A comparative study of portoenterostomy and choledochojejunostomy in New Zealand rabbits under immunosuppression
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A retrospective study on influencing factors of postoperative hospital stay and development of a predictive scoring model for elderly patients (≥70 years) with colorectal cancer undergoing laparoscopic radical resection
Background: With the accelerating global population aging, the proportion of elderly patients with colorectal cancer (CRC) undergoing laparoscopic radical resection is increasing annually. However, significant individual variations in postoperative hospital stay exist, and convenient clinical prediction tools remain lacking. This study aimed to develop and validate a simplified predictive scoring model for postoperative hospital stay in elderly CRC patients after laparoscopic radical resection. Read More
Analysis of risk factors associated with the development and postoperative complications of complicated acute appendicitis in elderly patients
Objective: Based on an analysis of large-scale retrospective case data, this study aimed to identify the risk factors associated with the development and postoperative complications of complicated acute appendicitis (CAA) in elderly patients (>60 years). Read More
Optimal surgical approach for cT3/T4 rectal cancer post-neoadjuvant chemoradiotherapy: robotic surgery versus TaTME guided by superior pelvic diaphragm localization
Luo Hai Lv Qiang Wang Haichuan Research 29 November 2025 Article: 46 Read More
EIGHT YEAR SINGLE CENTER EXPERIENCE WITH DOUBLE LAYER CAROTID STENT
Gianbattista Parlani, Giacomo Isernia, Lydia Romano, ... Piergiorgio Cao, Massimo Lenti, Gioele Simonte Published online: November 28, 2025 Read More
Minimally Invasive versus Complete Sternotomy for Reimplantation Valve-Sparing Aortic Root Replacement
Epidermal Growth Factor Receptor Exon 20 Insertion in Early Resected Non-Small Cell Lung Cancer: A Retrospective, Single-Center Study in Taiwan Read Mora
Artificial Intelligence in melanoma research: a bibliometric analysis
Abstract Background: As one of the most lethal skin cancers, melanoma has encountered many obstacles in diagnosis and therapy. Artificial Intelligence (AI) can help improve early diagnosis, prognosis, and treatment of melanoma. However, there is a lack of detailed and accurate bibliometric analysis of the field. Methods: All publications were extracted from Web of Science Core Collection based on AI and melanoma terms. Bibliometric analysis was conducted on 1,476 articles/reviews by using VOSviewer, CiteSpace and bibliometrix for co-authorship, citation, keyword and journal analysis. Read More