hip arthroplasty. The general data and preoperative laboratory test results of the patients were collected. Univariate and multivariate logistic regression analyses were performed to screen independent influencing factors. The backwards LR method was used to establish the prediction model. Then, we assessed and verified the degree of discrimination, calibration and clinical usefulness of the model. Finally, the prediction model was rendered in the form of a nomogram. Results: Age, blood glucose, direct bilirubin, glutamic-pyruvic transaminase, serum albumin, prothrombin time and haemoglobin were independent influencing factors of planned transfer to the ICU after hip arthroplasty. The area under the curve (AUC) of discrimination and the 500 bootstrap internal validation AUC of this prediction model was 0.793. The calibration curve fluctuated around the ideal curve and had no obvious deviation from the ideal curve. When the prediction probability was 12%-80%, the clinical decision curve was above two extreme lines. The discrimination, calibration and clinical applicability of this prediction model were good. The clinical prediction model was compared with the seven factors in the model for discrimination and clinical use. The discrimination and clinical practicability of this prediction model were superior to those of the internal factors. Conclusion: The prediction model has good clinical prediction ability and clinical practicability. The model is presented in the form of a linear graph, which provides an effective reference for the individual risk assessment of patients.