Mid-term clinical outcomes of totally endoscopic repair for mitral regurgitation in Barlow's disease.

OBJECTIVE: This study aimed to confirm the safety and feasibility of totally endoscopic repair for mitral regurgitation (MR) in Barlow's disease. METHODS: From June 2018 to December 2022, 21 consecutive Barlow's disease patients (aged 33 ± 12 years; 57.1% male) underwent totally endoscopic mitral valve (MV) repair with leaflets folding, multiple artificial chordae implantation and ring annuloplasty. The safety and feasibility of this technique was evaluated by its mid-term clinical outcomes. RESULTS: There was no operative death or complications. The mean cardiopulmonary bypass (CPB) time was 190 ± 41 (128-267) min, and the aortic cross-clamp time was 145 ± 32 (66-200) min. The average number of artificial chordae implantation was 2.9 ± 0.7 (1-4) pairs. The mean MV coaptation length was 1.4 ± 0.3 (0.8-1.8) cm, and the median transvalvular gradient was 1 [interquartile range (IQR), 1-2] mmHg. During a median follow-up time of 24 (IQR, 10-38) months, all patients showed persistent effective valve function with no significant MR or systolic anterior motion. CONCLUSIONS: Totally endoscopic repair was a safe, effective, and reproducible procedure with satisfied mid-term clinical outcomes for MR in Barlow's disease. However, further randomized and long-term follow-up studies were warranted to determine its clinical effects.

Alkylation Repair Homolog 5 Regulates N(6)-methyladenosine (m6A) Methylation of Mitsugumin 53 to Attenuate Myocardial Infarction by Inhibiting Apoptosis and Oxidative Stress.

ABSTRACT: N(6)-methyladenosine (m6A) methylation modification is involved in the progression of myocardial infarction (MI). In this study, we investigated the effects of demethylase alkylation repair homolog 5 (ALKBH5) on cell apoptosis and oxidative stress in MI. The ischemia/reperfusion (I/R) injury mouse model and hypoxia/reoxygenation (H/R) cell model were established. The levels of ALKBH5 and mitsugumin 53 (MG53) were measured by quantitative real-time polymerase chain reaction, immunohistochemical, and immunofluorescence analysis. Apoptosis was evaluated by TUNEL assay, flow cytometry, and western blot. Oxidative stress was assessed by antioxidant index kits. Methylation was analyzed by RNA binding protein immunoprecipitation (RIP), MeRIP, and dual-luciferase reporter assay. We observed that ALKBH5 and MG53 were highly expressed in MI. Overexpression of ALKBH5 inhibited H/R-induced cardiomyocyte apoptosis and oxidative stress in vitro, and it inhibited I/R-induced collagen deposition, cardiac function, and apoptosis in vivo. ALKBH5 could bind to MG53, inhibit m6A methylation of MG53, and increase its mRNA stability. Silencing of MG53 counteracted the inhibition of apoptosis and oxidative stress induced by ALKBH5. In conclusion, ALKBH5 suppressed m6A methylation of MG53 and inhibited MG53 degradation to inhibit apoptosis and oxidative stress of cardiomyocytes, thereby attenuating MI. The results provided a theoretical basis that ALKBH5 is a potential target for MI treatment.

Application of Machine Learning Algorithms to Predict New-Onset Postoperative Atrial Fibrillation and Identify Risk Factors Following Isolated Valve Surgery.

BACKGROUND: New-onset postoperative atrial fibrillation (POAF) is the most common complication after valvular surgery, but its etiology and risk factors are incompletely understood. This study investigates the benefits of machine learning methods in risk prediction and in identifying relative perioperative variables for POAF after valve surgery. METHODS: This retrospective study involved 847 patients, who underwent isolated valve surgery from January 2018 to September 2021 in our institution. We used machine learning algorithms to predict new-onset postoperative atrial fibrillation and to select relatively important variables from a set of 123 preoperative characteristics and intraoperative information. RESULTS: The support vector machine (SVM) model demonstrated the best area under the receiver operating characteristic (AUC) value of 0.786, followed by logistic regression (AUC = 0.745) and the Complement Naive Bayes (CNB) model (AUC = 0.672). Left atrium diameter, age, estimated glomerular filtration rate (eGFR), duration of cardiopulmonary bypass, New York Heart Association (NYHA) class III-IV, and preoperative hemoglobin were high-ranked variables. CONCLUSIONS: Risk models based on machine learning algorithms may be superior to traditional models, which were primarily based on logistic algorithms to predict the occurrence of POAF after valve surgery. Further prospective multicenter studies are needed to confirm the performance of SVM in predicting POAF.

Prediction of new onset postoperative atrial fibrillation using a simple Nomogram.

BACKGROUND: New onset postoperative atrial fibrillation (POAF) is the most common complication of cardiac surgery, with an incidence ranging from 15 to 50%. This study aimed to develop a new nomogram to predict POAF using preoperative and intraoperative risk factors. METHODS: We retrospectively analyzed the data of 2108 consecutive adult patients (> 18 years old) who underwent cardiac surgery at our medical institution. The types of surgery included isolated coronary artery bypass grafting, valve surgery, combined valve and coronary artery bypass grafting (CABG), or aortic surgery. Logistic regression or machine learning methods were applied to predict POAF incidence from a subset of 123 parameters. We also developed a simple nomogram based on the strength of the results and compared its predictive ability with that of the CHA2DS2-VASc and POAF scores currently used in clinical practice. RESULTS: POAF was observed in 414 hospitalized patients. Logistic regression provided the highest area under the receiver operating characteristic curve (ROC) in the validation cohort. A simple bedside tool comprising three variables (age, left atrial diameter, and surgery type) was established, which had a discriminative ability with a ROC of 0.726 (95% CI 0.693-0.759) and 0.727 (95% CI 0.676-0.778) in derivation and validation subsets respectively. The calibration curve of the new model was relatively well-fit (p = 0.502). CONCLUSIONS: Logistic regression performed better than machine learning in predicting POAF. We developed a nomogram that may assist clinicians in identifying individuals who are prone to POAF.

A Model of Long-Term Ventricular Fibrillation in Isolated Rat Hearts.

Ventricular fibrillation (VF) is a fatal arrhythmia with a high incidence in cardiac patients, but VF arrest under perfusion is a neglected method of intraoperative arrest in the field of cardiac surgery. With recent advances in cardiac surgery, the demand for prolonged VF studies under perfusion has increased. However, the field lacks simple, reliable, and reproducible animal models of chronic ventricular fibrillation. This protocol induces long-term VF through alternating current (AC) electrical stimulation of the epicardium. Different conditions were used to induce VF, including continuous stimulation with a low or high voltage to induce long-term VF and stimulation for 5 min with a low or high voltage to induce spontaneous long-term VF. The success rates of the different conditions, as well as the rates of myocardial injury and recovery of cardiac function, were compared. The results showed that continuous low-voltage stimulation induced long-term VF and that 5 min of low-voltage stimulation induced spontaneous long-term VF with mild myocardial injury and a high rate of recovery of cardiac function. However, the low-voltage, continuously stimulated long-term VF model had a higher success rate. High-voltage stimulation provided a higher rate of VF induction but showed a low defibrillation success rate, poor recovery of cardiac function, and severe myocardial injury. On the basis of these results, continuous low-voltage epicardial AC stimulation is recommended for its high success rate, stability, reliability, reproducibility, low impact on cardiac function, and mild myocardial injury.

Robotic-assisted laparoscopic and thoracoscopic approach: a challenging multidisciplinary minimally invasive surgery of intravascular leiomyomatosis with intracardiac extension.

OBJECTIVE: To show a case of severe intravascular leiomyomatosis with intracardiac extension treated by a multidisciplinary minimally invasive surgery. DESIGN: Stepwise demonstration of the technique with a video. SETTING: General Hospital. PATIENT(S): A 40-year-old woman with palpitation and dyspnea. INTERVENTION(S): The patient was diagnosed with intravascular leiomyomatosis by computed tomography scan. She underwent a successful single-stage minimally invasive surgery with complete excision. MAIN OUTCOME MEASURE(S): The feasibility and safety of using this technique for intravascular leiomyomatosis with intracardiac extension. RESULT(S): A combined thoracoabdominal surgery was successfully performed. During the procedure, cardiopulmonary bypass was maintained for 72 minutes. The patient soon recovered and was discharged. CONCLUSION(S): Minimally invasive surgery is a possible choice for intravascular leiomyomatosis with intracardiac extension.

Clinical effect of total thoracoscopic mitral valve surgery combined with atrial fibrillation radiofrequency ablation in patients with different left atrium diameter-a single center prospective observational cohort study.

Background: Mitral valve surgery combined with atrial fibrillation (AF) radiofrequency ablation (AFRA) is recommended as the first-line strategy for valvular disease-related AF. There are relatively few reports on the effect of AFRA combined with total thoracoscopic mitral valve surgery. This article aimed to analyze the clinical data and prognostic outcomes of patients with diverse left atrium diameter (LAD) (>50 or ≤50 mm) who underwent total thoracoscopic mitral valve surgery combined with AFRA. Methods: We conducted a prospective analysis of patients who underwent AFRA from January 2021 to June 2022 in the Department of Cardiovascular Surgery at the Chinese PLA General Hospital. The inclusion criteria were: (I) aged 40-70 years; (II) diagnosed with valvular heart disease and concomitant long-term persistent AF (>1 year); (III) patients who underwent total thoracoscopic mitral valve surgery; (IV) with a left ventricular end-diastolic diameter of ≤70 mm; (V) with a LAD ≤65 mm; and (VI) left ventricular ejection fraction (LVEF) ≥50%. The included patients were assigned to group A and B depend up on the LAD. All patients were followed up at 3 and 6 months timepoints postoperatively. The prime endpoint was the recovery rate of sinus rhythm. Results: There were 24 cases in group A (LAD >50 mm) and 16 cases in group B (LAD ≤50 mm). The two groups exhibited no statistical differences in terms of age, gender proportion, preoperative comorbidities, AF duration, preoperative heart function, and type of valve disease (P>0.05). The LAD, pulmonary artery pressure, and left ventricular diameter of group A were significantly greater than those of group B (P<0.05). There were no new cerebrovascular incidents during the perioperative and follow-up periods. The sinus rhythm conversion rates in group A after surgery and at 6 months were 75% and 66.7%, respectively; meanwhile, both of these values were 87.5% in group B, and the difference between the groups was statistically significant (P<0.05). Conclusions: Total thoracoscopic mitral valve surgery with AFRA is more effective in maintaining sinus rhythm in patients with LAD ≤50 mm than in those with LAD >50 mm without increased risk of adverse events. Further studies are warranted to validate our findings.

Transcriptomic responses of Saccharum spontaneum roots in response to polyethylene glycol - 6000 stimulated drought stress.

Drought is the abiotic factor that adversely affects plant growth, development survival, and crop productivity, posing a substantial threat to sustainable agriculture worldwide, especially in warm and dry areas. However, the extent of damage depends upon the crop growth stage, severity and frequency of the stress. In general, the reproductive growth phase is more sensitive to stresses causing a substantial loss in crop productivity. Saccharum spontaneum (L.) is the most variable wild relative of sugarcane with potential for use in sugarcane crop improvement programs. In the present study addresses the transcriptomic analysis of drought stress imposed by polyethylene glycol-6000 (PED-6000; w/v- 25%) on the root tip tissues of S. spontaneum GX83-10. The analysis of microarrays of drought-stressed roots was performed at 0 (CK), 2 (T2), 4 (T4), 8 (T8) and 24 h (T24). The analyzed data were compared with the gene function annotations of four major databases, such as Nr, KOG/COG, Swiss-Prot, and KEGG, and a total of 62,988 single-gene information was obtained. The differently expressed genes of 56237 (T4), 59319 (T8), and 58583 (T24), among which CK obtained the most significant number of expressed genes (35920) as compared to T24, with a total of 53683 trend genes. Gene ontology (GO) and KEGG analysis were performed on the 6 important trends, and a total of 598 significant GO IDs and 42 significantly enriched metabolic pathways. Furthermore, these findings also aid in the selection of novel genes and promoters that can be used to potentially produce crop plants with enhanced stress resistance efficiency for sustainable agriculture.

Sensing Gas Mixtures by Analyzing the Spatiotemporal Optical Responses of Liquid Crystals Using 3D Convolutional Neural Networks.

We report how analysis of the spatial and temporal optical responses of liquid crystal (LC) films to targeted gases, when performed using a machine learning methodology, can advance the sensing of gas mixtures and provide important insights into the physical processes that underlie the sensor response. We develop the methodology using O3 and Cl2 mixtures (representative of an important class of analytes) and LCs supported on metal perchlorate-decorated surfaces as a model system. Although O3 and Cl2 both diffuse through LC films and undergo redox reactions with the supporting metal perchlorate surfaces to generate similar initial and final optical states of the LCs, we show that a three-dimensional convolutional neural network can extract feature information that is encoded in the spatiotemporal color patterns of the LCs to detect the presence of both O3 and Cl2 species in mixtures and to quantify their concentrations. Our analysis reveals that O3 detection is driven by the transition time over which the brightness of the LC changes, while Cl2 detection is driven by color fluctuations that develop late in the optical response of the LC. We also show that we can detect the presence of Cl2 even when the concentration of O3 is orders of magnitude greater than the Cl2 concentration. The proposed methodology is generalizable to a wide range of analytes, reactive surfaces, and LCs and has the potential to advance the design of portable LC monitoring devices (e.g., wearable devices) for analyzing gas mixtures using spatiotemporal color fluctuations.

SERPINE1 Gene Is a Reliable Molecular Marker for the Early Diagnosis of Aortic Dissection.

With the acceleration of population aging, the detection rate of aortic dissection has increased. The incidence rate of aortic dissection has increased year by year and has become a serious threat to human health. However, the current clinical treatment of aortic dissection is mainly limited to surgery (including intracavity), but the complexity of the disease and the high risk of surgery seriously affect the overall treatment effect of the disease. Therefore, an in-depth study of the pathogenesis of aortic dissection and the development of early diagnosis methods is not only expected to control the development of aortic dissection but also to improve the existing clinical treatment effect. Based on the bioinformatics analysis of the related mRNA sequence data of aortic dissection in GEO database, the gene expression regulatory network of aortic dissection was constructed. Through the screening of key node genes, the key factors (molecular markers) that may affect the occurrence of aortic dissection were obtained, and their functions were tested in human aortic smooth muscle cells (HAoSMC). Finally, it was concluded that SERPINE1 gene is a reliable molecular marker for the early diagnosis of aortic dissection.

Human umbilical cord-derived stem cell sheets improve left ventricular function in rat models of ischemic heart failure.

INTRODUCTION: Human umbilical cord-derived mesenchymal stem cells (UC-MSCs) are among the most promising cell therapy sources used to treat ischemic heart disease. Cell sheet engineering has been used to transplant stem cells and improve their therapeutic effectiveness. We aimed to evaluate the effectiveness of UC-MSC sheets in the treatment of chronic ischemic heart failure. METHODS AND RESULTS: Flow cytometric analysis showed that UC-MSCs were positive for CD73, CD90, and CD105. UC-MSC sheets were produced from UC-MSCs using temperature-responsive culture dishes. Afterward, these sheets were transplanted onto the epicardial surface at the infarct heart in rat models of chronic ischemic heart failure. At four weeks after the transplantation, echocardiography analysis revealed that the cardiac function of the UC-MSC sheets group was significantly better than that of the suspension and myocardial infarction (MI) only groups. Furthermore, histological examinations revealed that the left ventricular remodeling was attenuated compared with the suspension and MI-only groups. In the UC-MSC slice group, the neovascular den and cell size in the infarct margin region were was significantly improved than in the suspension and MI-only groups. Also, the UC-MSC sheets inhibited the PI3K/AKT/mTOR signaling pathway in chronic ischemic heart failure. CONCLUSIONS: UC-MSC sheets can maintain cardiac function and attenuate ventricular remodeling in chronic ischemic heart failure, indicating that this strategy would be a promising therapeutic option in the clinical scenario.

Bioinformatic-based Identification of Genes Associated with Aortic Valve Stenosis.

BACKGROUND: Aortic valve stenosis (AS) disease is the most common valvular disease in developed countries. The pathology of AS is complex, and its main processes include calcification of the valve stroma and involve genetic factors, lipoprotein deposition and oxidation, chronic inflammation, osteogenic transition of cardiac valve interstitial cells, and active valve calcification. The aim of this study was to identify potential genes associated with AS. METHODS: Three original gene expression profiles (GSE153555, GSE12644, and GSE51472) were downloaded from the Gene Expression Omnibus (GEO) database and analyzed by GEO2R tool or 'limma' in R to identify differentially expressed genes (DEGs). Functional enrichment was analyzed using the ClusterProfiler package in R Bioconductor. STRING was utilized for the Protein-Protein Interaction (PPI) Network construct, and tissue-specific gene expression were identified using BioGPS database. The hub genes were screened out using the Cytoscape software. Related miRNAs were predicted in Targetscan, miWalk, miRDB, Hoctar, and TarBase. RESULTS: A total of 58 upregulated genes and 20 downregulated genes were screened out, which were mostly enriched in matrix remodeling and the immune system process. A module was thus clustered into by PPI network analysis, which mainly involved in Fc gamma R-mediated phagocytosis, Osteoclast differentiation. Ten genes (IBSP, NCAM1, MMP9, FCGR3B, COL4A3, FCGR1A, THY1, RUNX2, ITGA4, and COL10A1) with the highest degree scores were subsequently identified as the hub genes for AS by applying the CytoHubba plugin. And hsa-miR-1276 was finally identified as potential miRNA and miRNA-gene regulatory network was constructed using NetworkAnalyst. CONCLUSIONS: Our analysis suggested that IBSP, NCAM1, MMP9, FCGR3B, COL4A3, FCGR1A, THY1, RUNX2, ITGA4, and COL10A1 might be hub genes associated with AS, and hsa-miR-1276 was potential miRNA. This result could provide novel insight into pathology and therapy of AS in the future.

Development of machine learning models for mortality risk prediction after cardiac surgery.

Background: We developed machine learning models that combine preoperative and intraoperative risk factors to predict mortality after cardiac surgery. Methods: Machine learning involving random forest, neural network, support vector machine, and gradient boosting machine was developed and compared with the risk scores of EuroSCORE I and II, Society of Thoracic Surgeons (STS), as well as a logistic regression model. Clinical data were collected from patients undergoing adult cardiac surgery at the First Medical Centre of Chinese PLA General Hospital between December 2008 and December 2017. The primary outcome was post-operative mortality. Model performance was estimated using several metrics, including sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC). The visualization algorithm was implemented using Shapley's additive explanations. Results: A total of 5,443 patients were enrolled during the study period. The mean EuroSCORE II score was 3.7%, and the actual in-hospital mortality rate was 2.7%. For predicting operative mortality after cardiac surgery, the AUC scores were 0.87, 0.79, 0.81, and 0.82 for random forest, neural network, support vector machine, and gradient boosting machine, compared with 0.70, 0.73, 0.71, and 0.74 for EuroSCORE I and II, STS, and logistic regression model. Shapley's additive explanations analysis of random forest yielded the top-20 predictors and individual-level explanations for each prediction. Conclusions: Machine learning models based on available clinical data may be superior to clinical scoring tools in predicting postoperative mortality in patients following cardiac surgery. Explanatory models show the potential to provide personalized risk profiles for individuals by accounting for the contribution of influencing factors. Additional prospective multicenter studies are warranted to confirm the clinical benefit of these machine learning-driven models.