Wei Sun

Osteoarthritis (OA) is one of the most common joint diseases worldwide. Recently, cold‑inducible RNA binding protein (CIRP), a novel identified pro‑inflammatory cytokine, was reportedly increased in the synovial fluid of OA patients. However, its function and the underlying mechanism in OA progression remains unclear. Therefore, the current study investigated the role of CIRP in the progression of OA. It was observed that CIRP and matrix metalloproteinases were highly expressed in OA chondrocytes, whereas collagen Ⅱ exhibited low expression levels. Additionally, CIRP was found to be secreted by OA human chondrocytes in the form of exosomes. CIRP treatment influenced inflammatory related signaling pathway, which in turn affected inflammatory response and extracellular matrix (ECM) degradation in human chondrocytes. Additionally, CIRP induced the nuclear translocation of p65 and promoted ECM degradation dependent the Toll‑like receptor 4 (TLR4)/NF‑κB signaling. The present study also reported that CIRP increased the IL‑1β secretion via the NLR family pyrin domain containing 3 (NLRP3) inflammasome. Furthermore, targeting of CIRP by microRNA‑145 or exosome loading with microRNA‑145 attenuated its role in promoting OA both in vitro and in vivo. The findings indicated that CIRP acts as a pro‑inflammatory factor and activates the TLR4/NF‑κB/NLRP3 pathway, which promotes the inflammatory response, ECM degradation and the progression of OA and targeting CIRP could be a novel strategy for OA treatment.

Falls pose a significant threat to older adults’ health, affecting one-third of individuals aged over 65 annually and resulting in severe injuries despite numerous screening tools. While motoric cognitive risk syndrome (MCR) is established as a dementia risk factor, its utility as a fall prediction tool remains unclear due to inconsistent findings across populations. This study therefore aims to elucidate the associations between motoric cognitive risk syndrome and falls among older adults. Participants aged ≥ 60 years were selected from the English Longitudinal Study of Aging (ELSA), the Health and Retirement Study (HRS), and the China Health and Retirement Longitudinal Study (CHARLS). Subjective cognitive complaints (SCC) were assessed based on participants’ responses to standardized questionnaire items. Slow gait was defined as a gait speed of more than one standard deviation below the age- and gender-specific mean values. MCR was diagnosed when both SCC and slow gait were present. Outcomes for investigation included falls, multiple falls, and fall-related injuries. Logistic regression analysis was conducted to examine the longitudinal association between MCR, its components, and the occurrence of falls over the following four years. Ultimately, 10,373 participants were included in the analysis. After adjusting for covariates, MCR was associated with an increased risk of falls over the next four years, with relative increases of 60.0%, 50.5%, and 34.1% observed in the ELSA, HRS, and CHARLS cohorts, respectively. MCR was also linked to an elevated risk of multiple falls, although no significant association was found with fall-related injuries.In the fully adjusted models, slow gait alone did not show an independent association across all cohorts. Only MCR emerged as a significant and stable predictor of future falls, while further research is needed to clarify the role of SCC. This large-scale prospective study found that MCR significantly predicts falls in older adults, highlighting its potential as a clinically useful screening tool integrating cognitive and motor parameters for improved fall risk identification.

[This corrects the article DOI: 10.3389/fmicb.2024.1371679.].

Background: The relationship between gut microbiota and osteoarthritis (OA) occurrence remains unclear. Existing research needs to clearly understand how basal metabolic rate (BMR) regulates this relationship. Therefore, using a two-step bidirectional Mendelian Randomization approach, our study aims to investigate whether BMR levels mediate the causal relationship between gut microbiota and OA.Methods: In this study, we examined publicly available summary statistics from Genome-Wide Association Studies (GWAS) to determine the correlation between gut microbiota and OA. The analysis included one primary dataset and two secondary datasets. Initially, a two-step, two-sample, and reverse MR analysis was performed to identify the causal relationship between gut microbiota and OA. Subsequently, a two-step MR analysis revealed that the relationship between microbiota and OA is mediated by BMR. Sensitivity analyses confirmed the robustness of the study results.Results: In our analysis of the primary dataset, we discovered a positive correlation between three taxa and the outcome of OA, and eight taxa exhibited a negative correlation with the OA outcome. Through comparisons with the secondary dataset and multiple testing corrections, we found a negative association between the class Actinobacteria (OR=0.992886277, p-value = 0.003) and the likelihood of OA occurrence. Notably, knee osteoarthritis (KOA) and hip osteoarthritis (HOA) had a strong negative correlation (OR = 0.927237553/0.892581219). Our analysis suggests that BMR significantly mediates the causal pathway from Actinobacteria to OA, with a mediated effect of 2.59%. Additionally, BMR mediates 3.98% of the impact in the path from the order Bifidobacteriales and the family Bifidobacteriaceae to OA. Besides these findings, our reverse analysis did not indicate any significant effect of OA on gut microbiota or BMR.Conclusion: Our research results indicate that an increase in the abundance of specific gut microbial taxa is associated with a reduced incidence of OA, and BMR levels mediate this causal relationship. Further large-scale randomized controlled trials are necessary to validate the causal impact of gut microbiota on the risk of OA. This study provides new insights into the potential prevention of OA by modulating the gut microbiota.

Non-coding RNAs (ncRNAs), including circular RNAs (circRNAs) and long non-coding RNAs (lncRNAs), are unique RNA molecules widely identified in the eukaryotic genome. Their dysregulation has been discovered and played key roles in the pathogenesis of numerous diseases, including various cancers. Previously considered devoid of protein-coding ability, recent research has revealed that a small number of open reading frames (ORFs) within these ncRNAs endow them with the potential for protein coding. These ncRNAs-derived peptides or proteins have been proven to regulate various physiological and pathological processes through diverse mechanisms. Their emerging roles in disease diagnosis and targeted therapy underscore their potential utility in clinical settings. This comprehensive review aims to provide a systematic overview of proteins or peptides encoded by lncRNAs and circRNAs, elucidate their production and functional mechanisms, and explore their promising applications in cancer diagnosis, disease prediction, and targeted therapy.

Skeletal system disease (SSD) is defined as a class of chronic disorders of skeletal system with poor prognosis and causes heavy economic burden. m6A, methylation at the N6 position of adenosine in RNA, is a reversible and dynamic modification in posttranscriptional mRNA. Evidences suggest that m6A modifications play a crucial role in regulating biological processes of all kinds of diseases, such as malignancy. Recently studies have revealed that as the most abundant epigentic modification, m6A is involved in the progression of SSD. However, the function of m6A modification in SSD is not fully illustrated. Therefore, make clear the relationship between m6A modification and SSD pathogenesis might provide novel sights for prevention and targeted treatment of SSD. This article will summarize the recent advances of m6A regulation in the biological processes of SSD, including osteoporosis, osteosarcoma, rheumatoid arthritis and osteoarthritis, and discuss the potential clinical value, research challenge and future prospect of m6A modification in SSD.

Extracellular vesicles (EVs) are spherical bilayer membrane vesicles released by cells into extracellular spaces and body fluids, including plasma and synovial fluid. EV cargo comprises various biomolecules, such as proteins, DNA, mRNAs, non‑coding RNAs, lipids and metabolites. By delivering these bioactive molecules to recipient cells, EVs mediate intercellular communications and play a critical role in maintaining cellular homeostasis and promoting pathological progression. Of note, cells can selectively sort these bioactive molecules (particularly RNAs) into EVs for secretion, as well as regulate cell‑cell communications. RNA‑binding proteins (RBPs) are a large class of proteins capable of binding to RNA molecules and function in regulating RNA metabolism. There is increasing evidence to indicate that RBPs can be delivered to receipt cells to influence their cell biology and play a significant role in the sorting of coding and non‑coding RNAs in EVs. The present review summarized the current knowledge on EV‑associated RBPs, their functions in tumorigenesis and RBP‑related exosome engineering. It is hoped that the present review may provide novel insight into RBPs and targeted cancer treatment.

Extracellular vehicles play crucial function in osteosarcoma tumorigenesis.Extracellular vehicles mediated the intercellular communication of osteosarcoma cells with other types cells in tumor microenvironment.Extracellular vehicles have potential utility in osteosarcoma diagnosis and treatment.

Background: Machine learning (ML) has been widely utilized for constructing high-performance prediction models. This study aimed to develop a preoperative machine learning-based prediction model to identify functional recovery one year after hip fracture surgery.Methods: We collected data from 176 elderly hip fracture patients admitted to the Department of Orthopaedics and Oncology at Shenzhen Second People's Hospital between May 2019 and December 2019, who met the inclusion criteria. Patient's functional recovery was monitored for one year after surgery. We selected 26 factors, comprising 12 preoperative indicators, 8 surgical indicators, and 6 postoperative indicators. Eventually, 77 patients were included based on the exclusion criteria. Random allocation divided them into the training set (70%) and test set (30%) for internal validation. The Lasso method was employed to screen prognostic variables. We conducted comparisons among various common machine learning classifiers to determine the best prediction model. Prediction performance was evaluated using the area under the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis. To identify the importance of the predictor variables, we performed the recursive feature elimination (RFE) algorithm based on Shapley Additive Explanations (SHAP) values.Results: The AUCs for the testing dataset were as follows: logistic regression (Logit) model = 0.934, k-nearest neighbors (KNN) model = 0.930, support vector machine (SVM) model = 0.910, Gaussian naive Bayes (GNB) model = 0.926, decision tree (DT) model = 0.730, random forest (RF) model = 0.957, and Extreme Gradient Boosting (XGB) model = 0.902. Among the seven ML-based models tested, the RF model demonstrated the best prediction performance, incorporating four features: postoperative rehabilitation compliance, marital status, age-adjusted Charlson comorbidity score (aCCI), and clinical frailty scale (CFS).Conclusion: We developed a prediction model for the functional recovery following hip fracture surgery in elderly patients after one year, based on the Random Forest (RF) algorithm. This model exhibited superior prediction performance (ROC) compared to other models. The software application is available for use. External validation in a larger patient cohort or diverse hospital settings is necessary to assess the clinical utility of this tool.