BACKGROUND: Aortic dissection (AD) is a fatal cardiovascular disorder without effective medications due to unclear pathogenic mechanisms. Bestrophin3 (Best3), the predominant isoform of bestrophin family in vessels, has emerged as critical for vascular pathological processes. However, the contribution of Best3 to vascular diseases remains elusive. METHODS: Smooth muscle cell–specific and endothelial cell–specific Best3 knockout mice (Best3 ^SMKO and Best3 ^ECKO , respectively) were engineered to investigate the role of Best3 in vascular pathophysiology. Functional studies, single-cell RNA sequencing, proteomics analysis, and coimmunoprecipitation coupled with mass spectrometry were performed to evaluate the function of Best3 in vessels. RESULTS: Best3 expression in aortas of human AD samples and mouse AD models was decreased. Best3 ^SMKO but not Best3 ^ECKO mice spontaneously developed AD with age, and the incidence reached 48% at 72 weeks of age. Reanalysis of single-cell transcriptome data revealed that reduction of fibromyocytes, a fibroblast-like smooth muscle cell cluster, was a typical feature of human ascending AD and aneurysm. Consistently, Best3 deficiency in smooth muscle cells decreased the number of fibromyocytes. Mechanistically, Best3 interacted with both MEKK2 and MEKK3, and this interaction inhibited phosphorylation of MEKK2 at serine153 and MEKK3 at serine61. Best3 deficiency induced phosphorylation-dependent inhibition of ubiquitination and protein turnover of MEKK2/3, thereby activating the downstream mitogen-activated protein kinase signaling cascade. Furthermore, restoration of Best3 or inhibition of MEKK2/3 prevented AD progression in angiotensin II–infused Best3 ^SMKO and ApoE ^−/− mice. CONCLUSIONS: These findings unveil a critical role of Best3 in regulating smooth muscle cell phenotypic switch and aortic structural integrity through controlling MEKK2/3 degradation. Best3–MEKK2/3 signaling represents a novel therapeutic target for AD.

Background: Patients with acute type A aortic dissection are usually transferred to the intensive care unit (ICU) after surgery. Prolonged ICU length of stay (ICU-LOS) is associated with higher level of care and higher mortality. We aimed to develop and validate machine learning models for predicting ICU-LOS after acute type A aortic dissection surgery.Methods: A total of 353 patients with acute type A aortic dissection transferred to ICU after surgery from September 2016 to August 2019 were included. The patients were randomly divided into the training dataset (70%) and the validation dataset (30%). Eighty-four preoperative and intraoperative factors were collected for each patient. ICU-LOS was divided into four intervals (10 days) according to interquartile range. Kendall correlation coefficient was used to identify factors associated with ICU-LOS. Five classic classifiers, Naive Bayes, Linear Regression, Decision Tree, Random Forest, and Gradient Boosting Decision Tree, were developed to predict ICU-LOS. Area under the curve (AUC) was used to evaluate the models' performance.Results: The mean age of patients was 51.0 ± 10.9 years and 307 (87.0%) were males. Twelve predictors were identified for ICU-LOS, namely, D-dimer, serum creatinine, lactate dehydrogenase, cardiopulmonary bypass time, fasting blood glucose, white blood cell count, surgical time, aortic cross-clamping time, with Marfan's syndrome, without Marfan's syndrome, without aortic aneurysm, and platelet count. Random Forest yielded the highest performance, with an AUC of 0.991 (95% confidence interval [CI]: 0.978–1.000) and 0.837 (95% CI: 0.766–0.908) in the training and validation datasets, respectively.Conclusions: Machine learning has the potential to predict ICU-LOS for acute type A aortic dissection. This tool could improve the management of ICU resources and patient-throughput planning, and allow better communication with patients and their families.

Background: Acute aortic dissection type A is a life-threatening disease required emergency surgery during acute phase. Different clinical manifestations, laboratory tests, and imaging features of patients with acute aortic dissection type A are the risk factors of preoperative mortality. This study aims to establish a simple and effective preoperative mortality risk assessment model for patients with acute aortic dissection type A. Methods: A total of 673 Chinese patients with acute aortic dissection type A who were admitted to our hospital were retrospectively included. All patients were unable to receive surgically treatment within 3 days from the onset of disease. The patients included were divided into the survivor and deceased groups, and the endpoint event was preoperative death. Multivariable analysis was used to investigate predictors of preoperative mortality and to develop a prediction model. Results: Among the 673 patients, 527 patients survived (78.31%) and 146 patients died (21.69%). The developmental dataset had 505 patients, calibration by Hosmer Lemeshow was significant (χ² = 3.260, df = 8, P = 0.917) and discrimination by area under ROC curve was 0.8448 (95% CI 0.8007–0.8888). The validation dataset had 168 patients, calibration was significant (χ² = 5.500, df = 8, P = 0.703) and the area under the ROC curve was 0.8086 (95% CI 0.7291–0.8881). The following independent variables increased preoperative mortality: age (OR = 1.008, P = 0.510), abrupt chest pain (OR = 3.534, P < 0.001), lactic in arterial blood gas ≥ 3 mmol/L (OR = 3.636, P < 0.001), inotropic support (OR = 8.615, P < 0.001), electrocardiographic myocardial ischemia (OR = 3.300, P = 0.001), innominate artery involvement (OR = 1.625, P = 0.104), right common carotid artery involvement (OR = 3.487, P = 0.001), superior mesenteric artery involvement (OR = 2.651, P = 0.001), false lumen / true lumen of ascending aorta ≥ 0.75 (OR = 2.221, P = 0.007). Our data suggest that a simple and effective preoperative death risk assessment model has been established. Conclusions: Using a simple and effective risk assessment model can help clinicians quickly identify high-risk patients and make appropriate medical decisions.

Thoracic Aortic Aneurysm and Dissection (TAAD) is a life-threatening pathology and remains challenging worldwide. Up to 40% of TAAD are hereditary with complex heterogeneous genetic backgrounds. Recently, next-generation sequencing (NGS) has been successfully applied to identify genetic variants in an efficient and cost-effective manner. In our study, NGS coupled with DNA target-capture array was used to screen 11 known causative genes of TAAD in 70 patients from Southern China. All the identified variants were confirmed by Sanger sequencing. We identified forty variants in 36 patients (51.4%), including three known pathogenic (7.5%), 10 likely pathogenic variants (25%, 9 inFBN1, 1 inACTA2), and 27 variants with uncertain significance (VUS) (67.5%). Among the 27 VUS, 14 (51.9%) were in theFBN1gene, 3 inCol5A2, 2 inACTA2, 2 inMYH11, 2 inMYLK, 2 inSLC2A10, 1 inMSTNand 1 inSMAD3respectively. Based on the segregation data and independent reports, five known likely pathogenic variants and four VUS were upgraded to pathogenic variant and likely pathogenic variant respectively. Our data indicate that NGS is a highly efficient genetic method for identification of pathogenic variants in TAAD patients.

Background: Acute type A aortic dissection (ATAAD) is a rare, life-threatening condition affecting the aorta. This study explores the relationship between the level of admission D-dimer, which was assessed during the first 2 h from admission, and in-hospital major adverse events (MAE) with ATAAD.Methods: A total of 470 patients with enhanced computed tomography (CT) confirmed diagnosis of ATAAD who underwent operation treatment in Guangdong Provincial People's hospital between September 2017 and June 2021 were enrolled in the present study. The X-tile program was used to determine the optimal D-dimer thresholds for risk. Restricted cubic spline (RSC) was performed to assess the association between D-dimer and endpoint. The perioperative data were compared between the two groups, univariate and multivariate analyses were used to investigate the risk factors of major adverse events (in-hospital mortality, gastrointestinal bleeding, paraplegia, acute kidney failure, reopen the chest, low cardiac output syndrome, cerebrovascular accident, respiratory insufficiency, MODS, gastrointestinal bleeding, and severe infection).Results: Among 470 patients, 151 (32.1%) had MAE. In-hospital mortality was 7.44%. The patients with D-dimer >14,500 ng/ml were more likely to present with acute kidney failure, low cardiac output, cerebrovascular accident, multiple organ dysfunction syndromes (MODS), gastrointestinal bleeding, and severe infection. D-dimer level was an independent risk factor for acute kidney failure (OR 2.09, 95% CI: 1.25–3.51, p = 0.005), MODS (OR 6.40, 95% CI: 1.23–33.39, p = 0.028), gastrointestinal bleeding (OR 17.76, 95% CI: 1.99–158.78, p = 0.010) and mortality (OR 3.17, 95% CI: 1.32–7.63, p = 0.010). Multivariate regression analysis of adverse events also suggested that D-dimer >14,500 ng/ml (OR 1.68, 95% CI: 1.09–2.61, p = 0.020) was the independent risk factor of major adverse events.Conclusions: Increasing D-dimer levels were independently associated with the in-hospital MAE and thus can be used as a useful prognostic biomarker before the surgery.

Backgrounds: The goal of this study was to assess the impact of neutrophil count, in patients with acute type A aortic dissection (ATAAD).Methods: This study retrospectively collected data from patients between September 2017 and June 2021. Youden's index was used to determine the optimal cut-off value for the neutrophil count and patients were divided into two subgroups. A restricted cubic spline (RCS) was used to model the relationship between variables and in-hospital mortality. The least absolute shrinkage and selection operator (LASSO) method and multivariate logistic regression analyses were used to investigate the independent prognostic factors for in-hospital mortality in patients with ATAAD.Results: A total of 467 patients were enrolled in this study. In-hospital mortality was 7.28%. The group with elevated neutrophil counts had significantly higher mortality than the group with decreased neutrophil counts (10.8% vs. 3.2%, P = 0.02). This data shows that elevated neutrophil count was significantly associated with in-hospital mortality (OR 3.07, 95% CI 1.22–7.62, P = 0.02).Conclusions: Neutrophil count is an independent risk factor for in-hospital mortality in patients with ATAAD. It is an effective inflammatory index, which can be individualized for patients.