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Objectives This study aims to retrospectively analyze the clinical features of Stanford type A acute aortic dissection (TAAAD) based on Sun’s modified classification, and to investigate whether the Sun’s modified classification can be used to assess the risk of preoperative rupture. Methods Clinical data was collected between January 2018 and June 2019. Data included patient demographics, history of disease, type of dissection according to the Sun’s modified classification, time of onset, biochemical tests, and preoperative rupture. Results A total of 387 patients with TAAAD who met the inclusion criteria of Sun’s modified classification were included. There were more complex types, with 75, 151 and 140 patients in the type A1C, A2C and A3C groups, respectively. The age of the entire group of patients was 51.46 ± 12.65 years and 283 (73.1%) were male. The time from onset to the emergency room was 25.37 ± 30.78 h. There were a few cases of TAAAD combined with stroke, pericardial effusion, pleural effusion, and lower extremity and organ ischemia in the complex type group. The white blood cell count (WBC), neutrophil count (NEC) and blood amylase differed significantly between the groups. Three independent risk factors for preoperative rupture were identified: neutrophil count, blood potassium ion level, and platelet count. Binary logistic regression analysis showed that the Sun’s modified classification could not be used to assess the risk of preoperative rupture in TAAAD. Conclusion TAAAD was classified as the complex type in most patients. WBC, NEC and blood amylase were significantly different between the groups. NEC and serum potassium ion level were independent risk factors for preoperative rupture of TAAAD, while platelet count was its protective factor. More samples are needed to determine whether Sun’s modified classification can be used to evaluate the risk of preoperative rupture.

Flow patterns and classification within Spontaneous Isolated Superior Mesenteric Artery Dissection (SISMAD) are crucial for selecting subsequent treatment options. This study aims to propose a new classification of SISMAD and to propose two corresponding treatment plans based on this new classification. The 3D models of 70 patients with SISMAD were reconstructed and classified into Li types I-V based on morphology, followed by computational fluid dynamics analysis. The results show significant differences in blood flow patterns among patients with the same Li-type SISMAD, suggesting that the same treatment plan should not be applied universally. Based on the different blood flow conditions, a new classification of SISMAD is proposed (HX classification): Type I (dual-lumen flow type), subdivided into Ia and Ib; and Type II (single-lumen flow type). The simulation reveals that the rupture area of Type I SISMAD is related to the pressure difference between its true and false lumens, while the maximum-to-minimum diameter ratio of Type II SISMAD is associated with insufficient true lumen blood supply and lumen dilation. Furthermore, based on patient follow-up data and hemodynamic simulation results, corresponding treatment plans were proposed for the new classification: Type I was judged based on the ratio of rupture area to entrance area as a risk factor, and intervention treatment was recommended if the value was greater than 0.44; Type II can be judged as a risk factor based on the ratio of minimum diameter to maximum diameter, and if the value is less than 0.38, intervention treatment is recommended.

In this paper, we proposed a complete study method to achieve accurate aortic dissection diagnosis at the patient level. Based on the CT angiography (CTA) images, a classification model named DAT-DenseNet, which combined the deep attention Transformer module with the DenseNet architecture is proposed. In the first phase, two DAT-DenseNet are combined in parallel. It is used to accurately achieve two classification task at the CTA images. In the second stage, we propose a feature fusion module. It concatenates and fuses the image features output from the two classification models on a patient by patient basis. In the comparison experiments of classification model performance, DAT-DenseNet obtained 92.41% accuracy at the image level, which was 2.20% higher than the commonly used model. In the comparison experiments of model fusion method, our method obtained 90.83% accuracy at the patient level. The experiments showed that DAT-DenseNet model exhibits high performance at the image level. Our feature fusion module achieves the mapping from two classification image features to patient outcomes. It achieves accurate patient classification. The experiments’ results in the Discussion section elaborate the details of the experiment and confirmed that the results were reliable.

The following fictional case is intended as a learning tool within the Pathology Competencies for Medical Education (PCME), a set of national standards for teaching pathology. These are divided into three basic competencies: Disease Mechanisms and Processes, Organ System Pathology, and Diagnostic Medicine and Therapeutic Pathology. For additional information, and a full list of learning objectives for all three competencies, seehttp://journals.sagepub.com/doi/10.1177/2374289517715040.

This chapter contains sections titled: Acute Aortic Syndrome Congenital Aortic Diseases Acquired Aortic Diseases Recommended Reading

Stanford type A aortic dissection (TAAD) is a fatal cardiovascular emergency with high mortality within 48 hours. Elucidating molecular mechanisms and identifying reliable biomarkers are essential for improving diagnosis and guiding targeted interventions. We integrated four transcriptome datasets and two single-cell transcriptomic datasets using Harmony batch correction. Differentially expressed genes were identified with DESeq2. Three machine learning algorithms, LASSO, random forest, and SVM-RFE, were employed to identify hub genes, and SHAP analysis was used to quantify their individual contributions. A diagnostic system incorporating seven algorithms was constructed. Immune infiltration profiling, cell-cell communication analysis, and pseudotime trajectory analysis were performed. The proliferation and migration of vascular smooth muscle cells (VSMCs) were assessed using CCK-8 and wound healing assays. Integration of bulk and single cell transcriptomic datasets identified three hub genes, SIX4, SCNN1B, and PCDH11X, through convergent machine learning approaches. SHAP analysis highlighted SIX4 as the predominant predictor within diagnostic models, which consistently achieved high accuracy (AUC > 0.9). Single cell profiling localized SIX4 expression to synthetic vascular smooth muscle cells, where it was linked to enhanced CXCL12-CXCR4 mediated immune interactions and remodeling of the inflammatory microenvironment. Functional assays confirmed that SIX4 overexpression promoted vascular smooth muscle cell proliferation and migration, corroborating its role in TAAD progression. This study uncovered SIX4, SCNN1B, and PCDH11X as critical regulators of TAAD. SIX4 was identified as a key modulator of smooth muscle cell plasticity and immune signaling dynamics. These findings deepen our understanding of TAAD pathogenesis and demonstrate the utility of SHAP-guided models in identifying and prioritizing mechanistic drivers in this complex vascular disease.

This study aims to investigate the associations between specific bacterial taxa of the gut microbiome and the development of aortic aneurysm diseases, utilizing Mendelian Randomization (MR) to explore these associations and overcome the confounding factors commonly present in observational studies. Employing the largest available gut microbiome and aortic aneurysm Genome-Wide Association Study databases, including MiBioGen, Dutch Microbiome Project, FinnGen, UK Biobank, and Michigan Genomics Initiative, this study performs two-sample bidirectional MR analyses. Instrumental variables, linked to microbiome taxa at significant levels, were selected for identifying relationships with abdominal aortic aneurysms (AAA), thoracic aortic aneurysms (TAA), and aortic dissection (AD). Methods like inverse variance weighted, MR-PRESSO, MR-Egger, weighted median, simple mode, and mode-based estimate were used for MR analysis. Heterogeneity was assessed with the Cochran Q test. MR-Egger regression and MR-PRESSO addressed potential unbalanced horizontal pleiotropy. The analysis did not find any evidence of statistically significant associations between the gut microbiome and aortic aneurysm diseases after adjusting for the false discovery rate (FDR). Specifically, while initial results suggested correlations between 19 taxa and AAA, 25 taxa and TAA, and 13 taxa with AD, these suggested associations did not hold statistical significance post-FDR correction. Therefore, the role of individual gut microbial taxa as independent factors in the development and progression of aortic aneurysm diseases remains inconclusive. This finding underscores the necessity for larger sample sizes and more comprehensive studies to further investigate these potential links. The study emphasizes the complex relationship between the gut microbiome and aortic aneurysm diseases. Although no statistically significant associations were found after FDR correction, the findings provide valuable insights and highlight the importance of considering gut microbiota in aortic aneurysm diseases research. Understanding these interactions may eventually contribute to identifying new therapeutic and preventive strategies for aortic aneurysm diseases.

Mesenteric malperfusion (MMP) is an uncommon but devastating complication of acute aortic dissection (AAD) that combines 2 life-threatening conditions-aortic dissection and acute mesenteric ischemia. The complex pathophysiology of MMP poses substantial diagnostic and management challenges. Currently, delayed diagnosis remains a critical contributor to poor outcomes because of the absence of reliable individualized risk assessment tools. This study aims to develop and validate a deep learning-based model that integrates multimodal data to identify patients with AAD at high risk of MMP. This multicenter retrospective study included 525 patients with AAD from 2 hospitals. The training and internal validation cohort consisted of 450 patients from Beijing Anzhen Hospital, whereas the external validation cohort comprised 75 patients from Nanjing Drum Tower Hospital. Three machine learning models were developed: the benchmark model using laboratory parameters, the multiorgan feature-based AAD complicating MMP (MAM) model based on computed tomography angiography images, and the integrated model combining both data modalities. Model performance was assessed using the area under the curve, accuracy, sensitivity, specificity, and Brier score. To improve interpretability, gradient-weighted class activation mapping was used to identify and visualize discriminative imaging features. Univariate and multivariate regression analyses were used to evaluate the prognostic significance of the risk score generated by the optimal model. In the external validation cohort, the integrated model demonstrated superior performance, with an area under the curve of 0.780 (95% CI 0.777-0.785), which was significantly greater than those of the benchmark model (0.586, 95% CI 0.574-0.586) and the MAM model (0.732, 95% CI 0.724-0.734). This highlights the benefits of multimodal integration over single-modality approaches. Additional classification metrics revealed that the integrated model had an accuracy of 0.760 (95% CI 0.758-0.764), a sensitivity of 0.667 (95% CI 0.659-0.675), a specificity of 0.783 (95% CI 0.781-0.788), and a Brier score of 0.143 (95% CI 0.143-0.145). Moreover, gradient-weighted class activation mapping visualizations of the MAM model revealed that during positive predictions, the model focused more on key anatomical areas, particularly the superior mesenteric artery origin and intestinal regions with characteristic gas or fluid accumulation. Univariate and multivariate analyses also revealed that the risk score derived from the integrated model was independently associated with inhospital mortality risk among patients with AAD undergoing endovascular or surgical treatment (odds ratio 1.030, 95% CI 1.004-1.056; P=.02). Our findings demonstrate that compared with unimodal approaches, an integrated deep learning model incorporating both imaging and clinical data has greater diagnostic accuracy for MMP in patients with AAD. This model may serve as a valuable tool for early risk identification, facilitating timely therapeutic decision-making. Further prospective validation is warranted to confirm its clinical utility. Chinese Clinical Registry Center ChiCTR2400086050; http://www.chictr.org.cn/showproj.html?proj=226129.

The helical morphology of Type B aortic dissections (TBAD) represents a potentially important geometric biomarker that may influence dissection progression. While three-dimensional surface-based quantification methods provide accurate TBAD helicity assessment, their clinical adoption remains limited by significant processing time. We developed and validated a clinically practical centerline-based helicity quantification method using routine imaging software (TeraRecon) against an extensively validated surface-based method (SimVascular). In 87 TBAD patients, we semi-automatically extracted aortic, true lumen, and branch vessel centerlines from CT imaging. Helical parameters, including true lumen helical angle and peak helical twist, were computed relative to a standardized anatomical reference, enabling classification of patients into four distinct helicity categories: left-chiral, right-chiral, non-helical, and mixed-chiral patterns. The centerline method demonstrated 92% classification accuracy with excellent agreement with surface-based measurements (Cohen’s κ=0.88, p<0.001). Wilcoxon signed-rank tests revealed a median difference of −0.4∘ (z=−1.08, p=0.28), indicating no statistically significant systematic bias between methods. This centerline approach we have developed provides clinically feasible TBAD helicity classification while maintaining excellent agreement with the gold-standard surface-based method. This technique can integrate seamlessly with existing clinical workflows, enabling practical assessment of TBAD helical morphology for enhanced risk stratification and personalized treatment planning.

Bicuspid aortic valve (BAV), a common congenital anomaly with various morphological phenotypes, is also characterised by marked heterogeneity in clinical presentations including clinically silent condition with mild valvulo-aortopathy, progressive valvulopathy and complex valvulo-aortopathy with shorter life expectancy. The clinical importance of using a general and unified nosology for BAV is well-accepted by opinion leaders and an international consensus statement has been recently published, which will serve as an important scientific platform for BAV. This review describes the current knowledge of BAV based on clinical studies, addresses several unresolved issues requiring investigators’ attention and highlights the necessity of prospective studies with a very long follow-up duration for better appreciation of BAV-associated valvulo-aortopathy. In addition, the progression of valvular calcification in patients with BAV and its potential contribution to development of valvulopathy will be discussed.