BackgroundOsteoblasts-Osteoclasts has been a major area in bone disease research for a long time. However, there are few systematic studies in this field using bibliometric analysis. We aimed to perform a bibliometric analysis and visualization study to determine hotspots and trends of osteoblasts-osteoclasts in bone diseases, identify collaboration and influence among authors, countries, institutions, and journals, and assess the knowledge base to develop basic and clinical research in the future.MethodsWe collected articles and reviews for osteoblasts-osteoclasts in bone diseases from the Web of Science Core Collection. In addition, we utilized scientometrics software (CiteSpace5.8 and VOSviewer1.6.18) for visual analysis of countries/regions, institutions, authors, references, and keywords in the field.ResultsIn total, 16,832 authors from 579 institutions in 73 countries/regions have published 3,490 papers in 928 academic journals. The literature in this field is rapidly increasing, with Bone publishing the most articles, whereas Journal of Bone and Mineral Research had the most co-cited journals. These two journals mainly focused on molecular biology and the clinical medicine domain. The countries with the highest number of publications were the US and China, and the University of Arkansas for Medical Sciences was the most active institution. Regarding authors, Stavros C. Manolagas published the most articles, and Hiroshi Takayanagi had the most co-cited papers. Research in this field mainly includes molecular expression and regulatory mechanisms, differentiation, osteoprotection, inflammation, and tumors. The latest research hotspots are oxidative stress, mutation, osteocyte formation and absorption, bone metabolism, tumor therapy, and in-depth mechanisms.ConclusionWe identified the research hotspots and development process of osteoblasts-osteoclasts in bone disease using bibliometric and visual methods. Osteoblasts-osteoclasts have attracted increasing attention in bone disease. This study will provide a valuable reference for researchers concerned with osteoblasts-osteoclasts in bone diseases.

Objectives: The aim of this study was to establish a clinical prediction model to predict the prognosis of diabetic foot ulcer (DFU) patients treated with tibial cortex transverse transport surgery (TTT). Methods: This was a single-center, retrospective study. The clinical data and inspection results were collected by following up on DFU patients hospitalized at the First Affiliated Hospital of Guangxi Medical University and treated with TTT surgery. 202 patients’ clinical data were collected finally and they were divided into two groups according to Wagner’s grading classification. The indicators related to the severity of DFU and with the time of wound healing after surgery were confirmed through logistic regression and Lasso regression analysis. In addition, a clinical prediction model was established. Finally, the intersection of the two sets of indicators yielded the indicators related to wound severity and the postoperative healing time. Results: 202 patients were separated into Wagner 2/3 group(n=135,mean age 62.40±9.68 years) and Wagner 4 group(n=67, mean age 61.12±9.07 years).After the student’s t-test, Lasso regression, and Logistic regression analysis, three factors were identified to describe the severity of the DFU between the two groups: platelet-to-lymphocyte ratio (PLR) (OR: 0.992, 95% CI: 0.987-0.998), monocyte-to-neutrophil ratio (MNR) (OR: 0.000, 95% CI: 0.000-0.050), Hemoglobin (HGB) (OR: 0.965, 95% CI: 0.938-0.992). Univariate COX regression analysis determined 12 factors (PLR, MNR, ect) related to the healing time after the operation. Conclusions: PLR and MNR were factors related to DFU severity and prognosis after TTT surgery.