Carnosine (β-alanyl-L-histidine), described as an enigmatic peptide for its antioxidant, anti-aging and especially antiproliferation properties, has been demonstrated to play an anti-tumorigenic role in certain types of cancer. However, its function in human gastric carcinoma remains unclear. In this study, the effect of carnosine on cell proliferation and its underlying mechanisms were investigated in the cultured human gastric carcinoma cells. The mTOR signaling axis molecules were analyzed in carnosine treated cells. The results showed that treatment with carnosine led to proliferation inhibition, cell cycle arrest in the G0/G1 phase, apoptosis increase, and inhibition of mTOR signaling activation by decreasing the phosphorylation of Akt, mTOR and p70S6K, suggesting that proliferation inhibition of carnosine in human gastric carcinoma was through the inhibition of Akt/mTOR/p70S6K pathway, and carnosine would be a mimic of rapamycin.

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Surgical sutures play an important role across a wide range of medical treatments and a wide variety exist, differing in strength, size, composition, and performance. Recently, increasing interest has been paid to bioactive and electronic sutures made of synthetic polymers, owing to their ability to reduce inflammation as well as medically and/or electronically facilitate wound healing. However, integrating sensing capabilities into bioactive sutures without adversely affecting their mechanical strength, biocompatibility, and/or bioactivity remains challenging. In this work, a set of biomimicking, antibacterial, and sensing sutures based on the regenerated silk fibroin is designed and fabricated. These sensing sutures, inspired by the “core–shell” multilayered structure of natural spider‐silk fibers, are hierarchically structured and heterogeneously functionalized to allow for the integration of multiple, clinically favorable functions into one suture device. These functions included: reducing inflammation and bacterial infection in wound sites, measuring tension of both the tissue and suture, and aiding tissue healing via multi‐modal controlled drug and growth factor release. Critically, these functions are coupled with real‐time optical and electronic monitoring capabilities. This approach provides greater insight into multifunctional sutures with inherent sensing capabilities and offers enormous potential in both therapeutic and diagnostic applications.

Background: Preoperative identification of hepatocellular carcinoma (HCC), combined hepatocellular-cholangiocarcinoma (cHCC-ICC), and intrahepatic cholangiocarcinoma (ICC) is essential for treatment decision making. We aimed to use ultrasound-based radiomics analysis to non-invasively distinguish histopathological subtypes of primary liver cancer (PLC) before surgery. Methods: We retrospectively analyzed ultrasound images of 668 PLC patients, comprising 531 HCC patients, 48 cHCC-ICC patients, and 89 ICC patients. The boundary of a tumor was manually determined on the largest imaging slice of the ultrasound medicine image by ITK-SNAP software (version 3.8.0), and then, the highthroughput radiomics features were extracted from the obtained region of interest (ROI) of the tumor. The combination of different dimension-reduction technologies and machine learning approaches was used to identify important features and develop the moderate radiomics model. The comprehensive ability of the radiomics model can be evaluated by the area under the receiver operating characteristic curve (AUC). Results: After digitally processing tumor ultrasound images, 5,234 high-throughput radiomics features were obtained. We used the Spearman + least absolute shrinkage and selection operator (LASSO) regression method for feature selection and logistics regression for modeling to develop the HCC-vs-non-HCC radiomics model (composed of 16 features). The Spearman + statistical test + random forest methods were used for feature selection, and logistics regression was applied for modeling to develop the ICCvs-cHCC-ICC radiomics model (composed of 19 features). The overall performance of the radiomics model in identifying different histopathological types of PLC was moderate, with AUC values of 0.854 (training cohort) and 0.775 (test cohort) in the