Epigallocatechin gallate (EGCG) inhibits lipopolysaccharide-induced inflammation in RAW 264.7 macrophage cells via modulating nuclear factor kappa-light-chain enhancer of activated B cells (NF-κB) signaling pathway.

Epigallocatechin-3-gallate (EGCG) is a major bioactive compound in tea polyphenol extract. After ingestion, EGCG reaches the intestine and may commence anti-inflammation in the intestinal organ. Thus, in this paper, the anti-inflammatory effect of EGCG was studied using lipopolysaccharide (LPS)-induced inflammation in RAW 264.7 cells. LPS induction instigated morphological deformation extensively which was normalized by EGCG. In LPS-induced macrophage cells, EGCG was found to lower cellular nitric oxide (32% of LPS group) and intercellular ROS level (45.4% of LPS group). It also suppressed the expression of IL-1ß (LPS 132.6 ± 14.6, EGCG 10.67 ± 3.65), IL-6 (LPS 2994.44 ± 178.5, EGCG 408.33 ± 52.34), TNF-α (LPS 27.11 ± 2.84, EGCG 1.22 ± 0.03), and iNOS (LPS 40.45 ± 11.17, EGCG 10.24 ± 0.89). The GO function analysis identified that these differential genes involved 24 biological processes, 18 molecular functions, and 19 cellular component-related processes. KEGG pathway enrichment analysis revealed that LPS significantly affects NF-κB, TNF, and TLR signaling pathways. Western blotting revealed that EGCG diminished P-IκB/IκB ratio by 75% and p-p65/p65 by 50% compared to the LPS group. Finally, Arg-1 and CD-206 mRNA expression were determined by RT-PCR, which was consistent with the RNA-Seq result. These findings indicate that EGCG exerts an anti-inflammatory effect by reducing NO and ROS production, suppressing TLR4 protein expression, and inhibiting IκB and p65 phosphorylation.

Machine learning based on automated breast volume scanner radiomics for differential diagnosis of benign and malignant BI-RADS 4 lesions / 中华超声影像学杂志

Objective:To evaluate the performance of machine learning (ML) based on automated breast volume scanner (ABVS) radiomics in distinguishing benign and malignant BI-RADS 4 lesions.Methods:Between May to December 2020, patients with BI-RADS 4 lesions from the Affiliated Hospital of Southwest Medical University (Center 1) and Guangdong Provincial Hospital of Traditional Chinese Medicine (Center 2) were prospectively collected and divided into training cohort (Center 1) and external validation cohort (Center 2). The radiomics features of BI-RADS 4 lesions were extracted from the axial, sagittal and coronal ABVS images by MaZda software. In the training cohort, 7 feature selection methods and thirteen ML algorithms were combined in pairs to construct different ML models, and the 6 models with the best performance were verified in the external validation cohort to determine the final ML model. Finally, the diagnostic performance and confidence (5-point scale) of radiologists (R1, R2 and R3, with 3, 6 and 10 years of experience, respectively) with or without the model were evaluated.Results:①A total of 251 BI-RADS 4 lesions were enrolled, including 178 lesions (91 benign, 87 malignant) in the training cohort and 73 lesions (44 benign, 29 malignant) in the external validation cases. ②In the training cohort, the 6 ML models (DNN-RFE, AdaBoost-RFE, LR-RFE, LDA-RFE, Bagging-RFE and SVM-RFE) had the best diagnostic performance, and their AUCs were 0.972, 0.969, 0.968, 0.968, 0.967 and 0.962, respectively. ③In the external validation cohort, the DNN-RFE still had the best performance with the AUC, accuracy, sensitivity, specificity, PPV and NPV were 0.886, 0.836, 0.934, 0.776, 86.8% and 82.5%, respectively. ④Before model assistance, R1 had the worst diagnostic performance with the accuracy, sensitivity, specificity, PPV and NPV were 0.680, 0.750, 0.640, 57% and 81%, respectively. After model assistance, the diagnostic performance of R1 was significantly improved ( P=0.039), and its diagnostic sensitivity, specificity, accuracy, PPV and NPV improved to 0.730, 0.810, 0.930, 68% and 94%; while the improvement of R2 and R3 were not significantly ( P=0.811, 0.752). Meanwhile, the overall diagnostic confidence of the 3 radiologists increased from 2.8 to 3.3 ( P<0.001). Conclusions:The performance of ML based on ABVS radiomics in distinguishing between benign and malignant BI-RADS 4 lesions is good, which may improve the diagnostic performance of inexperienced radiologists and enhance diagnostic confidence.

Surgical indication,diagnosis and treatment of suspected pancreatic cystic lesions / 局解手术学杂志

Objective To explore the surgical indication,diagnosis and treatment of suspected pancreatic cystic lesions.Methods To-tally 341 patients were admitted into our hospital because of pancreatic cystic lesions from October 2010 to October 2015.Except the 278 confirmed cases,the clinical data of the rest 63 patients with vague diagnosis were retrospectively analyzed.Results The surgical indication of pancreatic cystic lesions were:the lesion diameter was more than 4 cm;the imaging diagnosis was malignant;the lesion was combined with obvious clinical symptoms which can not explain with other diseases;there were asymptomatic pancreatic cystic lesion and main pancre-atic duct dilation without surgical risk factors;the follow-up showed that the desease is in development.Endoscopic ultrasonography guided fine needle aspiration is a useful supplement for more accurate diagnosis.However,it is an uncertain diagnostic value currently.Conclusion Diameter of lesion,uncertain imaging diagnosis,severe abdominal symptoms,obstructive jaundice and abnormal serum tumor markers are the surgical indication for patients.