Exploring value of CT coronary imaging combined with machine-learning methods to predict myocardial ischemia.

PURPOSE: To establish a machine-learning (ML) model based on coronary computed tomography angiography (CTA) images for evaluating myocardial ischemia in patients diagnosed with coronary atherosclerosis. METHODS: This retrospective analysis includes CTA images acquired from 110 patients. Among them, 58 have myocardial ischemia and 52 have normal myocardial blood supply. The patients are divided into training and test datasets with a ratio 7 : 3. Deep learning model-based CQK software is used to automatically segment myocardium on CTA images and extract texture features. Then, seven ML models are constructed to classify between myocardial ischemia and normal myocardial blood supply cases. Predictive performance and stability of the classifiers are determined by receiver operating characteristic curve with cross validation. The optimal ML model is then validated using an independent test dataset. RESULTS: Accuracy and areas under ROC curves (AUC) obtained from the support vector machine with extreme gradient boosting linear method are 0.821 and 0.777, respectively, while accuracy and AUC achieved by the neural network (NN) method are 0.818 and 0.757, respectively. The naive Bayes model yields the highest sensitivity (0.942), and the random forest model yields the highest specificity (0.85). The k-nearest neighbors model yields the lowest accuracy (0.74). Additionally, NN model demonstrates the lowest relative standard deviations (0.16 for accuracy and 0.08 for AUC) indicating the high stability of this model, and its AUC applying to the independent test dataset is 0.72. CONCLUSION: The NN model demonstrates the best performance in predicting myocardial ischemia using radiomics features computed from CTA images, which suggests that this ML model has promising potential in guiding clinical decision-making.

Enhanced computed tomography radiomics-based machine-learning methods for predicting the Fuhrman grades of renal clear cell carcinoma.

OBJECTIVE: To develop and test an optimal machine learning model based on the enhanced computed tomography (CT) to preoperatively predict pathological grade of clear cell renal cell carcinoma (ccRCC). METHODS: A retrospective analysis of 53 pathologically confirmed cases of ccRCC was performed and 25 consecutive ccRCC cases were selected as a prospective testing set. All patients underwent routine preoperative abdominal CT plain and enhanced scans. Renal tumor lesions were segmented on arterial phase images and 396 radiomics features were extracted. In the training set, seven discrimination classifiers for high- and low-grade ccRCCs were constructed based on seven different machine learning models, respectively, and their performance and stability for predicting ccRCC grades were evaluated through receiver operating characteristic (ROC) analysis and cross-validation. Prediction accuracy and area under ROC curve were used as evaluation indices. Finally, the diagnostic efficacy of the optimal model was verified in the testing set. RESULTS: The accuracies and AUC values achieved by support vector machine with radial basis function kernel (svmRadial), random forest and naïve Bayesian models were 0.860±0.158 and 0.919±0.118, 0.840±0.160 and 0.915±0.138, 0.839±0.147 and 0.921±0.133, respectively, which showed high predictive performance, whereas K-nearest neighborhood model yielded lower accuracy of 0.720±0.188 and lower AUC value of 0.810±0.150. Additionally, svmRadial had smallest relative standard deviation (RSD, 0.13 for AUC, 0.17 for accuracy), which indicates higher stability. CONCLUSION: svmRadial performs best in predicting pathological grades of ccRCC using radiomics features computed from the preoperative CT images, and thus may have high clinical potential in guiding preoperative decision.

Development and External Validation of Radiomics Approach for Nuclear Grading in Clear Cell Renal Cell Carcinoma.

BACKGROUND AND PURPOSE: Nuclear grades of clear cell renal cell carcinoma (ccRCC) are usually confirmed by invasive methods. Radiomics is a quantitative tool that uses non-invasive medical imaging for tumor diagnosis and prognosis. In this study, a radiomics approach was proposed to analyze the association between preoperative computed tomography (CT) images and nuclear grades of ccRCC. METHODS: Our dataset included 320 ccRCC patients from two centers and was divided into a training set (n = 124), an internal test set (n = 123), and an external test set (n = 73). A radiomic feature set was extracted from unenhanced, corticomedullary phase, and nephrographic phase CT images. The maximizing independent classification information criteria function and recursive feature elimination with cross-validation were used to select effective features. Random forests were used to build a final model for predicting nuclear grades, and area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of radiomic features and models. RESULTS: The radiomic features from the three CT phases could effectively distinguished the four nuclear grades. A combined model, merging radiomic features and clinical characteristics, obtained good predictive performances in the internal test set (AUC 0.77, 0.75, 0.79, and 0.85 for the four grades, respectively), and performance was further confirmed in the external test set, with AUCs of 0.75, 0.68, and 0.73 (no fourth-level data). CONCLUSION: The combination of CT radiomic features and clinical characteristics could discriminate the nuclear grades in ccRCC, which may help in assisting treatment decision making.

Predictive value of bilirubin and serum γ-glutamyltranspeptidase levels in type-2 diabetes mellitus patients with acute coronary syndrome.

BACKGROUND: Cardiovascular disease is a major complication of diabetes mellitus (DM). Type-2 DM (T2DM) is associated with an increased risk of cardiovascular events and mortality, while serum biomarkers may facilitate the prediction of these outcomes. Early differential diagnosis of T2DM complicated with acute coronary syndrome (ACS) plays an important role in controlling disease progression and improving safety. AIM: To investigate the correlation of serum bilirubin and γ-glutamyltranspeptidase (γ-GGT) with major adverse cardiovascular events (MACEs) in T2DM patients with ACS. METHODS: The clinical data of inpatients from January 2022 to December 2022 were analyzed retrospectively. According to different conditions, they were divided into the T2DM complicated with ACS group (T2DM + ACS, n = 96), simple T2DM group (T2DM, n = 85), and simple ACS group (ACS, n = 90). The clinical data and laboratory indices were compared among the three groups, and the correlations of serum total bilirubin (TBIL) levels and serum γ-GGT levels with other indices were discussed. T2DM + ACS patients received a 90-day follow-up after discharge and were divided into event (n = 15) and nonevent (n = 81) groups according to the occurrence of MACEs; Univariate and multivariate analyses were further used to screen the independent influencing factors of MACEs in patients. RESULTS: The T2DM + ACS group showed higher γ-GGT, total cholesterol, low-density lipoprotein cholesterol (LDL-C) and glycosylated hemoglobin (HbA1c) and lower TBIL and high-density lipoprotein cholesterol levels than the T2DM and ACS groups (P < 0.05). Based on univariate analysis, the event and nonevent groups were significantly different in age (t = 3.3612, P = 0.0011), TBIL level (t = 3.0742, P = 0.0028), γ-GGT level (t = 2.6887, P = 0.0085), LDL-C level (t = 2.0816, P = 0.0401), HbA1c level (t = 2.7862, P = 0.0065) and left ventricular ejection fraction (LEVF) levels (t=3.2047, P = 0.0018). Multivariate logistic regression analysis further identified that TBIL level and LEVF level were protective factor for MACEs, and age and γ-GGT level were risk factors (P < 0.05). CONCLUSION: Serum TBIL levels are decreased and γ-GGT levels are increased in T2DM + ACS patients, and the two indices are significantly negatively correlated. TBIL and γ-GGT are independent influencing factors for MACEs in such patients.

Prognostic Significance of Myocardial Blood Flow Quantification for Major Adverse Cardiac Events: A Systematic Review and Meta-analysis.

Chronic coronary syndromes involve reduced myocardial blood flow (MBF). MBF is a reliable predictor of outcomes, independent of the presence of significant stenosis. Whether MBF can predict major adverse cardiac events (MACE) during long-term follow-up is unknown. PubMed, Embase, Cochrane, CNKI, and WANFANG were searched for papers published up to January 2021. The exposure was the incremental unit of stress MBF (mL/g/min) or low MBF versus high MBF. The imaging examinations included positron emission tomography/computed tomography and coronary magnetic resonance. The study outcome was the occurrence of MACE during follow-up, summarized as time-to-event hazard ratios (HRs) and 95% confidence intervals (CIs). Six studies (300 MACEs in 2326 patients) were included. Four studies presented stress MBF data by unit increments. The pooled HR showed that an increase in stress MBF by 1 mL/g/min is a protective factor for MACE (HR = 0.32; 95% CI, 0.18-0.57; I2 = 62.9%, Pheterogeneity = 0.044). Two studies reported stress MBF as low/high. The results showed that a high-stress MBF was protective against MACEs (HR = 0.43; 95% CI, 0.24-0.78; I2 = 39.5%, Pheterogeneity = 0.199). Quantification of stress MBF using positron emission tomography/computed tomography and coronary magnetic resonance might have incremental predictive value for future MACEs in a population at intermediate to high cardiovascular risk. The results will require validation in large prospective randomized controlled trials.

Artificial Intelligence and Big Data Technologies in the Construction of Surgical Risk Prediction Model for Patients with Coronary Artery Bypass Grafting.

The objective of this work was to predict the risk of mortality rate in patients with coronary artery bypass grafting (CABG) based on the risk prediction model of CABG using artificial intelligence (AI) and big data technologies. The clinical data of 2,364 patients undergoing CABG in our hospital from January 2019 to August 2021 were collected in this work. Based on AI and big data technology, business requirement analysis, system requirement analysis, complication prediction module, big data mining technology, and model building are carried out, respectively; the successful CABG risk prediction system includes case feature analysis service, risk warning service, and case retrieval service. The commonly used precision, recall, and F1-score were adopted to evaluate the quality of the gradient-boosted tree (GBT) model. The analysis proved that the GBT model was the best in terms of precision, F1-score, and area under the receiver operating characteristic curve (ROC). According to the CABG risk prediction model, 1,382 patients had a score of <0, 463 patients had a score of 0 ≤ score ≤ 2, 252 patients had a score of 2 < score ≤ 5, and 267 patients had a score of >5, which were stratified into four groups: A, B, C, and D. The actual number of in-hospital deaths was 25, and the in-hospital mortality rate was 1.05%. The mortality rate predicted by the CABG risk prediction model was 2.67 ± 1.82% (95% confidential interval (CI) (2.87-2.98)), which was higher than the actual value. The CABG risk prediction model showed the credible results only in group B with AUC = 0.763 > 0.7. In group B, 3 patients actually died, the actual mortality rate was 0.33%, and the predicted mortality rate was 0.96 ± 0.78 (95% CI (0.82-0.87)), which overestimated the mortality rate of patients in group B. It successfully constructed a CABG risk prediction model based on the AI and big data technologies, which would overestimate the mortality of patients with intermediate risk, and it is suitable for different types of heart diseases through continuous research and development and innovation, and provides clinical guidance value.

Prediction of myocardial ischemia in coronary heart disease patients using a CCTA-Based radiomic nomogram.

Objective: The present study aimed to predict myocardial ischemia in coronary heart disease (CHD) patients based on the radiologic features of coronary computed tomography angiography (CCTA) combined with clinical factors. Methods: The imaging and clinical data of 110 patients who underwent CCTA scan before DSA or FFR examination in Changzhou Second People's Hospital, Nanjing Medical University (90 patients), and The First Affiliated Hospital of Soochow University (20 patients) from March 2018 to January 2022 were retrospectively analyzed. According to the digital subtraction angiography (DSA) and fractional flow reserve (FFR) results, all patients were assigned to myocardial ischemia (n = 58) and normal myocardial blood supply (n = 52) groups. All patients were further categorized into training (n = 64) and internal validation (n = 26) sets at a ratio of 7:3, and the patients from second site were used as external validation. Clinical indicators of patients were collected, the left ventricular myocardium were segmented from CCTA images using CQK software, and the radiomics features were extracted using pyradiomics software. Independent prediction models and combined prediction models were established. The predictive performance of the model was assessed by calibration curve analysis, receiver operating characteristic (ROC) curve and decision curve analysis. Results: The combined model consisted of one important clinical factor and eight selected radiomic features. The area under the ROC curve (AUC) of radiomic model was 0.826 in training set, and 0.744 in the internal validation set. For the combined model, the AUCs were 0.873, 0.810, 0.800 in the training, internal validation, and external validation sets, respectively. The calibration curves demonstrated that the probability of myocardial ischemia predicted by the combined model was in good agreement with the observed values in both training and validation sets. The decision curve was within the threshold range of 0.1-1, and the clinical value of nomogram was higher than that of clinical model. Conclusion: The radiomic characteristics of CCTA combined with clinical factors have a good clinical value in predicting myocardial ischemia in CHD patients.

High Inflammatory Factor Levels Increase Cardiovascular Complications in Diabetic Patients Undergoing Coronary Artery Bypass Grafting.

Objective: To investigate the association between inflammation and clinical outcomes of coronary artery bypass grafting (CABG) in diabetic patients. Methods: A total of 300 diabetic patients with coronary heart disease who underwent CABG were selected. Patients were divided into a group with cardiovascular events (32 in the MACCE group) and a group without cardiovascular events (268 in the non-MACCE group) according to whether cardiovascular events occurred within 30 days. The differences in clinical parameters; serum levels of TNF-α, IL-6, IL-18, IL-1ß, and CRP; factors associated with the occurrence of MACCE; and risk factors affecting the midterm all-cause mortality of patients were compared between the two groups. Results: The serum levels of TNF-α, IL-6, IL-18, and CRP in the MACCE group were significantly higher than those in the non-MACCE group (p < 0.05). Gender, smoking, hyperlipidemia, duration of diabetes, and levels of TNF-α, IL-6, IL-18, and CRP were closely related to the occurrence of MACCE. The Kaplan-Meier survival analysis evaluation results showed that the levels of IL-6 and CRP significantly affected the midterm all-cause mortality rate (p < 0.05). Multivariate Cox regression analysis showed that the advanced age, hypertension, hyperlipidemia, long duration of diabetes, elevated serum IL-6, and CRP levels could be used as risk factors for midterm all-cause mortality. Conclusions: Inflammation levels in diabetic patients are associated with complications and midterm all-cause mortality in patients undergoing coronary artery bypass grafting.

Delivery of miR-654-5p via SonoVue Microbubble Ultrasound Inhibits Proliferation, Migration, and Invasion of Vascular Smooth Muscle Cells and Arterial Thrombosis and Stenosis through Targeting TCF21.

Background: Abnormal proliferation of vascular smooth muscle cells (VSMCs) is an important cause of vascular stenosis. The study explored the mechanism of inhibition of vascular stenosis through the molecular mechanism of smooth muscle cell phenotype transformation. Methods: Coronary heart disease-related genes were screened by bioinformatics, and the target genes of miR-654-5p were predicted by dual-luciferase method and immunofluorescence method. miR-654-5p mimic stimulation and transfection of TCF21 and MTAP into cells. SonoVue microbubble sonication was used to deliver miR-654-5p into cells. Cell proliferation, migration, and invasion were detected by CCK-8, wound scratch, and Transwell. HE and IHC staining were performed to study the effect of miR-654-5p delivery via SonoVue microbubble ultrasound on vessel stenosis in a model of arterial injury. Gene expression was determined by qRT-PCR and WB. Results: TCF21 and MTAP were predicted as the target genes of miR-654-5p. Cytokines induced smooth muscle cell proliferation, migration, and invasion and promoted miR-654-5p downregulation; noticeably, downregulated miR-654-5p was positively associated with the cell proliferation and migration. Overexpression of TCF21 promoted proliferation, invasion, and migration, and mimic reversed such effects. miR-654-5p overexpression delivered by SonoVue microbubble ultrasound inhibited proliferation, migration, and invasion of cells. Moreover, in arterial injury model, we found that SonoVue microbubble ultrasound transmitted miR-654-5p into the arterial wall to inhibit arterial thrombosis and stenosis, while TCF21 was inhibited. Conclusion: Ultrasound delivery of miR-654-5p via SonoVue microbubbles was able to inhibit arterial thrombosis and stenosis by targeting TCF21.

Image quality and diagnostic accuracy of different dosages of iodixanol in computed tomography angiography and perfusion of overweight patients with coronary artery stenosis: A feasibility study.

OBJECTIVES: Iodixanol contrast media with different doses using computed tomography angiography (CTA) and perfusion (CTP) to diagnose coronary artery disease (CAD) in overweight patients lacks assessment. Our study compared iodixanol 320 mg I/ml and 270 mg I/ml on image quality and accuracy of CTA combined CTP (CTA-CTP) to diagnose CAD. METHODS: Overweight patients with suspected of CAD were randomized into iodixanol 270 group (received iodixanol 270 mg I/ml) and iodixanol 320 group (received iodixanol 320 mg I/ml). Based on these characteristics data, receiver operating characteristic (ROC) curve and corresponding area under the curve (AUC) were plotted to assess the sensitivity and specificity of the two administrations. RESULTS: The subjective definition score, signal to noise ratio, and CT value of aorta in iodixanol 320 group were higher than iodixanol 270 group. In iodixanol 270 group: the image exhibited a normal state of both vessels and myocardial perfusion; and the AUC, specificity, and sensitivity were 0.376, 66.67, and 80.46, respectively. In iodixanol 320 group: the image exhibited a diameter stenosis in right coronary artery and myocardial infarction of inferior wall and proximal inferior wall septum, as well as myocardial perfusion defects; and the AUC, specificity, and sensitivity in iodixanol 320 group were 0.824, 75.00, and 89.87, respectively. CONCLUSION: Accuracy and image quality of iodixanol 320 mg I/ml in the diagnosis of CAD with CTA-CTP was higher than using iodixanol 270 mg I/ml.

A Nano-Based P1Cminimum Probe to Monitor Thrombosis in Grafts and Improve Cardiac Function After Coronary Artery Bypass Grafting Surgery.

Magnetic resonance imaging is widely used to identify and monitor thrombi in grafted vessels following coronary artery bypass grafting surgery (CABG). We produced a biosensor, P1Cm-SPIO-Cy5.5, composed of P1Cm peptide, superparamagnetic iron oxide nanoparticles (SPIO), and polyethylene glycol (PEG), for use in MRI thrombus imaging. Activated platelets induced by adenosine diphosphate were used for combination tests in vitro. A rat model of common carotid artery thrombosis was used for anti-thrombus experiments in vivo. P1Cm-SPIO-Cy5.5 remains stable in vitro and has good anticoagulation capacity. It can bind specifically to activated platelets and thrombi. In rats that underwent bypass surgery, P1Cm-SPIO-Cy5.5 could detect and label thrombi over a long period, and prevent thrombosis in grafted vessels. P1Cm-SPIO-Cy5.5 improved cardiac function in rats following CABG surgery. P1Cm-SPIO-Cy5.5 is a potential sensor for use in MRI for the early diagnosis and prevention of thrombosis after CABG surgery.

Role of CT texture features for predicting outcome of pancreatic cancer patients with liver metastases.

Objective: The purpose of this study was to evaluate the prognostic value of computed tomography (CT) texture features of pancreatic cancer with liver metastases. Methods: We included 39 patients with metastatic pancreatic cancer (MPC) with liver metastases and performed texture analysis on primary tumors and metastases. The correlations between texture parameters were assessed using Pearson's correlation. Univariate Cox proportional hazards model was used to assess the correlations between clinicopathological characteristics, texture features and overall survival (OS). The univariate Cox regression model revealed four texture features potentially correlated with OS (P<0.1). A radiomics score (RS) was determined using a sequential combination of four texture features with potential prognostic value that were weighted according to their ß-coefficients. Furthermore, all variables with P<0.1 were included in the multivariate analysis. A nomogram,which was developed to predict OS according to independent prognostic factors, was internally validated using the C-index and calibration plots. Kaplan-Meier analysis and the log-rank test were performed to stratify OS according to the RS and nomogram total points (NTP). Results: Few significant correlations were found between texture features of primary tumors and those of liver metastases. However, texture features within primary tumors or liver metastases were significantly associated. Multivariate analysis showed that Eastern Cooperative Oncology Group performance status (ECOG PS), chemotherapy, Carbohydrate antigen 19-9 (CA19-9), and the RS were independent prognostic factors (P<0.05). The nomogram incorporating these factors showed good discriminative ability (C-index = 0.754). RS and NTP stratified patients into two potential risk groups (P<0.01). Conclusion: The RS derived from significant texture features of primary tumors and metastases shows promise as a prognostic biomarker of OS of patients with MPC. A nomogram based on the RS and other independent prognostic clinicopathological factors accurately predicts OS.

LncRNA NORAD Promotes Vascular Endothelial Cell Injury and Atherosclerosis Through Suppressing VEGF Gene Transcription via Enhancing H3K9 Deacetylation by Recruiting HDAC6.

Coronary artery disease (CAD) is a major atherosclerotic cardiovascular disease and the leading cause of mortality globally. Long non-coding RNAs (lncRNAs) play crucial roles in CAD development. To date, the effect of lncRNA non-coding RNA activated by DNA damage (NORAD) on atherosclerosis in CAD remains unclear. The primary aim of this study was to investigate the effect of lncRNA NORAD on vascular endothelial cell injury and atherosclerosis. Here, ox-LDL-treated human umbilical vein endothelial cells (HUVECs) and high-fat-diet (HFD)-fed ApoE-/- mice were utilized as in vitro and in vivo models. The present study found that lncRNA NORAD expression was increased in ox-LDL-treated HUVECs and thoracic aorta of atherosclerotic mice, and knockdown of lncRNA NORAD alleviated vascular endothelial cell injury and atherosclerosis development in vitro and in vivo. Knockdown of lncRNA NORAD aggravated ox-LDL-reduced or atherosclerosis-decreased vascular endothelial growth factor (VEGF) expression in HUVECs and thoracic aorta of mice to ameliorate vascular endothelial cell injury and atherosclerosis development. Moreover, nucleus lncRNA NORAD suppressed VEGF gene transcription through enhancing H3K9 deacetylation via recruiting HDAC6 to the VEGF gene promoter in ox-LDL-treated HUVECs. In addition, VEGF reduced FUS (FUS RNA binding protein) expression by a negative feedback regulation in HUVECs. In summary, lncRNA NORAD enhanced vascular endothelial cell injury and atherosclerosis through suppressing VEGF gene transcription via enhancing H3K9 deacetylation by recruiting HDAC6. The findings could facilitate discovering novel diagnostic markers and therapeutic targets for CAD.

The role of phosphoprotein phosphatases catalytic subunit genes in pancreatic cancer.

Compelling evidence suggests that phosphoprotein phosphatases (PPPs) are involved in a large spectrum of physiological and pathological processes, but little is known about their roles in pancreatic cancer. We investigated the expression level, prognostic value, and potential function of PPPs with data from Oncomine, GEPIA, THPA, and TCGA databases and an independent cohort of patients with pancreatic cancer. Among all the PPP catalytic subunits (PPPcs), the transcription levels of PPP1CA, PPP1CB, PPP3CA, PPP3CB, and PPP4C were higher in pancreatic cancer than in normal pancreas (P<0.01, fold change > 2). Kaplan-Meier analysis showed that high transcription levels of PPP1CA, PPP1CB, PPP2CA, PPP2CB, PPP3CA, and PPP4C correlated with poorer survival. In contrast, patients with high levels of PPP3CB, PPP3CC, PPP5C, PPP6C, and PPEF2 had much better prognoses. Data from THPA and patients with pancreatic cancer enrolled in our hospital also confirmed the prognostic value of PPP1CA, PPP1CB, PPP2CA, PPP2CB, PPP3CA, PPP3CB, and PPP6C at the protein level. In addition, the Pearson Chi-square test showed that PPP3CB level was significantly correlated with T and N stages. GO and KEGG analyses showed that the genes and pathways related to the pathogenesis and progression of pancreatic cancer were greatly affected by alterations in PPPcs. Results of the present study suggest that PPP1CA, PPP1CB, PPP2CA, PPP2CB, and PPP3CA have deleterious effects but PPP3CB, PPP5C, and PPP6C have beneficial effects on pancreatic cancer.

The prognostic and predictive value of the albumin-bilirubin score in advanced pancreatic cancer.

Albumin-bilirubin (ALBI) showed its prognostic and predictive value in hepatobiliary disease like hepatocellular carcinoma. However, little has been known about its role in pancreatic cancer.In this retrospective study, 149 patients with advanced pancreatic cancer (APC) treated in the Shanghai General Hospital from January 2009 to December 2014 were enrolled as the training cohort and 120 patients treated from January 2015 to December 2018 were taken as the validation cohort. We generated the ALBI score according previous studies. The correlations between ALBI and clinicopathological parameters were evaluated with the Pearson Chi-square test. Kaplan-Meier method and log-rank test were conducted to determine the correlation between ALBI and overall survival (OS). Then we used Cox regression model to investigate the prognostic significance of ALBI. We further assessed retrospectively whether ALBI score could be used to identify combination therapy candidates for APC.Eastern Cooperative Oncology Group Performance Status, hemoglobin, aspartate aminotransferase, and alanine aminotransferase were found to be significantly correlated with ALBI. Kaplan-Meier analysis showed that the median OS in patients with a pretreatment ALBI ≥-2.6 was 7.0 months, which was significantly shorter than OS of patients with a ALBI <-2.6 (13.0 months, P = .001). ALBI was independently correlated with OS in multivariate analysis. In the subgroup analysis, ALBI showed significant prognostic value in patients with liver metastasis but not those without liver metastasis in all 3 cohorts. In addition, only in the group with ALBI <-2.6, patients receiving combination therapy showed better prognosis than those receiving monotherapy.In conclusion, ALBI was a promising prognostic biomarker in APC with liver metastasis. ALBI also showed predictive value in identifying combination therapy candidates for patients with APC.

Cardiac CT scanning in coronary artery disease: Epicardial fat volume and its correlation with coronary artery lesions and left ventricular function.

Coronary artery disease (CAD) is a major and common disease that poses a threat to human health. Recent studies suggested that epicardial fat may have an important role in the pathogenesis of CAD. Therefore, the association between epicardial fat volume (EFV) and left ventricular function with CAD was investigated in the present study. A total of 61 patients with suspected CAD who underwent CT scanning were enrolled. Baseline data, parameters of left heart function and EFV of the subjects were collected and analyzed. The degree of coronary artery lesions was assessed using the Gensini score. Pearson's correlation analysis and a logistic regression model were applied to assess the association between EFV and risk factors for CAD, the Gensini score and left ventricular function index. A total of 29 female and 32 male subjects with a median age of 63 years were enrolled. The median body mass index (BMI) of the subjects was 23.37 kg/m2 and the median EFV was 86.41 cm3. It was revealed that risk factors of CAD, specially hypertension, diabetes mellitus, dyslipidemia, history of myocardial infarction and smoking, had no significant association with the EFV (P>0.05); however, the EFV was significantly positively correlated with the BMI (r=0.479, P<0.0001), interventricular septal thickness (r=0.436, P=0.004), left ventricular posterior wall thickness (r=0.350, P=0.0058), left ventricular end diastolic diameter (r=0.265, P=0.0388), left ventricular mass (r=0.445, P=0.0003) and left ventricular mass index (r=0.371, P=0.0035). However, no correlation was identified between the EFV and the Gensini score (r=0.131, P=0.3137). In conclusion, the EFV measured by cardiac CT scanning was positively correlated with the BMI and left ventricular function, but was not associated with the presence of CAD according to the Gensini scores.

LncRNA ZFAS1 confers inflammatory responses and reduces cholesterol efflux in atherosclerosis through regulating miR-654-3p-ADAM10/RAB22A axis.

PURPOSE: Atherosclerosis is the leading cause of cardiovascular diseases (CVD) with high incidence rate and mortality rate. Long non-coding RNAs (lncRNAs) are important functional molecules in atherosclerosis. Present study aimed to explore the functional role and underlying mechanism of ZFAS1 in atherosclerosis. METHODS: The in-vitro cell model of atherosclerosis was established by using oxidized low-density lipoprotein (ox-LDL) to induce THP-1 macrophage-derived foam cells. qRT-PCR measured the mRNA levels of ZFAS1, miR-654-3p, ADAM10 and RAB22A. Western blot detected the protein levels of ADAM10 and RAB22A. The levels of IL-1ß, IL-6 and TNF-ɑ (inflammatory biomarkers) were tested with ELISA assay. Detection of cholesterol efflux rate was experimented. The interaction between RNAs was affirmed with luciferase reporter and RNA pull-down experiments. RESULTS: The expression of ZFAS1 was significantly up-regulated in in-vitro cell model of atherosclerosis at a dose- and time-dependent manner. Knockdown of ZFAS1 impaired inflammatory responses and promoted cholesterol efflux rate. Overexpression of ZFAS1 accelerated inflammatory responses and hampered cholesterol efflux rate. Then, the cytoplasmic role of ZFAS1 was revealed. By bio-informatics analysis and mechanism assays, miR-654-3p was identified to bind with ZFAS1. Moreover, ADAM10 and RAB22A were targeted and suppressed by miR-654-3p. ZFAS1 served as a ceRNA to positively regulate ADAM10 and RAB22A expression through endogenously sponging miR-654-3p. CONCLUSION: In conclusion, ZFAS1 elevated ADAM10/RAB22A expression to reduce cholesterol efflux rate and facilitate inflammatory responses in atherosclerosis at a miR-654-3p-dependent way, suggesting a prospective treatment method for amelioration of atherosclerosis.