[Applications of Artificial Intelligence in Pancreatic Cystic Lesion Imaging].

As the detection rate of pancreatic cystic lesions(PCL)increases,artificial intelligence(AI)has made breakthroughs in the imaging workflow of PCL,including image post-processing,lesion detection,segmentation,diagnosis and differential diagnosis.AI-based image post-processing can optimize the quality of medical images and AI-assisted models for lesion detection,segmentation,diagnosis and differential diagnosis significantly enhance the work efficiency of radiologists.This article reviews the application progress of AI in PCL imaging and provides prospects for future research directions.

Should All Pancreatic Cystic Lesions with Worrisome or High-Risk Features Be Resected? A Clinical and Radiological Machine Learning Model May Help to Answer.

RATIONALE AND OBJECTIVES: According to current guidelines, pancreatic cystic lesions (PCLs) with worrisome or high-risk features may have overtreatment. The purpose of this study was to build a clinical and radiological based machine-learning (ML) model to identify malignant PCLs for surgery among preoperative PCLs with worrisome or high-risk features. MATERIALS AND METHODS: Clinical and radiological details of 317 pathologically confirmed PCLs with worrisome or high-risk features were retrospectively analyzed and applied to ML models including Support Vector Machine, Logistic Regression (LR), Decision Tree, Bernoulli NB, Gaussian NB, K Nearest Neighbors and Linear Discriminant Analysis. The diagnostic ability for malignancy of the optimal model with the highest diagnostic AUC in the cross-validation procedure was further evaluated in internal (n = 77) and external (n = 50) testing cohorts, and was compared to two published guidelines in internal mucinous cyst cohort. RESULTS: Ten clinical and radiological feature-based LR model was the optimal model with the highest AUC (0.951) in the cross-validation procedure. In the internal testing cohort, LR model reached an AUC, accuracy, sensitivity, and specificity of 0.927, 0.909, 0.914, and 0.905; in the external testing cohort, LR model reached 0.948, 0.900, 0.963, and 0.826. When compared to the European guidelines and the ACG guidelines, LR model demonstrated significantly better accuracy and specificity in identifying malignancy, while maintaining the same high sensitivity. CONCLUSION: Clinical- and radiological-based LR model can accurately identify malignant PCLs in patients with worrisome or high-risk features, possessing diagnostic performance better than the European guidelines as well as ACG guidelines.

USP51 promotes non-small cell lung carcinoma cell stemness by deubiquitinating TWIST1.

BACKGROUND: USP51 is a deubiquitinase (DUB), that is involved in diverse cellular processes. Accumulating evidence has demonstrated that USP51 contributes to cancer development. However, its impact on non-small cell lung carcinoma (NSCLC) cell malignancy is largely unknown. METHODS: In this study, we performed bioinformatics analysis on a dataset from The Cancer Genome Atlas to determine the association between USP51 and cell stemness marker expression in NSCLC patients. RT‒qPCR, Western blotting, and flow cytometry were performed to examine the effects of USP51 depletion on stemness marker expression. Colony formation and tumor sphere formation assays were used to assess the stemness of NSCLC cells. A cycloheximide chase time-course assay and a polyubiquitination assay were carried out to analyze the effects of USP51 on the TWIST1 protein level. TWIST1 was overexpressed in USP51 knockdown NSCLC cells to determine whether TWIST1 is required. The effect of USP51 on the in vivo growth of NSCLC cells was tested through subcutaneous injections in mice. RESULTS: We found that USP51 deubiquitinates TWIST1, which is significantly upregulated in the tissues of patients with NSCLC and is closely associated with poor prognosis. USP51 expression was positively correlated with the expression of stemness marker CD44, SOX2, NANOG, and OCT4 in NSCLC patients. USP51 depletion attenuated mRNA, protein, and cell surface expression of stemness markers and the stemness of NSCLC cells. Ectopic USP51 expression potentiated the stability of the TWIST1 protein by attenuating its polyubiquitination. In addition, TWIST1 re-expression in NSCLC cells reversed the inhibitory effect of USP51 knockdown on cell stemness. Furthermore, the in vivo results confirmed the suppressive effect of USP51 depletion on NSCLC cell growth. CONCLUSIONS: Our results show that USP51 maintains the stemness of NSCLC cells by deubiquitinating TWIST1. Knocking it down reduces both cell stemness and growth of NSCLC cells.

A systematic review of prognosis predictive role of radiomics in pancreatic cancer: heterogeneity markers or statistical tricks?

OBJECTIVES: We aimed to systematically evaluate the prognostic prediction accuracy of radiomics features extracted from pre-treatment imaging in patients with pancreatic ductal adenocarcinoma (PDAC). METHODS: Radiomics literature on overall survival (OS) prediction of PDAC were all included in this systematic review. A further meta-analysis was performed on the effect size of first-order entropy. Methodological quality and risk of bias of the included studies were assessed by the radiomics quality score (RQS) and prediction model risk of bias assessment tool (PROBAST). RESULTS: Twenty-three studies were finally identified in this review. Two (8.7%) studies compared prognosis prediction ability between radiomics model and TNM staging model by C-index, and both showed a better performance of the radiomics. Twenty-one (91.3%) studies reported significant predictive values of radiomics features. Nine (39.1%) studies were included in the meta-analysis, and it showed a significant correlation between first-order entropy and OS (HR 1.66, 95%CI 1.18-2.34). RQS assessment revealed validation was only performed in 5 (21.7%) studies on internal datasets and 2 (8.7%) studies on external datasets. PROBAST showed that 22 (95.7%) studies have a high risk of bias in participants because of the retrospective study design. CONCLUSION: First-order entropy was significantly associated with OS and might improve the accuracy of PDAC prognosis prediction. Existing studies were poorly validated, and it should be noted in future studies. Modification of PROBAST for radiomics studies is necessary since the strict requirements of prospective study design may not be applicable to the demand for a large sample size in the model construction stage. KEY POINTS: • Radiomics based on the primary lesion holds great potential for prognosis prediction. First-order entropy was significantly associated with the overall survival of PDAC and might improve the accuracy of current PDAC prognosis prediction. • We strongly recommend that at least an internal validation should be conducted in any radiomics study. Attention should be paid to the complex relationships between radiomics features. • Due to the close relationship between radiomics and big data, the strict requirement of prospective study design in PROABST may not be appropriate for radiomics studies. A balance between study types and sample sizes for radiomics studies needs to be found in the model construction stage.

CTRP1 decreases ABCA1 expression and promotes lipid accumulation through the miR-424-5p/FoxO1 pathway in THP-1 macrophage-derived foam cells.

Prevention of ATP binding cassette transporter A1 (ABCA1)-dependent cholesterol efflux leads to lipid accumulation in macrophages and atherosclerosis development. C1q tumor necrosis factor-related protein 1 (CTRP1), a conserved paralog of adiponectin, has been shown to aggravate atherosclerosis via its proinflammatory property. However, very little is known about its effects on ABCA1 expression and macrophage lipid accumulation. In the current studies, we found that CTRP1 downregulated ABCA1 expression, inhibited cholesterol efflux to apoA-I and promoted lipid accumulation in THP-1 macrophage-derived foam cells. Forkhead box O1 (FoxO1), a transcriptional repressor of ABCA1, was identified as a direct target of miR-424-5p. Mechanistically, CTRP1 attenuated miR-424-5p levels and then augmented FoxO1 expression in the nucleus, which led to downregulation of ABCA1 expression and inhibition of cholesterol efflux. In conclusion, these findings suggest that CTRP1 restrains cholesterol efflux and facilitates macrophage lipid accumulation through the miR-424-5p/FoxO1/ABCA1 signaling pathway, thereby providing a novel mechanistical insight into its proatherosclerotic action.

CTRP12 ameliorates atherosclerosis by promoting cholesterol efflux and inhibiting inflammatory response via the miR-155-5p/LXRα pathway.

C1q tumor necrosis factor-related protein 12 (CTRP12), a conserved paralog of adiponectin, is closely associated with cardiovascular disease. However, little is known about its role in atherogenesis. The aim of this study was to examine the influence of CTRP12 on atherosclerosis and explore the underlying mechanisms. Our results showed that lentivirus-mediated CTRP12 overexpression inhibited lipid accumulation and inflammatory response in lipid-laden macrophages. Mechanistically, CTRP12 decreased miR-155-5p levels and then increased its target gene liver X receptor α (LXRα) expression, which increased ATP binding cassette transporter A1 (ABCA1)- and ABCG1-dependent cholesterol efflux and promoted macrophage polarization to the M2 phenotype. Injection of lentiviral vector expressing CTRP12 decreased atherosclerotic lesion area, elevated plasma high-density lipoprotein cholesterol levels, promoted reverse cholesterol transport (RCT), and alleviated inflammatory response in apolipoprotein E-deficient (apoE-/-) mice fed a Western diet. Similar to the findings of in vitro experiments, CTRP12 overexpression diminished miR-155-5p levels but increased LXRα, ABCA1, and ABCG1 expression in the aortas of apoE-/- mice. Taken together, these results suggest that CTRP12 protects against atherosclerosis by enhancing RCT efficiency and mitigating vascular inflammation via the miR-155-5p/LXRα pathway. Stimulating CTRP12 production could be a novel approach for reducing atherosclerosis.

The effectiveness of the combined problem-based learning (PBL) and case-based learning (CBL) teaching method in the clinical practical teaching of thyroid disease.

BACKGROUND: This study aimed to evaluate the effectiveness and efficiency of PBL-CBL combined teaching in thyroid surgery and make observations from the students' perspectives, based on their satisfaction with the learning process. METHODS: We prospectively enrolled 354 fourth-year students majoring in clinical medicine, along with 232 residents, from September 2014 to June 2019. These participants were randomly allocated into either the combined PBL-CBL teaching group or the traditional lecture-based classroom group to attend a course about thyroid nodules. Both pre- and post-class quizzes were conducted. An anonymous questionnaire was also administered to both groups to evaluate the students' perceptions and experiences. We compared the two teaching methods among all the students as well as with the fourth-year students and residents in subgroups. RESULTS: The traditional group's pre-class quiz scores were significantly higher than the PBL-CBL group's (as determined by a two-tailed t-test at a 95% confidence interval, T = 16.483, P < 0.001). After class, in the PBL-CBL group, the mean total quiz score and the basic knowledge and case analysis scores increased significantly (P < 0.001). The PBL-CBL group's performance improvement was significantly higher than the traditional group's (increasing from 52.76 to 70.51 vs. from 67.03 to 71.97). Furthermore, the scores for learning motivation, understanding, student-teacher interaction, the final examination, communication skills, clinical thinking skills, self-learning skills, teamwork skills, and knowledge absorption, as measured by the survey, were significantly higher in the PBL-CBL group than in the traditional group (P < 0.001). Meanwhile, the survey scores representing the amount of students' free time the course consumed were significantly lower in the PBL-CBL group than in the traditional group (P < 0.001). CONCLUSIONS: PBL combined with CBL may be an effective method for improving medical students' and residents' performance and enhancing their clinical skills.

Radiosensitizing effects of Sestrin2 in PC3 prostate cancer cells.

OBJECTIVES: The stress-responsive genes of Sestrin family are recognized as new tumor suppressor genes in breast carcinoma, however, the function of Sestrin family in human prostate cancer is not clear. Ionizing radiation (IR) is known to induce Sestrin gene expression in breast cancer cells. However, the response of Sestrin to IR has not been reported in PC3 prostate cancer cells. MATERIALS AND METHODS: Sestrin2 expression in prostate cancer cell lines (PC3, LNCaP clone FGC, and DU145) was detected by Western blot and real-time PCR. Cell counting kit (CCK-8) was used to detect cellular proliferation. The radiosensitivity of PC3 cells was detected by clonogenic assay. RESULTS: Sestrin2 expression in prostate cancer cell lines (PC3, LNCaP clone FGC, and DU145) is low. In vitro assays indicated that over-expressing Sestrin2 in human prostate cancer PC3 inhibited tumor proliferation. In addition, elevated Sestrin2 expression sensitized PC3 cells to IR. CONCLUSION: We determined Sestrin2 may function as a tumor suppressor through repressing proliferation, mediating sensitization to IR in PC3 cells.