Association study of NAFLD with pericoronary adipose tissue and pericardial adipose tissue: Diagnosis of stable CAD patients with NAFLD based on radiomic features.

BACKGROUND AND AIM: Nonalcoholic fatty liver disease (NAFLD) is prone to complicated cardiovascular disease, and we aimed to identify patients with NAFLD who are prone to developing stable coronary artery disease (CAD). METHODS AND RESULTS: We retrospectively recruited adults who underwent coronary computed tomography angiography (CTA). A total of 127 NAFLD patients and 127 non-NAFLD patients were included in this study. Clinical features and imaging parameters were analysed, mainly including pericardial adipose tissue (PAT), pericoronary adipose tissue (PCAT), and radiomic features of 6792 PCATs. The inflammatory associations of NAFLD patients with PAT and PCAT were analysed. Clinical features (model 1), CTA parameters (model 2), the radscore (model 3), and a composite model (model 4) were constructed to identify patients with NAFLD with stable CAD. The presence of NAFLD resulted in a greater inflammatory involvement in all three coronary arteries (all P < 0.01) and was associated with increased PAT volume (r = 0.178**, P < 0.05). In the presence of NAFLD, the mean CT value of the PAT was significantly correlated with the fat attenuation index (FAI) in all three vessels and had the strongest correlation with the RCA FAI (r = 0.55, p < 0.001). A total of 9 radiomic features were screened by LASSO regression to calculate radiomic scores. In the model comparison, model 4 had the best performance of all models (AUC 0.914 [0.863-0.965]) and the highest overall diagnostic value of the model (sensitivity: 0.814, specificity: 0.941). CONCLUSIONS: NAFLD correlates with PAT volume and PCAT inflammation. Furthermore, combining clinical features, CTA parameters, and radiomic scores can improve the efficiency of early diagnosis of stable CAD in patients with NAFLD.

Multi-Object Trajectory Prediction Based on Lane Information and Generative Adversarial Network.

Nowadays, most trajectory prediction algorithms have difficulty simulating actual traffic behavior, and there is still a problem of large prediction errors. Therefore, this paper proposes a multi-object trajectory prediction algorithm based on lane information and foresight information. A Hybrid Dilated Convolution module based on the Channel Attention mechanism (CA-HDC) is developed to extract features, which improves the lane feature extraction in complicated environments and solves the problem of poor robustness of the traditional PINet. A lane information fusion module and a trajectory adjustment module based on the foresight information are developed. A socially acceptable trajectory with Generative Adversarial Networks (S-GAN) is developed to reduce the error of the trajectory prediction algorithm. The lane detection accuracy in special scenarios such as crowded, shadow, arrow, crossroad, and night are improved on the CULane dataset. The average F1-measure of the proposed lane detection has been increased by 4.1% compared to the original PINet. The trajectory prediction test based on D2-City indicates that the average displacement error of the proposed trajectory prediction algorithm is reduced by 4.27%, and the final displacement error is reduced by 7.53%. The proposed algorithm can achieve good results in lane detection and multi-object trajectory prediction tasks.

Correlation analysis of the abdominal visceral fat area with the structure and function of the heart and liver in obesity: a prospective magnetic resonance imaging study.

BACKGROUND: The differences in fat deposition sites exhibit varying degrees of systemic inflammatory responses and organ damage, especially in obese individuals with excessive visceral fat. Visceral fat, which is closely related to an increase in mortality rates related to heart and liver diseases. However, few studies have analysed the differences in heart and liver indicators and their correlation among groups based on the abdominal visceral fat area (AVFA). OBJECTIVE: Clarifying the differences in and correlations of heart and liver indicators among groups with different severities of AVFA by magnetic resonance imaging (MRI). METHODS: Sixty-nine subjects with obesity were enrolled. The study group consisted of forty-one individuals (AVFA ≥ 150 cm2), and the control group consisted of twenty-eight individuals (100 cm2 ≤ AVFA < 150 cm2). The differences in and correlations between clinical, laboratory, and MRI indicators of the heart and liver between the two groups were analysed. RESULTS: In the study group, the incidences of type 2 diabetes mellitus (T2DM) and insulin resistance were higher, and liver function indicators were worse. The left ventricular eccentricity ratio (LVER), left ventricular mass (LVM) and global peak wall thickness (GPWT) were higher in the study group than in the control group (P = 0.002, P = 0.001, P = 0.03), and the left ventricle global longitudinal strain (LVGLS) was lower in the study group than in the control group (P = 0.016). The pericardiac adipose tissue volume (PATV) and myocardial proton density fat fraction (M-PDFF) were higher in the study group than in the control group (P = 0.001, P = 0.001). The hepatic proton density fat fraction (H-PDFF) and abdominal subcutaneous fat area (ASFA) were higher in the study group than in the control group (P < 0.001, P = 0.012). There was a moderate positive correlation (ρ = 0.39-0.59, P < 0.001) between the AVFA and LVER, LVM, GPWT, LVGLS, and H-PDFF. There was no difference in right ventricular and most left ventricular systolic and diastolic function between the two groups. CONCLUSION: The high AVFA group had a larger LVM, GPWT and PATV, more obvious changes in LVER, impaired left ventricular diastolic function, an increased risk of heart disease, and more severe hepatic fat deposition and liver injury. Therefore, there is a correlation between the amount of visceral adipose tissue and subclinical cardiac changes and liver injury.

Diagnosis of coronary artery disease in patients with type 2 diabetes mellitus based on computed tomography and pericoronary adipose tissue radiomics: a retrospective cross-sectional study.

BACKGROUND: Patients with type 2 diabetes mellitus (T2DM) are highly susceptible to cardiovascular disease, and coronary artery disease (CAD) is their leading cause of death. We aimed to assess whether computed tomography (CT) based imaging parameters and radiomic features of pericoronary adipose tissue (PCAT) can improve the diagnostic efficacy of whether patients with T2DM have developed CAD. METHODS: We retrospectively recruited 229 patients with T2DM but no CAD history (146 were diagnosed with CAD at this visit and 83 were not). We collected clinical information and extracted imaging manifestations from CT images and 93 radiomic features of PCAT from all patients. All patients were randomly divided into training and test groups at a ratio of 7:3. Four models were constructed, encapsulating clinical factors (Model 1), clinical factors and imaging indices (Model 2), clinical factors and Radscore (Model 3), and all together (Model 4), to identify patients with CAD. Receiver operating characteristic curves and decision curve analysis were plotted to evaluate the model performance and pairwise model comparisons were performed via the DeLong test to demonstrate the additive value of different factors. RESULTS: In the test set, the areas under the curve (AUCs) of Model 2 and Model 4 were 0.930 and 0.929, respectively, with higher recognition effectiveness compared to the other two models (each p < 0.001). Of these models, Model 2 had higher diagnostic efficacy for CAD than Model 1 (p < 0.001, 95% CI [0.129-0.350]). However, Model 4 did not improve the effectiveness of the identification of CAD compared to Model 2 (p = 0.776); similarly, the AUC did not significantly differ between Model 3 (AUC = 0.693) and Model 1 (AUC = 0.691, p = 0.382). Overall, Model 2 was rated better for the diagnosis of CAD in patients with T2DM. CONCLUSIONS: A comprehensive diagnostic model combining patient clinical risk factors with CT-based imaging parameters has superior efficacy in diagnosing the occurrence of CAD in patients with T2DM.

A predictive model of 30-day mortality in patients with acute type A aortic dissection.

OBJECTIVE: Acute type A aortic dissection (ATAAD) is a life-threatening cardiovascular disease that requires an effective predictive model to predict and assess a patient's risk of death. Our study aimed to construct a model for predicting the risk of 30-day death in patients with ATAAD and the prediction accuracy of the German Registry of Acute Aortic Dissection Type A (GERAADA) Score and the European System for Cardiac Operative Risk Evaluation (EuroSCORE II) was verified. MATERIALS AND METHODS: Between June 2019 and June 2023, 109 patients with ATAAD underwent surgical treatment at our hospital (35 in the death group and 74 in the survival group). The differences in image parameters between the two groups were compared. Search for independent predictors and develop models that predict 30-day mortality in patients with ATAAD. GERAADA Score and EuroSCORE II were retrospectively calculated and indicated mortality was assessed using the receiver operating characteristic (ROC) curve. RESULTS: Logistic regression analysis showed that ascending aortic length and pericardial effusion were independent predictors of death within 30 days in patients with ATAAD. We constructed four models, GERAADA Score (Model 1), EuroSCORE II (Model 2), Model 1, ascending aorta length, and pericardial effusion (Model 3), and Model 2, ascending aorta length, and pericardial effusion (Model 4). The area under the curve (AUC = 0.832) of Model 3 was significantly different from those of Models 1 (AUC = 0.683) and 2 (AUC = 0.599), respectively (p < 0.05, DeLong test). CONCLUSIONS: Adding ascending aorta length and pericardial effusion to the GERAADA Score can improve the predictive power of 30-day mortality in patients with ATAAD.

Triglyceride glucose index is associated with functional coronary artery stenosis in hypertensive patients.

Background: The triglyceride glucose (TyG) index is an effective method for determining insulin resistance (IR). Limited research has explored the connection between the TyG index and functionally significant stenosis in hypertensive patients. Furthermore, the connections between the TyG index, fat attenuation index (FAI) and atherosclerotic plaque characteristics are also worth exploring. Methods: The study screened 1622 hypertensive participants without coronary artery disease history who underwent coronary computed tomography angiography. The TyG index was calculated as ln (fasting glucose [mg/dL] * fasting TG [mg/dL]/2). Adverse plaque characteristics (HRPCs), high-risk plaques (HRPs), FAI, and CT-derived fractional flow reserve (FFRCT) were analyzed and measured for all patients. Functionally significant stenosis causing ischemia is defined as FFRCT ≤ 0.80. Two patient groups were created based on the FFRCT: the FFRCT < 0.80 group and the FFRCT > 0.80 group. In hypertensive patients, the association between the TyG index and FFRCT was examined applying a logistic regression model. Results: The TyG index was higher for people with FFRCT ≤ 0.80 contrast to those with FFRCT > 0.80. After controlling for additional confounding factors, the logistic regression model revealed a clear connection between the TyG index and FFRCT ≤ 0.80 (OR = 1.718, 95% CI 1.097-2.690, p = 0.018). The restricted cubic spline analysis displayed a nonlinear connection between the TyG index and FFRCT ≤ 0.80 (p for nonlinear = 0.001). The TyG index increased the fraction of individuals with HRPs and HRPCs, FAI raised, and FFRCT decreased (p < 0.05). The multivariate linear regression analysis illustrated a powerfulcorrelation between high TyG index levels and FAI, FFRCT, positive remodeling (PR), and low-attenuation plaque (LAPs) (standardized regression coefficients: 0.029 [p = 0.007], -0.051 [p < 0.001], 0.029 [p = 0.027], and 0.026 [p = 0.046], separately). Conclusion: In hypertensive patients, the TyG index showed an excellent association with a risk of FFRCT ≤ 0.80. Additionally, the TyG index was also linked to FAI, FFRCT, PR, and LAPs.

Enhanced CT-Based Intratumoral and Peritumoral Radiomics Nomograms Predict High-Grade Patterns of Invasive Lung Adenocarcinoma.

RATIONALE AND OBJECTIVES: Extraction of intratumoral and peritumoral radiomics features combined with clinical factors to establish nomograms to predict high-grade patterns (micropapillary and solid) of invasive adenocarcinoma of the lung (IAC). MATERIALS AND METHODS: A retrospective study was conducted on 463 patients with pathologically confirmed IAC. Patients were randomized in a 7:3 ratio into a training cohort (n = 324) and a testing cohort (n = 139). A total of 2154 CT-based radiomic features were extracted from each of the four regions: gross tumor volume (GTV) and gross peritumoral tumor volume (GPTV3, GPTV6, GPTV9) containing peri-tumor regions of 3 mm, 6 mm, and 9 mm. A radiomics nomogram was constructed based on the optimal radiomics model and clinically independent predictors. RESULTS: The GPTV3 radiomics model showed better predictive performance in the testing group compared to the GTV (0.840), GPTV6 (0.843), and GPTV9 (0.734) models, with an AUC value of 0.889 in the testing group. In the clinical model, tumor density and the presence of a spiculation sign were identified as independent predictors. The nomogram, which combined these independent predictors with the GPTV3-Radscore, proved to be clinically useful. CONCLUSION: The GPTV3 radiomics model was superior to the GTV, GPTV6, and GPTV9 radiomics models in predicting high-grade patterns (HGP) of IAC. In addition, nomograms based on GPTV3 radiomics features and clinically independent predictors can further improve the prediction efficiency.

Correlation analysis of dynamic changes of abdominal fat during rapid weight loss after bariatric surgery: A prospective magnetic resonance imaging study.

OBJECTIVE: The factors related to the changes in the liver and abdominal adipose tissue during the rapid weight loss after bariatric surgery remain uncertain. METHODS: This study included 44 participants who had undergone sleeve gastrectomy. The study aimed to analyze changes and correlations of body weight (BW), laboratory tests, and magnetic resonance imaging (MRI) indicators of the liver and abdominal adipose tissue conducted before and after bariatric surgery at 1, 3, and 6 months. RESULTS: Following a rapid weight loss within 6 months of surgery, there was a concurrent decrease in blood glucose, blood lipids, and fat content of the liver and abdomen and the changes showed a correlation. The change of BW (ΔBW) was positively correlated with the change of hepatic proton density fat fraction (ΔPDFF) in one and three months after surgery and was positively correlated with the change of abdominal visceral fat area (ΔAVFA) in six months after surgery, (P<0.05). In one month after surgery, ΔPDFF was positively correlated with the change of aspartate aminotransferase (ΔAST), change of alanine aminotransferase (ΔALT), and change of triglyceride glucose (ΔTYG) index (P<0.05). ΔPDFF was positively correlated with the change of hepatic native T1 values (P<0.001) and was moderately negatively correlated with the change of hepatic apparent diffusion coefficient (ΔADC) values in three months after surgery (P<0.05). CONCLUSION: ΔBW can serve as an indirect indicator for evaluating changes in liver fat fraction at 1 and 3 months after bariatric surgery and indicative of changes in visceral fat 6 months after surgery. ΔPDFF was positively correlated with ΔAST, ΔALT and ΔTYG index in 1 months after surgery.

Identification of patients with acute coronary syndrome and representation of their degree of inflammation: application of pericoronary adipose tissue within different radial distances of the proximal coronary arteries.

Background: Pericoronary adipose tissue (PCAT) around the proximal right coronary artery (RCA) is considered a marker of coronary inflammation. We aimed to explore the segments of PCAT that represent coronary inflammation in patients with acute coronary syndrome (ACS) and to identify patients with ACS and stable coronary artery disease (CAD) prior to intervention. Methods: We retrospectively enrolled consecutive patients with ACS and stable CAD who underwent invasive coronary angiography (ICA) after coronary computed tomography angiography (CCTA) from November 2020 to October 2021 at the Fourth Affiliated Hospital of Harbin Medical University. The fat attenuation index (FAI) was obtained using PCAT quantitative measurement software, and the coronary Gensini score was also calculated to indicate the severity of CAD. The differences and correlations between FAI within different radial distances of proximal coronary arteries were evaluated, and the recognition ability of FAI for patients with ACS and stable CAD was evaluated by establishing receiver operator characteristic (ROC) curves. Results: A total of 267 patients were included in the cross-sectional study, including 173 patients with ACS. With the increase of radial distance from the outer wall of proximal coronary vessels, the FAI decreased (P<0.001). The FAI around the proximal left anterior descending artery (LAD) within the reference diameter from the outer wall of the vessel (LADref) had the highest correlation with the FAI around culprit lesions [r=0.587; 95% confidence interval (CI): 0.489-0.671; P<0.001]. The model based on clinical features, Gensini score, and LADref had the highest recognition performance for patients with ACS and stable CAD [area under the curve (AUC): 0.663; 95% CI: 0.540-0.785]. Conclusions: LADref is most correlated with FAI around culprit lesions in patients with ACS and has higher value in the preintervention differentiation of patients with ACS and stable CAD compared to the use of clinical features alone.

Model development and validation of noninvasive parameters based on coronary computed tomography angiography to predict culprit lesions in acute coronary syndromes within 3 years: value of plaque characteristics, hemodynamics and pericoronary adipose tissue.

Background: Machine learning (ML) is combined with noninvasive parameters from coronary computed tomography angiography (CTA) to construct predictive models to identify culprit lesions that may lead to acute coronary syndrome (ACS). Methods: We retrospectively analyzed 132 patients with ACS at the Fourth Affiliated Hospital of Harbin Medical University who had coronary CTA between 3 months and 3 years before the ACS event, with a total of 240 lesions. Lesions from 2020 (n=154) were included in the training set, and lesions from 2021 (n=86) were included in the test set for internal validation. We evaluated the role of plaque characteristics, hemodynamic parameters and pericoronary adipose tissue (PCAT) attenuation from CTA in identifying culprit ACS lesions. In the training set, logistic regression was used to screen CTA-derived parameters with P values <0.05 for the model construction. Logistic regression, random forest, Bayesian and K-nearest neighbor algorithms were used to build classification models, and their performance was assessed using the test set. The following models were established to evaluate the effectiveness of different combinations of models to identify culprit lesions: Model 1 was established for plaque characteristics; Model 2 was established for hemodynamic parameters; Model 3 was established for PCAT attenuation; Model 4 was established for plaque characteristics and hemodynamic parameters; and Model 5 was established for plaque characteristics, hemodynamic parameters and PCAT attenuation. Results: A total of ten high-risk factors were screened for the ML model construction, and the ML model based on the logistic regression algorithm had the best performance among the four algorithms (accuracy =0.721; sensitivity =0.892; specificity =0.592; positive prediction =0.623; and negative prediction =0.879). In this model, the minimum lumen area, positive remodeling and lesion-specific fat attenuation index (FAI) were the risk factors significantly associated with the culprit lesion. Analysis of the effect of different combinations of models to identify culprit lesions showed that Model 5 had the best predictive effect (AUC =0.819 and 95% CI: 0.722-0.916). Conclusions: ACS can be predicted using ML based on CTA parameters. Compared to other models, the model combining plaque characteristics, hemodynamic parameters and PCAT attenuation performed best in predicting the culprit lesion.

MiR-21 and let-7 cooperation in the regulation of lung cancer.

Background: Lung cancer occurs and develops as a result of a complicated process involving numerous genes; therefore, single-gene regulation has a limited therapeutic effect. We discovered that miR-21 expression was high in lung cancer tissues and cells, whereas let-7 expression was low, and it is unclear whether their combined regulation would be superior to therapy involving single regulation. The goal of our research was to investigate this situation and the regulatory mechanism that exists between these genes. Methods: To regulate the levels of miR-21 and let-7 in these two types of lung cancer cells, we transfected miRNA mimics or inhibitors into A549 and H460 cells. Lung cancer cells were tested for proliferation, apoptosis, migration, and invasion. The results were verified using a Western blot and a qRT-PCR assay. Bioinformatics was used to investigate their potential regulatory pathways, and luciferase assays were used to confirm the binding sites. Results: The expression of miR-21 was increased and that of let-7 was decreased in lung cancer tissues and cells compared with paracancerous tissues and normal lung cells (p < 0.01). Tumor cells were inhibited by downregulation of miR-21 and upregulation of let-7, and cooperative regulation showed a better effect. Upregulation of miR-21 and downregulation of let-7 promoted tumor cells, and this tumor-promoting effect was amplified by cooperative regulation. MiR-21 regulated lung cancer cells directly via the Wnt/-catenin pathway, and let-7 exerted its effects via the PLAG1/GDH1 pathway. MiR-21 and let-7 cooperated to regulate lung cancer cells via the K-ras pathway. Conclusions: The effect of cooperative regulation of miR-21 and let-7 on lung cancer is greater than that of a single miRNA. MiR-21 and let-7 are important differentially expressed genes in lung cancer that are regulated by the K-ras pathway. As a result, for multigene lung cancer, the cooperative regulation of two miRNAs will provide a new target and direction for lung cancer treatment in the future.