Objective: The present study aimed to prospectively evaluate the role of metagenomic next-generation sequencing (mNGS) in the etiological diagnosis of patients with perioperative infective endocarditis (IE).Methods: From May 1st, 2019 to December 31st, 2020, a total of 99 patients with IE were enrolled in the present study according to the modified Duke criteria, etiological, and pathological results. 11 non-IE patients undergoing heart valve surgery in the same period were selected as the control group. A blood culture test was performed immediately after admission, and the valves harvested operatively were examined by blood culture and mNGS.Results: In the IE group, there were 29 cases (29.3%) with positive blood culture, 16 cases (16.2%) with positive valve culture, and 85 cases (85.9%) with positive valve mNGS. Compared to culture-based detection, mNGS achieved better performance with a sensitivity, specificity, area under the curve (AUC) of 0.859, 0.727, and 0.793, respectively. The combined approach using culture and mNGS further improved the diagnostic accuracy (sensitivity 89.9%, specificity 72.7%, AUC 0.813). Preoperative white blood cell (P = 0.029) and neutrophils (P = 0.046) were identified as independent factors affecting the detection rate of mNGS. In the mNGS-positive group, 95 strains of pathogens were found and 10 cases were identified with mixed infection. There were 72 gram-positive bacteria and 14 gram-negative bacteria. mNGS positive group displayed higher species richness than mNGS negative group with enrichment of Streptococcus sanguis, Streptococcus buccalis, and Streptococcus griseus. Proteobacteria and Actinomycetes were enriched in mNGS negative group. Notably, six patients showed disconcordant results between culture and mNGS. Rothia aeria was identified in the blood culture, valve culture, and valve mNGS in one patient. Bartonella Quintana and Coxiella burnetii, which were fastidious intracellular bacteria, were found in two blood and valve culture-negative cases.Conclusions: mNGS outperformed the conventional culture method and displayed high accuracy in detecting pathogens in IE patients. This study provided support for the use of mNGS in the etiological diagnosis of IE.

Cardiomyopathy is a group of disease characterized by structural and functional damage to the myocardium. The etiologies of cardiomyopathies are diverse, spanning from genetic mutations impacting fundamental myocardial functions to systemic disorders that result in widespread cardiac damage. Many specific gene mutations cause primary cardiomyopathy. Environmental factors and metabolic disorders may also lead to the occurrence of cardiomyopathy. This review provides an in‐depth analysis of the current understanding of the pathogenesis of various cardiomyopathies, highlighting the molecular and cellular mechanisms that contribute to their development and progression. The current therapeutic interventions for cardiomyopathies range from pharmacological interventions to mechanical support and heart transplantation. Gene therapy and cell therapy, propelled by ongoing advancements in overarching strategies and methodologies, has also emerged as a pivotal clinical intervention for a variety of diseases. The increasing number of causal gene of cardiomyopathies have been identified in recent studies. Therefore, gene therapy targeting causal genes holds promise in offering therapeutic advantages to individuals diagnosed with cardiomyopathies. Acting as a more precise approach to gene therapy, they are gradually emerging as a substitute for traditional gene therapy. This article reviews pathogenesis and therapeutic interventions for different cardiomyopathies.

Background: Previous models for predicting delirium after cardiac surgery remained inadequate. This study aimed to develop and validate a machine learning-based prediction model for postoperative delirium (POD) in cardiac valve surgery patients. Methods: The electronic medical information of the cardiac surgical intensive care unit (CSICU) was extracted from a tertiary and major referral hospital in southern China over 1 year, from June 2019 to June 2020. A total of 507 patients admitted to the CSICU after cardiac valve surgery were included in this study. Seven classical machine learning algorithms (Random Forest Classifier, Logistic Regression, Support Vector Machine Classifier, K-nearest Neighbors Classifier, Gaussian Naive Bayes, Gradient Boosting Decision Tree, and Perceptron.) were used to develop delirium prediction models under full (q = 31) and selected (q = 19) feature sets, respectively. Result: The Random Forest classifier performs exceptionally well in both feature datasets, with an Area Under the Curve (AUC) of 0.92 for the full feature dataset and an AUC of 0.86 for the selected feature dataset. Additionally, it achieves a relatively lower Expected Calibration Error (ECE) and the highest Average Precision (AP), with an AP of 0.80 for the full feature dataset and an AP of 0.73 for the selected feature dataset. To further evaluate the best-performing Random Forest classifier, SHAP (Shapley Additive Explanations) was used, and the importance matrix plot, scatter plots, and summary plots were generated. Conclusions: We established machine learning-based prediction models to predict POD in patients undergoing cardiac valve surgery. The random forest model has the best predictive performance in prediction and can help improve the prognosis of patients with POD.

Background: Gastrointestinal bleeding (GIB) is a notable complication in patients diagnosed with aortic dissection (AD). We evaluated the outcomes and identified the risk factors associated with GIB in patients with AD. Methods: A retrospective case-control study was conducted on patients diagnosed with type A aortic dissection (TAAD) who underwent total aortic arch replacement (TAAR) at our institution from July 2021 to July 2023. Comprehensive clinical data, laboratory findings, and imaging results were meticulously gathered and analyzed to identify potential risk factors linked to GIB in this patient cohort. Results: Of the 198 AD patients who underwent TAAR, 38 (19.2%) developed postoperative GIB (GIB group), with a median interval of 7 days between surgery and bleeding onset. The GIB group exhibited significantly higher mortality (26.3% vs. 3.1%, P<0.001), prolonged intensive care unit (ICU) stay {15 [interquartile range (IQR), 8–25] vs. 7 (IQR, 5–12) days, P<0.001}, and extended duration of ventilation [168 (IQR, 120–372) vs. 71 (IQR, 34–148) hours, P54 years [odds ratio (OR): 3.529], intraoperative red blood cell (RBC) transfusion >600 mL (OR: 3.865), and concomitant celiac trunk and superior mesenteric artery (SMA) hypoperfusion (OR: 15.974) as independent risk factors for GIB in AD patients. Conclusions: GIB subsequent to TAAR in AD patients is linked to adverse prognosis. Factors such as advanced age, extensive intraoperative transfusion, and gastrointestinal (GI) perfusion abnormalities may heighten the risk of GIB in this patient population.

Marfan syndrome (MFS) is an inherited autosomal dominant disorder that affects connective tissue with an incidence of about 1 in 5,000 to 10,000 people. 90% of MFS is caused by mutations in the fibrillin-1 (FBN1) gene. We recruited a family with MFS phenotype in South China and identified a novel variant. This study investigated whether this genetic variant is pathogenic and the potential pathway related to lipid metabolism in MFS. A three-generation consanguineous family was recruited for this study. Whole exome sequencing (WES) was utilized on family members. The 3D structure of the protein was predicted using AlphaFold. CRISPR/Cas9 was applied to generate a similar fbn1 nonsense mutation (fbn1+/−) in zebrafish. RNA-seq analysis on zebrafish was performed to identify potential pathways related to MFS pathogenesis. Our study identified a novel variant [NM_000138.5; c.7764 C > G: p.(Y2588*)] in FBN1 gene from the family and identified the same site mutation among the proband along with her son and daughter. Structural modeling showed the p.Y2588* mutation resulted from a truncated protein. Compared to wild-type zebrafish, the F2 generation fbn1+/− zebrafish exhibited MFS phenotype. RNA-seq analysis indicated that many genes related to leptin are up-regulating, which could affect bone development and adipose homeostasis. A novel variant was identified in FBN1 gene. In a zebrafish model, we found functional evidence supporting the pathogenicity of the detected nonsense mutation. Our research proposes a possible mechanism underlying the relationship between lipid metabolism and MFS. These findings can help improve the clinical diagnosis and treatment of MFS.