Global scientific trends on exosomes therapy for osteoporosis from 2004 to 2023: A bibliometric and visualized analysis.

BACKGROUND: Exosomes have emerged as pivotal mediators in modulating physiological and pathological processes implicated in osteoporosis (OP) through their distinctive mode of intracellular communication. The use of exosomes has evoked considerable interest, catalyzing a surge in research endeavors on a global scale. This study endeavors to scrutinize contemporary landscapes and burgeoning trends in this realm. METHODS: The Web of Science Core Collection was used to retrieve publications on exosomes therapy for OP within the time frame of January 1, 2004 to December 31, 2023. The bibliometric methodology was applied to study and index the collected data. VOSviewer and citespace software were used to conduct visualization, co-authorship, co-occurrence, and publication trend analyses of exosome therapy in OP. RESULTS: A total of 610 publications (443 articles and 167 reviews) from 51 countries and 911 institutions were included in this study. Shanghai Jiao Tong University, Central South University, Sichuan University, and Zhejiang University are leading research institutions in this field. Stem Cell Research Therapy published the highest number of articles and has emerged as the most cited journal. Of the 4077 scholars who participated in the study, Xie, Hui, Zhang, Yan, Tan, and Yi-Juan had the largest number of articles. Furthermore, according to the cluster analysis of external keywords, future research hotspots can be categorized into 3 directions: research status of exosomes for the treatment of OP, treatment of OP through exosome-regulated signaling pathways, and exosomes as targeted drug delivery systems. CONCLUSION: This study suggests that the number of future publications on exosome therapy for OP will increase, with a focus on fundamental investigations into drug-loading capacities and molecular mechanisms. In summary, this study presents the first systematic bibliometric analysis of exosome therapy publications in OP, providing an objective and comprehensive overview of the field and a valuable reference for researchers in this domain.

A machine learning-based model for clinical prediction of distal metastasis in chondrosarcoma: a multicenter, retrospective study.

Background: The occurrence of distant metastases (DM) limits the overall survival (OS) of patients with chondrosarcoma (CS). Early diagnosis and treatment of CS remains a great challenge in clinical practice. The aim of this study was to investigate metastatic factors and develop a risk stratification model for clinicians' decision-making. Methods: Six machine learning (ML) algorithms, including logistic regression (LR), plain Bayesian classifier (NBC), decision tree (DT), random forest (RF), gradient boosting machine (GBM) and extreme gradient boosting (XGBoost). A 10-fold cross-validation was performed for each model separately, multicenter data was used as external validation, and the best (highest AUC) model was selected to build the network calculator. Results: A total of 1,385 patients met the inclusion criteria, including 82 (5.9%) patients with metastatic CS. Multivariate logistic regression analysis showed that the risk of DM was significantly higher in patients with higher pathologic grades, T-stage, N-stage, and non-left primary lesions, as well as those who did not receive surgery and chemotherapy. The AUC of the six ML algorithms for predicting DM ranged from 0.911-0.985, with the extreme gradient enhancement algorithm (XGBoost) having the highest AUC. Therefore, we used the XGB model and uploaded the results to an online risk calculator for estimating DM risk. Conclusions: In this study, combined with adequate SEER case database and external validation with data from multicenter institutions in different geographic regions, we confirmed that CS, T, N, laterality, and grading of surgery and chemotherapy were independent risk factors for DM. Based on the easily available clinical risk factors, machine learning algorithms built the XGB model that predicts the best outcome for DM. An online risk calculator helps simplify the patient assessment process and provides decision guidance for precision medicine and long-term cancer surveillance, which contributes to the interpretability of the model.

Radical-Scavenging and Subchondral Bone-Regenerating Nanomedicine for Osteoarthritis Treatment.

Osteoarthritis (OA) is characterized by cartilage degradation and subchondral bone remodeling. However, most available studies focus on either cartilage degradation or subchondral bone lesion, alone, and rarely pay attention to the synergy of these two pathological changes. Herein, a dual-functional medication is developed to simultaneously protect cartilage and achieve subchondral bone repair. Black phosphorus nanosheets (BPNSs), with a strong reactive oxygen species (ROS)-scavenging capability and high biocompatibility, also present a notable promoting effect in osteogenesis. BPNSs efficiently eliminate the intracellular ROS and, thus, protect the inherent homeostasis between cartilage matrix anabolism and catabolism. RNA sequencing results of BPNSs-treated OA chondrocytes further reveal the restoration of chondrocyte function, activation of antioxidant enzymes, and regulation of inflammation. Additional in vivo assessments solidly confirm that BPNSs inhibit cartilage degradation and prevent OA progression. Meanwhile, histological evaluation and microcomputed tomography (micro-CT) scanning analysis verify the satisfying disease-modifying effects of BPNSs on OA. Additionally, the excellent biocompatibility of BPNSs enables them as a competitive candidate for OA treatment. This distinct disease-modifying treatment of OA on the basis of BPNSs provides an insight and paradigm on the dual-functional treatment strategy focusing on both cartilage degradation and subchondral bone lesion in OA and explores a broader biomedical application of BPNS nanomedicine in orthopedics.