Safety and efficacy of remimazolam versus propofol during EUS: a multicenter randomized controlled study.

BACKGROUND AND AIMS: Propofol, a widely used sedative in GI endoscopic procedures, is associated with cardiorespiratory suppression. Remimazolam is a novel ultrashort-acting benzodiazepine sedative with rapid onset and minimal cardiorespiratory depression. This study compared the safety and efficacy of remimazolam and propofol during EUS procedures. METHODS: A multicenter randomized controlled study was conducted between October 2022 and March 2023 in patients who underwent EUS procedures. Patients were randomly assigned to receive either remimazolam or propofol as a sedative agent. The primary endpoint was cardiorespiratory adverse events (AEs) during the procedure, including desaturation, respiratory depression, hypotension, and tachycardia. Secondary endpoints were the time to achieve sedation, recovery time, quality of sedation, pain at the injection site, and satisfaction of both endoscopists and patients. RESULTS: Four hundred patients enrolled in the study: 200 received remimazolam (10.8 ± 7.7 mg) and 200 received propofol (88.0 ± 49.1 mg). For cardiorespiratory AEs, the remimazolam group experienced fewer occurrences than the propofol group (8.5% vs 16%, P = .022). A nonsignificant trend was found toward less oxygen desaturation (1.0% vs 3.5%, P = .09), respiratory depression (.5% vs 1.5%, P = .62), hypotension (2.5% vs 5.5%, P = .12), and tachycardia (4.5% vs 5.5%, P = .68) with remimazolam than with propofol. Remimazolam showed a shorter induction time than propofol while maintaining comparable awakening and recovery times. Injection site pain was significantly lower in the remimazolam group than in the propofol group. The remimazolam group demonstrated a significantly higher quality of sedation and satisfaction scores than the propofol group, as evaluated by both endoscopists and patients. CONCLUSIONS: Remimazolam was superior to propofol in terms of safety and efficacy during EUS examinations. (Clinical trial registration number: KCT 0007643.).

Convolutional neural network model for automatic recognition and classification of pancreatic cancer cell based on analysis of lipid droplet on unlabeled sample by 3D optical diffraction tomography.

INTRODUCTION: Pancreatic cancer cells generally accumulate large numbers of lipid droplets (LDs), which regulate lipid storage. To promote rapid diagnosis, an automatic pancreatic cancer cell recognition system based on a deep convolutional neural network was proposed in this study using quantitative images of LDs from stain-free cytologic samples by optical diffraction tomography. METHODS: We retrieved 3D refractive index tomograms and reconstructed 37 optical images of one cell. From the four cell lines, the obtained fields were separated into training and test datasets with 10,397 and 3,478 images, respectively. Furthermore, we adopted several machine learning techniques based on a single image-based prediction model to improve the performance of the computer-aided diagnostic system. RESULTS: Pancreatic cancer cells had a significantly lower total cell volume and dry mass than did normal pancreatic cells and were accompanied by greater numbers of lipid droplets (LDs). When evaluating multitask learning techniques utilizing the EfficientNet-b3 model through confusion matrices, the overall 2-category accuracy for cancer classification reached 96.7 %. Simultaneously, the overall 4-category accuracy for individual cell line classification achieved a high accuracy of 96.2 %. Furthermore, when we added the core techniques one by one, the overall performance of the proposed technique significantly improved, reaching an area under the curve (AUC) of 0.997 and an accuracy of 97.06 %. Finally, the AUC reached 0.998 through the ablation study with the score fusion technique. DISCUSSION: Our novel training strategy has significant potential for automating and promoting rapid recognition of pancreatic cancer cells. In the near future, deep learning-embedded medical devices will substitute laborious manual cytopathologic examinations for sustainable economic potential.