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Diethylamine nitrosamine (DEN), as an initiator of liver tumor, and carbon tetrachloride (CCl4), as a tumor promoter, have been used to study the molecular events of liver cancer in animal models. Recently, our in vitro study reported BLE (Berberis lycium Royle ethanol extract) as the most effective agent against liver cancer, thus we continued our study in vivo to assess the hepatoprotective effect of BLE and its most active alkaloid, berberine, in albino mice (70 male). Moreover, we investigated the biochemical/immunohistochemical effects of a single alkaloid versus the effect of Berberis extract in mice liver. Hepatic cancer was induced in mice by a single intraperitoneal injection with DEN (100 mg/kg b.wt), followed by biweekly injections of CCl4 (0.5 mL/kg) for 30 days. The development of liver cancer was assessed after 60 days of DEN injection by measuring the elevated level of the serum tumor marker alpha-fetoprotein (AFP) and liver function test (ALT, AST, ALP, and BUN) markers. After the confirmation of liver cancer development, the BLE extract and berberine were fed to mice for 90 days and the serum biomarkers for liver injury (LFTs and AFP) were measured again. Overall, berberine (120 mg/kg b.wt) proved to be a stronger agent in reducing the symptoms of HCC in mice, as compared with BLE. Histopathological analysis agreed well with the biochemical observations. Immunohistochemistry analysis suggested significant suppression of the quantitative expression of the key oncogene cyclin D1 at low (60 mg/kg) and high (120 mg/kg) doses of berberine. These findings implicate the amelioration of hepatocarcinoma by berberine more prominently in mice, by suppression of cyclin-dependent kinase activator (CD1) expression, reducing LFTs, as well as AFP, in the serum. Thus, our findings are novel, as berberine may help in controlling the perturbation in CD1 associated with aggressive forms of HCC. However, future studies should be directed at finding out whether berberine has any effect on inhibitors (p27 and CDKI) of cyclin-dependent kinase too.

Scanning tunneling microscopy (STM) imaging has been routinely applied in studying surface nanostructures owing to its capability of acquiring high-resolution molecule-level images of surface nanostructures. However, the image analysis still heavily relies on manual analysis, which is often laborious and lacks uniform criteria. Recently, machine learning has emerged as a powerful tool in material science research for the automatic analysis and processing of image data. In this paper, we propose a method for analyzing molecular STM images using computer vision techniques. We develop a lightweight deep learning framework based on the YOLO algorithm by labeling molecules with its keypoints. Our framework achieves high efficiency while maintaining accuracy, enabling the recognitions of molecules and further statistical analysis. In addition, the usefulness of this model is exemplified by exploring the length of polyphenylene chains fabricated from on-surface synthesis. We foresee that computer vision methods will be frequently used in analyzing image data in the field of surface chemistry.