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Total laparoscopy-assisted total gastrectomy with Da Vinci robotic system conducted by robotic enhanced neurocomputing joint intelligence gastrointestinal surgery hub (RENJI-GISH): a preliminary clinical study and case report.
World J Surg Oncol
March 2025
Department of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Background: On August 7, 2024, the inaugural total laparoscopy-assisted total gastrectomy with the Da Vinci robotic system was performed in the department of gastrointestinal surgery of Renji Hospital, Shanghai Jiaotong University School of Medicine. The procedure, conducted by RENJI-GISH, employed the use of a Da Vinci robot system in conjunction with the Vision Pro and SonoScape medical electronic endoscopy system. This phenomenon has not been documented in the field of gastric cancer surgery The objective of this study is to investigate the safety, feasibility, and surgical effect of the first total laparoscopy-assisted total gastrectomy with the Da Vinci robotic system, conducted by the Robotic Enhanced Neurocomputing Joint Intelligence Gastrointestinal Surgery Hub (RENJI-GISH).
View Article and Find Full Text PDFGastric stress events impact the bioavailability of a poorly soluble weak base dabigatran from pellet-filled capsules: An outcome from pharmacokinetic simulations based on biorelevant dissolution testing, machine learning, and a novel timewise first-order dissolution model.
Int J Pharm
March 2025
Physiolution, 74 Piłsudskiego St. 50-020 Wrocław, Poland.
Current physiologically-based biopharmaceutics modeling (PBBM) neglects the effect of gastrointestinal stress events on the disintegration and dissolution of oral solid dosage forms. Biorelevant dissolution testing can simulate the behavior of drug products under physiological agitation but a workload limits variability examination. In this study, we overcame these deficiencies by inputting dissolution profiles generated by machine-learning (ML) into PBBM-based simulations.
View Article and Find Full Text PDFBackground: We assessed the feasibility and safety of a fully endoscopic one-anastomosis gastric bypass (OAGB) procedure in obese adult minipigs, with short bypass limb length if feasible or full length in a sham-controlled study.
Methods: A natural orifice transluminal endoscopic surgery (NOTES) OAGB procedure was performed in 4 obese adult Yucatan minipigs. Weight change was compared to 2 sham-controlled animals.
Deuterium Metabolic Imaging of the Human Abdomen at Clinical Field Strength.
Invest Radiol
March 2025
From the Department of Radiology, University of Cambridge, Cambridge, UK (P.W., M.A.M., I.H.-M., J.R.B., M.J.Z.-M., A.G., E.L., M.W., F.A.G.); University of Cambridge, Cancer Research UK Cambridge Institute, Cambridge, UK (M.A.M., F.A.G.); and GE HealthCare, Munich, Germany (R.F.S.).
Objectives: The aim of the study was to translate abdominal deuterium metabolic imaging (DMI) to clinical field strength by optimizing the radiofrequency coil setup, the administered dose of deuterium (2H)-labeled glucose, and the data processing pipeline for quantitative characterization of DMI signals over time. This was assessed in the kidney and liver to establish a basis for routine clinical studies in the future.
Materials And Methods: 5 healthy volunteers were recruited and imaged on 2 or 3 separate occasions, with varying doses of 2H-glucose: 0.
Identification and Validation of Four Serum Biomarkers With Optimal Diagnostic and Prognostic Potential for Gastric Cancer Based on Machine Learning Algorithms.
Cancer Med
March 2025
Department of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Background: Gastric cancer (GC) is considered a highly heterogeneous disease, and currently, a comprehensive approach encompassing molecular data from various biological levels is lacking.
Methods: This study conducted different analyses, including the identification of differentially expressed genes (DEGs), weighted correlation networks (WGCNA), single-cell RNA sequencing (scRNA-seq), mRNA expression-based stemness index (mRNAsi), and multiCox analysis, utilizing data from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Subsequently, the machine learning algorithms including least absolute shrinkage and selection operator (LASSO) regression and random forest (RF), combined with multiCox analysis were exploited to identify hub genes.
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