The therapeutic effect is not ideal for patients with colorectal cancer that has already metastasized.In recent years,it has been found that extracellular vesicles play an important role in various aspects of cancer cells,and their impact on the invasion and metastasis process of colorectal cancer has gradually been revealed.This review reviews and analyzes the role of extracellular vesicles in the invasion and metastasis of colorectal cancer,and briefly introduces the role of some extracellular vesicles in the treatment of colorectal cancer.

Objective:To investigate the benefits of artificial intelligence (AI)-based image optimization technique on image quality of coronary CT angiography (CCTA).Methods:Sixty patients, who were referred for CCTA, were prospectively enrolled between May and June 2018 in Peking Union Medical College Hospital and were randomly divided into two groups. Group A was scanned with a low tube voltage of 80 kVp and a reduced contrast media volume of lopamiro at 0.7 ml /kg and group B was scanned with a standard 120 kVp tube voltage and an injection of 70 ml lopamiro. According to the different reconstruction methods, group A was divided into two subgroups. The images of group A1 were reconstructed with iterative reconstruction (IR). IR and further AI-based image optimization were used in group A2. Group B was also reconstructed by IR. To evaluate image quality objectively, the mean attenuation of contrast-enhancement values, background noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured and calculated in the region of interests (ROIs) of the aortic root (Ao), left main coronary artery (LM), left anterior descending branch (LAD), left circumflex branch (LCX) and right coronary artery (RCA), respectively. In addition, the subjective evaluation was performed by two radiologists using Likert 4 scale (1 for excellent and 4 for poor) to evaluate the image quality of coronary artery branches and segments. The estimated radiation dose in terms of volume CT dose index (CTDI vol), dose length product (DLP) and effective dose (ED) was recorded and compared between group A and group B. Analyses of the differences between groups were compared with image quality, radiation dose by t test or Wilcoxon signed ranks test, and subjective assessments were compares with χ 2 test. Results:In terms of lumen enhancement, compared to group A2, there was no significant difference in CT value of each ROI ( P>0.05); CT value of group A1 and group A2 at Ao was significantly higher than that of group B ( P<0.01), but there was no significant difference in other ROI ( P>0.05). By comparing noise, SNR and CNR, it could be seen that compared to group B, A2 group optimized by AI had a significantly lower noise level at Ao than group B ( P<0.001), and there was no statistical difference in ROI for the rest (all P>0.05).SNR at Ao was significantly higher than that of group B ( P<0.001), and there was no statistical difference in ROI for the rest ( P>0.05).However, the CNR of group A2 was significantly higher than that of group B in all ROI ( P<0.001). Compared to the AI-optimized A2 group, the noise of A2 group was significantly lower than that of A1 group at all ROI, and SNR and CNR were significantly higher than that of A1 group ( P<0.001). The subjective evaluation results of coronary segments showed that image quality of group A2 and group B was significantly better than that of group A1 ( P=0.002,0.038). There was no significant difference between group A2 and group B ( P=0.543). The radiation dose indexes of CTDI vol, DLP and ED in group A were significantly lower than those in group B (all P<0.001). The ED was decreased by 70.4%. Meanwhile, the volume of contrast media in group A was reduced by 37.1% than that that in group B. Conclusion:Compared to conventional scanning, CCTA images optimized by AI technology improved subjective and objective image quality.

Objective:To establish and internally validate a nomogram model for predicting complicated acute appendicitis (CA).Methods:The clinical data from 663 acute appendicitis patients from the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine from October 2015 to October 2022 were retrospectively analyzed. There were 411 males and 252 females, aged ( M (IQR)) 41 (22) years (range: 18 to 84 years). There were 516 cases of CA and 147 cases of uncomplicated acute appendicitis. The minimum absolute contraction and selection operator regression model was used to screen the potential relative factors of CA, and the screened factors were included in the Logistic regression model for multivariate analysis. Software R was used to establish a preoperative CA nomogram prediction model, the receiver operating characteristic curve of the model was drawn, and the value of area under the curve (AUC) was compared to evaluate its identification ability, and the Bootstrap method was used for internal verification. Results:The elderly (age≥60 years) ( OR=2.428, 95% CI: 1.295 to 4.549), abdominal pain time (every rise of 1 hour) ( OR=1.089, 95% CI: 1.072 to 1.107), high fever (body temperature≥39 ℃) ( OR=1.122, 95% CI: 1.078 to 1.168), total bilirubin (every rise of 1 μmol/L) ( OR=2.629, 95% CI: 1.227 to 5.635) were independent relative factors of CA (all P<0.05). The AUC of this model was 0.935 (95% CI: 0.915 to 0.956). After internal verification using the Bootstrap method, the model still had a high discrimination ability (AUC=0.933), and the predicted CA curve was still in good agreement with the actual clinical CA curve. Conclusion:The clinical prediction model based on the elderly (age≥60 years), prolonged abdominal pain time, high fever (body temperature≥39 ℃), and increased total bilirubin can help clinicians effectively identify CA.

Objective:To establish and internally validate a nomogram model for predicting complicated acute appendicitis (CA).Methods:The clinical data from 663 acute appendicitis patients from the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine from October 2015 to October 2022 were retrospectively analyzed. There were 411 males and 252 females, aged ( M (IQR)) 41 (22) years (range: 18 to 84 years). There were 516 cases of CA and 147 cases of uncomplicated acute appendicitis. The minimum absolute contraction and selection operator regression model was used to screen the potential relative factors of CA, and the screened factors were included in the Logistic regression model for multivariate analysis. Software R was used to establish a preoperative CA nomogram prediction model, the receiver operating characteristic curve of the model was drawn, and the value of area under the curve (AUC) was compared to evaluate its identification ability, and the Bootstrap method was used for internal verification. Results:The elderly (age≥60 years) ( OR=2.428, 95% CI: 1.295 to 4.549), abdominal pain time (every rise of 1 hour) ( OR=1.089, 95% CI: 1.072 to 1.107), high fever (body temperature≥39 ℃) ( OR=1.122, 95% CI: 1.078 to 1.168), total bilirubin (every rise of 1 μmol/L) ( OR=2.629, 95% CI: 1.227 to 5.635) were independent relative factors of CA (all P<0.05). The AUC of this model was 0.935 (95% CI: 0.915 to 0.956). After internal verification using the Bootstrap method, the model still had a high discrimination ability (AUC=0.933), and the predicted CA curve was still in good agreement with the actual clinical CA curve. Conclusion:The clinical prediction model based on the elderly (age≥60 years), prolonged abdominal pain time, high fever (body temperature≥39 ℃), and increased total bilirubin can help clinicians effectively identify CA.