Tongue color parameters in predicting the degree of coronary stenosis: a retrospective cohort study of 282 patients with coronary angiography.

Purpose: This retrospective cohort study aimed to analyze the relationship between tongue color and coronary artery stenosis severity in 282 patients after underwent coronary angiography. Methods: A retrospective cohort study was conducted to collect data from patients who underwent coronary angiography in the Department of Cardiology, Shanghai Jiading District Central Hospital from October 1, 2023 to January 15, 2024. All patients were divided into four various stenosis groups. The tongue images of each patient was normalized captured, tongue body (TC_) and tongue coating (CC_) data were converted into RGB and HSV model parameters using SMX System 2.0. Four supervised machine learning classifiers were used to establish a coronary artery stenosis grading prediction model, including random forest (RF), logistic regression, and support vector machine (SVM). Accuracy, precision, recall, and F1 score were used as classification indicators to evaluate the training and validation performance of the model. SHAP values were furthermore used to explore the impacts of features. Results: This study finally included 282 patients, including 164 males (58.16%) and 118 females (41.84%). 69 patients without stenosis, 70 patients with mild stenosis, 65 patients with moderate stenosis, and 78 patients with severe stenosis. Significant differences of tongue parameters were observed in the four groups [TC_R (P = 0.000), TC_G (P = 0.003), TC_H (P = 0.001) and TC_S (P = 0.024),CC_R (P = 0.006), CC_B (P = 0.023) and CC_S (P = 0.001)]. The SVM model had the highest predictive ability, with AUC values above 0.9 in different stenosis groups, and was particularly good at identifying mild and severe stenosis (AUC = 0.98). SHAP value showed that high values of TC_RIGHT_R, low values of CC_LEFT_R were the most impact factors to predict no coronary stenosis; high CC_LEFT_R and low TC_ROOT_H for mild coronary stenosis; low TC_ROOT_R and CC_ROOT_B for moderate coronary stenosis; high CC_RIGHT_G and low TC_ROOT_H for severe coronary stenosis. Conclusion: Tongue color parameters can provide a reference for predicting the degree of coronary artery stenosis. The study provides insights into the potential application of tongue color parameters in predicting coronary artery stenosis severity. Future research can expand on tongue features, optimize prediction models, and explore applications in other cardiovascular diseases.

CircRNA Larp4b/miR-298-5p/Mef2c Regulates Cardiac Hypertrophy Induced by Angiotensin II.

Cardiac hypertrophy (CH) is an early marker in the clinical course of heart failure. Circular RNAs (circRNAs) play important roles in human disease. However, the role of circ_Larp4b in myocardial hypertrophy has not been studied. Angiotensin II (Ang II) treated HL-1 cells to induce a CH cell model. Quantitative real-time polymerase chain reaction was used to detect the expression of circ_Larp4b, microRNA-298-5p, and myocyte enhancer factor 2 (Mef2c). Western blot detected the protein level of alpha-actinin-2 (ACTN2), beta-myosin heavy chain (ß-MHC), atrial natriuretic peptide (ANP), and Mef2c. The relationship between miR-298-5p and circ_Larp4b or Mef2c was verified by dual-luciferase reporter assay and RNA pull-down assay. Circ_Larp4b and Mef2c were upregulated in HL-1 cells treated with Ang II. Moreover, circ_Larp4b down-regulation regulated the progress of CH induced by Ang II. MiR-298-5p was a target of circ_Larp4b, and Mef2c was a target of miR-298-5p. Overexpressed Mef2c reversed the cell size inhibited by miR-298-5p in Ang II-induced HL-1 cells. Circ_Larp4b regulated CH progress by regulating miR-298-5p/Mef2c axis.

A Lightweight Network for Accurate Coronary Artery Segmentation Using X-Ray Angiograms.

An accurate and automated segmentation of coronary arteries in X-ray angiograms is essential for cardiologists to diagnose coronary artery disease in clinics. The existing deep learning-based coronary arteries segmentation models focus on using complex networks to improve the accuracy of segmentation while ignoring the computational cost. However, performing such segmentation networks requires a high-performance device with a powerful GPU and a high bandwidth memory. To address this issue, in this study, a lightweight deep learning network is developed for a better balance between computational cost and segmentation accuracy. We have made two efforts in designing the network. On the one hand, we adopt bottleneck residual blocks to replace the internal components in the encoder and decoder of the traditional U-Net to make the network more lightweight. On the other hand, we embed the two attention modules to model long-range dependencies in spatial and channel dimensions for the accuracy of segmentation. In addition, we employ Top-hat transforms and contrast-limited adaptive histogram equalization (CLAHE) as the pre-processing strategy to enhance the coronary arteries to further improve the accuracy. Experimental evaluations conducted on the coronary angiograms dataset show that the proposed lightweight network performs well for accurate coronary artery segmentation, achieving the sensitivity, specificity, accuracy, and area under the curve (AUC) of 0.8770, 0.9789, 0.9729, and 0.9910, respectively. It is noteworthy that the proposed network contains only 0.75 M of parameters, which achieves the best performance by the comparative experiments with popular segmentation networks (such as U-Net with 31.04 M of parameters). Experimental results demonstrate that our network can achieve better performance with an extremely low number of parameters. Furthermore, the generalization experiments indicate that our network can accurately segment coronary angiograms from other coronary angiograms' databases, which demonstrates the strong generalization and robustness of our network.

Diffuse ST segment depression and ST segment elevation in lead aVR and V1 by left circumflex artery occlusion.

The current guidelines for resting electrocardiograms of diffuse ST segment depression coupled with ST segment elevation in aVR and/or V1 that are otherwise unremarkable indicate multivessel or left main coronary artery obstruction. However, our case meets the above electrocardiogram changes, but involves left circumflex artery occlusion.

Coarctation of the aorta diagnosed by cardiac computed tomography angiography in a 49-year-old man presenting with acute myocardial infarction.

Aortic coarctation in patients with acute myocardial infarction is an uncommon finding, We report the case of a 49-year-old man with acute myocardial infarction and diagnosis of severe aortic coarctation in concomitance with a three-dimensional computed tomography scan.