ObjectiveTo develop and validate a nomogram for predicting the risk of peripheral artery disease (PAD) in patients with type 2 diabetes mellitus (T2DM) and assess its clinical application value.MethodsClinical data were retrospectively collected from 474 patients with T2DM at the Air Force Medical Center between January 2019 and April 2022. The patients were divided into training and validation sets using the random number table method in a ratio of 7:3. Multivariate logistic regression analysis was performed to identify the independent risk factors for PAD in patients with T2DM. A nomogram prediction model was developed based on the independent risk factors. The predictive efficacy of the prediction model was evaluated using the consistency index (C-index), area under the curve (AUC), receiver operating characteristic (ROC) curve, Hosmer-Lemeshow (HL) test, and calibration curve analysis. Additionally, decision curve analysis (DCA) was performed to evaluate the prediction model’s performance during clinical application.ResultsAge, disease duration, blood urea nitrogen (BUN), and hemoglobin (P<0.05) were observed as independent risk factors for PAD in patients with T2DM. The C-index and the AUC were 0.765 (95% CI: 0.711-0.819) and 0.716 (95% CI: 0.619-0.813) for the training and validation sets, respectively, indicating that the model had good discriminatory power. The calibration curves showed good agreement between the predicted and actual probabilities for both the training and validation sets. In addition, the P-values of the HL test for the training and validation sets were 0.205 and 0.414, respectively, indicating that the model was well-calibrated. Finally, the DCA curve indicated that the model had good clinical utility.ConclusionA simple nomogram based on three independent factors–duration of diabetes, BUN, and hemoglobin levels–may help clinicians predict the risk of developing PAD in patients with T2DM.

Background: Tuberculosis (TB) is an old infectious disease caused by Mycobacterium tuberculosis infection. Vaccination is the most effective way to prevent and control TB. However, there is relatively little literature that systematically analyzes the progress of new TB vaccine research from a bibliometric perspective. This study was conducted to examine the development of TB vaccines over the past 20 years and to identify research priorities and directions for the future. Methods: The Science Citation Index Expanded (SCI-E) of the Web of Science Core Collection (WOSCC) database was selected to search the literature related to TB vaccines. The countries, institutions, authors, journals, references, and keywords of each publication were analyzed and visualized using the VOSviewer, CiteSpace, and Bibliometrix software. Furthermore, GraphPad Prism and Microsoft Excel 365 were also used for statistical analysis. Results: As of 20 October 2022, 7960 publications related to TB vaccines were identified with 288,478 citations. The United States of America (USA) accounted for the largest share (2658, 33.40%), followed by the United Kingdom (UK, 1301, 16.34%), and China (685, 8.6%). Regarding affiliations, the University of London had the most publications (427) and shared the highest H-index (76) with the Statens Serum Institut of Denmark. In terms of the number of articles for the journals and authors, the journal Vaccine ranked first with 629 articles. Professor Peter Anderssen has published the highest number of papers (160). The burst keywords and thematic maps analysis showed that future trends in TB vaccine development would focus on exploring the interaction mechanisms between M. tuberculosis and the host. Conclusion: The number of publications on TB vaccines has grown over the past two decades. Developed countries play a significant role in TB vaccine research, and developing countries are fast catching up. We believe that future research will be aimed at understanding the fine molecular mechanisms of host–pathogen interaction, leading to the development of better TB vaccines.

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