ABSTRACT
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.
Subject(s)
Bibliometrics , Exosomes , Osteoporosis , Humans , Osteoporosis/therapyABSTRACT
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.