Objective To explore the effects of adriamycin-loaded immuno-nanoparticles on multidrug resistance (MDR) of hepatoma cell line SMMC-7721/ADM. Methods The cytotoxicity of the adriamycin-loaded immuno-nanoparticles on the bepatoma cell line SMMC-7721/ADM in vitro and the tumor cell-binding ability of adriamycin-loaded immuno-nanoparticles were detected. Results The effect of the cytotoxicity of adriamycin-loaded immuno-nanoparticles on the hepatoma cell line SMMC-7721/ADM was significantly better than that of adriamycin-loaded nanoparticles. Adriamycin-loaded immuno-nanoparticles had the specific binding ability with the hepatoma cell line SMMC-7721/ADM. Conclusions Adriamycin-loaded immuno-nanoparticles can overcome the MDR of the tumor in vitro. The mechanism may be that immuno-nanoparticles could adhere to the tumor cell membrane, and the release of the loaded adriamycin creates a high local concentration in the extracellular medium. The increased concentration gradient improves the diffusion of adriamycin from the extracellular medium to the intracellular medium.

To explore the feasibility of CT-based image radiomics signature in identification of primary gastric lymphoma and Borrmann type IV gastric cancer.
 Methods: A retrospective analysis of 71 patients with primary gastric lymphoma or Borrmann type IV gastric cancer confirmed by pathology in our Hospital from January 2009 to April 2017 was performed. There were 28 patients with primary gastric lymphoma and 43 patients with Borrmann type IV gastric cancer. The feature extraction algorithm based on Matlab 2017a software was used to extract the features of image, and the logistic regression model was used to screen the features to establish radiomics signature. The CT sign diagnosis model was established, which included the periplasmic fat infiltration, softness of the stomach wall, abdominal lymph node and peripheral organ metastasis, ascites, mucosal white line sign and lesion thickness. The classification of the two models was evaluated by receiver operating characteristic curve.
 Results: A total of 32 3D features were extracted from CT image for each patients. Two features were found to be the most important differential diagnosis factors, and the radiomics signature was established. The CT sign diagnosis model consisted of ascites, periplasmic fat infiltration, stomach wall softness and mucosal white line. For the radiomics signature and the CT subjective finding model, the AUCs were 0.964 and 0.867 with the accuracy at 94.4% and 80.2%, the sensitivity at 93.0% and 74.4%, the specificity at 96.4% and 89.3%, respectively. After Delong test, the diagnostic efficacy of the radiomics signature was higher than the CT sign diagnosis model (P<0.001).
 Conclusion: CT-based image radiomics signature can accurately identify primary gastric lymphoma and Borrmann type IV gastric cancer, and can potentially provide important assistance in clinical diagnosis for the two diseases.
Humans ; Lymphoma, Non-Hodgkin ; Neoplasm Staging ; Retrospective Studies ; Stomach Neoplasms ; Tomography, X-Ray Computed