Objective To study the effect of PPARγ ligand troglitazone (TGZ) on invasion and metastasis of gastric cancer cells, and investigate the relationship of PPARγ ligand with gastric cancer.Methods The expression of PPARγ in gastric cancer cell line MGC803 were detected by immunofluorescence cytochemistry method. The effect of different density TGZ on proliferation activity and adhesion of gastric cancer cell were detected by MTT chromatometry. The effect of different ligands on invasion and metastasis of gastric cancer cell MGC803 were detected by invasion system in vitro. Results The expression of PPARγ mainly located in cell nucleus. TGZ inhibited the proliferation of gastric cancer cell, decreased cell adhesion, locomotory capacity and invasion to matrigel, which had time and dose-dependent relationship.When treatment with 0. 1,1.0 and 10μ mol/ L TGZ, inhibition ratio of invasion and metastasis of cell was 8.79% ,31.31% ,51.42% and 28.29% ,4. 27% ,59. 27% respectively, which had statistical significance compared with control group( P <0. 05). When treatment was 10μ mol/L TGZ, cell adhesion was 0. 32 ±0. 03, it was statistically significant higher than that in control group (0. 52 ± 0. 04, P < 0. 05 ). Conclusion Human gastric cancer cell line MGC803 expressed functional PPARγ protein. TGZ inhibited adhesion and invasion of MGC803 cell on ECM at different degree, the effect of combination of two ligands was evident, which mechanism of action needed to be further investigated.

Objective To propose a classification method for ultrasound images of pedicle screw canals based on radiomics analysis,and to evaluate the integrity of the screw canal.Methods With thoracolumbar spine specimens from 4 fresh cadavers,50 pedicle screw canals were pre-established and ultrasound images of the canals were acquired.A total of 2 000 images(1 000 intact and 1 000 damaged canal samples)were selected.The dataset was randomly divided in a 4∶1 ratio using 5-fold cross-validation to form training and testing sets(consisting of 1 600 and 400 samples,respectively).Firstly,the optimal radius of the region of interest was identified using the Otsu's thresholding method,followed by feature extraction using pyradiomics.Principal component analysis and the least absolute shrinkage and selection operator algorithm were employed for dimensionality reduction and feature selection,respectively.Subsequently,3 machine learning models(support vector machine[SVM],logistic regression,and random forest)and 3 deep learning models(visual geometry group[VGG],ResNet,and Transformer)were used to classify the ultrasound images.The performance of each model was evaluated using accuracy.Results With a region of interest radius of 230 pixels,the SVM model achieved the highest classification accuracy of 96.25%.The accuracy of the VGG model was only 51.29%,while the accuracies of the logistic regression,random forest,ResNet,and Transformer models were 85.50%,80.75%,80.17%,and 75.18%,respectively.Conclusion For ultrasound images of pedicle screw canals,the machine learning model performs better than the deep learning model as a whole,and the SVM model has the best classification performance,which can be used to assist physicians in diagnosis.