Hot spots and frontiers in bone-tendon interface research: a bibliometric analysis and visualization from 2000 to 2023.

Objective: In this research, we investigated the current status, hotspots, frontiers, and trends of research in the field of bone-tendon interface (BTI) from 2000 to 2023, based on bibliometrics and visualization and analysis in CiteSpace, VOSviewer, and a bibliometric package in R software. Methods: We collected and organized the papers in the Web of Science core collection (WoSCC) for the past 23 years (2000-2023), and extracted and analyzed the papers related to BTI. The extracted papers were bibliometrically analyzed using CiteSpace for overall publication trends, authors, countries/regions, journals, keywords, research hotspots, and frontiers. Results: A total of 1,995 papers met the inclusion criteria. The number of papers published and the number of citations in the field of BTI have continued to grow steadily over the past 23 years. In terms of research contribution, the United States leads in terms of the number and quality of publications, number of citations, and collaborations with other countries, while the United Kingdom and the Netherlands lead in terms of the average number of citations. The University of Leeds publishes the largest number of papers, and among the institutions hosting the 100 most cited papers Hospital for Special Surgery takes the top spot. MCGONAGLE D has published the highest number of papers (73) in the last 10 years. The top three clusters include #0 "psoriatic arthritis", #1 "rotator cuff repair", and #2 "tissue engineering". The structure and function of the BTI and its key mechanisms in the healing process are the key to research, while new therapies such as mechanical stimulation, platelet-rich plasma, mesenchymal stem cells, and biological scaffolds are hot topics and trends in research. Conclusion: Over the past 23 years, global research on the BTI has expanded in both breadth and depth. The focus of research has shifted from studies concentrating on the structure of the BTI and the disease itself to new therapies such as biomaterial-based alternative treatments, mechanical stimulation, platelet-rich plasma, etc.

Pancreatic cancer challenge in 52 Asian countries: age-centric insights and the role of modifiable risk factors (1990-2019).

Background: Pancreatic cancer is renowned for its elevated incidence and mortality rates on a global scale. The disease burden of pancreatic cancer is anticipated to increase, particularly in Asia, due to its vast and rapidly aging population. Methods: Data from the Global Burden of Disease 2019 were analyzed for pancreatic cancer burden across 52 countries in Asia, including the incidence, mortality, and disability-adjusted life years (DALY) for pancreatic cancer, with a focus on risk factors such as high body mass index (BMI), elevated fasting plasma glucose, and smoking. We applied the Estimated Annual Percentage Change, the Age-Period-Cohort model, and decomposition analysis to evaluate incidence trends and effects. Results: From 1990 to 2019, both incidence and mortality rates of pancreatic cancer in Asia significantly increased, with an average annual standardized incidence rate change of 1.73%. Males consistently exhibited higher rates than females, with smoking as a key risk factor. Central Asia reported the highest rates, and South Asia the lowest. The incidence rose with age, peaking in those aged 70~74. The disease burden increased in all age groups, particularly in populations aged 55 and above, representing 84.41% of total cases in 2019, up from 79.01% in 1990. Pancreatic cancer ranked the fifth in incidence among six major gastrointestinal tumors but presented a significant growth rate of mortality and DALY. Conclusion: With the growing, aging population in Asia, the pancreatic cancer burden is projected to escalate, bringing a significant public health challenge. Hence, comprehensive public health strategies emphasizing early detection, risk modification, and optimized treatment of pancreatic cancer are imperative.

Construction of a molecular inflammatory predictive model with histone modification-related genes and identification of CAMK2D as a potential response signature to infliximab in ulcerative colitis.

Background: Ulcerative colitis (UC) is a lifelong inflammatory disease affecting the rectum and colon with numerous treatment options that require an individualized treatment plan. Histone modifications regulate chromosome structure and gene expression, resulting in effects on inflammatory and immune responses. However, the relationship between histone modification-related genes and UC remains unclear. Methods: Transcriptomic data from GSE59071 and GSE66407 were obtained from the Gene Expression Omnibus (GEO), encompassing colonic biopsy expression profiles of UC patients in inflamed and non-inflamed status. Differentially expressed gene (DEG) analyses, functional enrichment analyses, weighted gene co-expression network analysis (WGCNA), and random forest were performed to identify histone modification-related core genes associated with UC inflammation. Features were screened through the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), establishing a molecular inflammatory predictive model using logistic regression. The model was validated in the GSE107499 dataset, and the performance of the features was assessed using receiver operating characteristic (ROC) and calibration curves. Immunohistochemistry (IHC) staining of colonic biopsy tissues from UC patients treated with infliximab was used to further confirm the clinical application value. Univariate logistic regression on GSE14580 highlighted features linked to infliximab response. Results: A total of 253 histone modification-related DEGs were identified between inflammatory and non-inflammatory patients with UC. Seven key genes (IL-1ß, MSL3, HDAC7, IRF4, CAMK2D, AUTS2, and PADI2) were selected using WGCNA and random forest. Through univariate logistic regression, three core genes (CAMK2D, AUTS2, and IL-1ß) were further incorporated to construct the molecular inflammatory predictive model. The area under the curve (AUC) of the model was 0.943 in the independent validation dataset. A significant association between CAMK2D protein expression and infliximab response was observed, which was validated in another independent verification set of GSE14580 from the GEO database. Conclusion: The molecular inflammatory predictive model based on CAMK2D, AUTS2, and IL-1ß could reliably distinguish the mucosal inflammatory status of UC patients. We further revealed that CAMK2D was a predictive marker of infliximab response. These findings are expected to provide a new evidence base for personalized treatment and management strategies for UC patients.