Anti-angiogenic and antitumor effects of anlotinib combined with bevacizumab for colorectal cancer.

BACKGROUND: The progression and metastasis of tumors are typically accompanied by angiogenesis. Crucially, vascular endothelial growth factor (VEGF) and its receptors (VEGFRs) play a significant role in tumor-associated angiogenesis. In this study, the aim was to investigate the antitumor effect of combining bevacizumab (Bev) with anlotinib (An) on colorectal cancer (CRC). METHODS: The CCK-8 assay, EdU assay, and Annexin V staining were conducted to evaluate the proliferation and apoptosis of CRC cells in vitro. The migration capability of CRC cells and HUVECs was assessed using the Transwell assay. Additionally, the tube formation capability of HUVECs was investigated. Furthermore, the antitumor and antiangiogenic effects were evaluated in the BALB/c mice model using immunohistochemistry, TUNEL staining, and 18F-FDG PET/CT imaging. Finally, we analyzed the inhibitory effect of Bev and/or An on related signaling effectors through western blotting. RESULTS: The in vivo CRC mice model revealed that the combination of Bev + An significantly suppressed tumor formation and angiogenesis. Bev + An inhibited tumor glucose metabolism and increased the median survival period in tumor-bearing mice. Mechanistically, the expressions of VEGF, VEGFR2, PDGFR, and FGFR, as well as the phosphorylation levels of AKT, were inhibited after Bev+An treatment. In conclusion, the dual vertical targeting of VEGF and VEGFR in the CRC mice model strongly inhibited tumor growth and angiogenesis, with the suppression of the AKT signaling pathway playing a partial role.

A novel artificial intelligence model for measuring fetal intracranial markers during the first trimester based on two-dimensional ultrasound image.

OBJECTIVE: To establish reference ranges of fetal intracranial markers during the first trimester and develop the first novel artificial intelligence (AI) model to measure key markers automatically. METHODS: This retrospective study used two-dimensional (2D) ultrasound images from 4233 singleton normal fetuses scanned at 11+0-13+6 weeks of gestation at the Affiliated Suzhou Hospital of Nanjing Medical University from January 2018 to July 2022. We analyzed 10 key markers in three important planes of the fetal head. Based on these, reference ranges of 10 fetal intracranial markers were established and an AI model was developed for automated marker measurement. AI and manual measurements were compared to evaluate differences, correlations, consistency, and time consumption based on mean error, Pearson correlation analysis, intraclass correlation coefficients (ICCs), and average measurement time. RESULTS: The results of AI and manual methods had strong consistency and correlation (all ICC values >0.75, all r values >0.75, and all P values <0.001). The average absolute error of both only ranged from 0.124 to 0.178 mm. AI achieved a 100% detection rate for abnormal cases. Additionally, the average measurement time of AI was only 0.49 s, which was more than 65 times faster than the manual measurement method. CONCLUSION: The present study first established the normal standard reference ranges of fetal intracranial markers based on a large Chinese population data set. Furthermore, the proposed AI model demonstrated its capability to measure multiple fetal intracranial markers automatically, serving as a highly effective tool to streamline sonographer tasks and mitigate manual measurement errors, which can be generalized to first-trimester scanning.