A rat HIRI model was constructed and treated with an intraperitoneal injection of agomir-miR-494 or agomir-NC (negative control) for 7 days after the surgery. The pathophysiological changes in sham-operated rats, HIRI, HIRI + agomir-miR-494, and HIRI + agomir-NC were compared. The effect of miR-494 was also assessed in an H₂O₂-induced apoptosis model. Hepatic AML12 cells were transfected with mimics NC or miR-494 mimics, followed by 6-h H₂O₂ treatment. Cell proliferation and apoptosis were detected by CCK8 assay and flow cytometry, respectively. Further, the miR-494 target gene was identified by luciferase reporter assay, and verified both in vitro and in vivo experiments. The activity of AKT pathway was further analyzed in vivo by Western blot. HIRI + agomir-miR-494 rats exhibited significantly higher miR-494 expression, lower serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), and glutamate dehydrogenase (GLDH) level, lower hepatic MDA, TOA, and OSI, alleviated hepatic necrosis, reduced hepatocyte apoptosis, and decreased expression of apoptosis-related proteins, when compared with HIRI + agomir-NC rats (P<0.05 or 0.01). After H₂O₂ treatment, AML-12 cells transfected with miR-494 mimics had significantly higher proliferation and lower apoptosis rate compared with mimics NC group (P<0.01). PTEN was identified as an miR-494 target gene. PTEN expression was significantly down-regulated in AML12 cells transfected with miR-494 mimics, and was up-regulated by treatment of miR-494 inhibitor (P<0.01). Moreover, HIRI + agomir-miR-494 rats exhibited significantly lower PTEN expression, and higher p-AKT, p-mTOR, and p-p70S6K levels compared with HIRI + agomir-NC rats. Therefore, miR-494 protected rats against hepatic ischemia/reperfusion (I/R) injury through down-regulating its downstream target gene PTEN, leading to the activation of PI3K/AKT signaling pathway.

Objective: The aim of this study was to assess the diagnostic ability of artificial intelligence (AI) in the detection of early upper gastrointestinal cancer (EUGIC) using endoscopic images. Methods: Databases were searched for studies on AI-assisted diagnosis of EUGIC using endoscopic images. The pooled area under the curve (AUC), sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) with 95% confidence interval (CI) were calculated. Results: Overall, 34 studies were included in our final analysis. Among the 17 image-based studies investigating early esophageal cancer (EEC) detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.98, 0.95 (95% CI, 0.95–0.96), 0.95 (95% CI, 0.94–0.95), 10.76 (95% CI, 7.33–15.79), 0.07 (95% CI, 0.04–0.11), and 173.93 (95% CI, 81.79–369.83), respectively. Among the seven patient-based studies investigating EEC detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.98, 0.94 (95% CI, 0.91–0.96), 0.90 (95% CI, 0.88–0.92), 6.14 (95% CI, 2.06–18.30), 0.07 (95% CI, 0.04–0.11), and 69.13 (95% CI, 14.73–324.45), respectively. Among the 15 image-based studies investigating early gastric cancer (EGC) detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.94, 0.87 (95% CI, 0.87–0.88), 0.88 (95% CI, 0.87–0.88), 7.20 (95% CI, 4.32–12.00), 0.14 (95% CI, 0.09–0.23), and 48.77 (95% CI, 24.98–95.19), respectively. Conclusions: On the basis of our meta-analysis, AI exhibited high accuracy in diagnosis of EUGIC. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/, identifier PROSPERO (CRD42021270443).

Background: Robotic techniques can theoretically overcome the limitations of laparoscopic liver resection and are currently recognized as safe options; however, it is not known which approach is better. The purpose of this study was to compare the advantages of robotic hepatectomy and laparoscopic hepatectomy. Methods: Electronic databases (the Cochrane Library, PubMed (MEDLINE), Embase and Web of Science) were systematically searched from January 2000 to August 2023 for eligible studies that compared robotic hepatectomy and laparoscopic hepatectomy. Studies that met the inclusion criteria were then reviewed systematically. The reported data were aggregated statistically using RevMan 5.4 software. The parameters of interest included intraoperative, postoperative, survival and financial outcomes. Subgroup analysis was performed according to the type and difficulty level of hepatectomy and the study setting. Results: A total of 26 propensity-score matching comparative trials met the inclusion criteria, which comprised 9355 participants (robotic hepatectomy versus laparoscopic hepatectomy: 3938 versus 5417) in the meta-analysis. For surgical outcomes, lower blood loss, lower open conversion rate and higher R0 resection rate were observed in the robotic hepatectomy group compared with the laparoscopic hepatectomy group (mean difference (MD) −86.22, 95% c.i. −116.49 to −55.95, I² = 87%, P < 0.001; OR 0.51, 95% c.i. 0.38 to 0.69, I² = 40%, P < 0.001; OR 1.31, 95% c.i. 1.03 to 1.67, I² = 0%, P = 0.030 respectively). The lower blood loss (major hepatectomy group: MD −56.88, 95% c.i. −109.09 to −4.28, I² = 76%, P = 0.030; IWATE score (advanced/expert more than 80%) group: MD −0.61, 95% c.i. −1.14 to −0.08, I² = 95%, P < 0.001) and lower open conversion rate (major hepatectomy group: OR 0.41, 95% c.i. 0.30 to 0.56, I² = 0%, P < 0.001; IWATE score (advanced/expert less than 80%) group: OR 0.52, 95% c.i. 0.36 to 0.75, I² = 0%, P = 0.659) advantage persisted across subgroup analyses. Conclusion: The robotic approach had advantages to laparoscopic in terms of lower blood loss and reduced rates of open conversion, especially in difficult hepatectomies.

Background: Development of a deep learning method to identify Barrett's esophagus (BE) scopes in endoscopic images. Methods: 443 endoscopic images from 187 patients of BE were included in this study. The gastroesophageal junction (GEJ) and squamous-columnar junction (SCJ) of BE were manually annotated in endoscopic images by experts. Fully convolutional neural networks (FCN) were developed to automatically identify the BE scopes in endoscopic images. The networks were trained and evaluated in two separate image sets. The performance of segmentation was evaluated by intersection over union (IOU). Results: The deep learning method was proved to be satisfying in the automated identification of BE in endoscopic images. The values of the IOU were 0.56 (GEJ) and 0.82 (SCJ), respectively. Conclusions: Deep learning algorithm is promising with accuracies of concordance with manual human assessment in segmentation of the BE scope in endoscopic images. This automated recognition method helps clinicians to locate and recognize the scopes of BE in endoscopic examinations.