Nomogram to predict recurrent chest pain in patients with myocardial bridging.

OBJECTIVE: Patients with myocardial bridging (MB) frequently experience recurrent chest pain, even in those without coronary heart disease. This study aims to predict the risk of recurrent chest pain in patients with MB by using a novel predictive nomogram. METHODS: This retrospective study enrolled 250 patients with acute chest pain who underwent coronary computed tomography angiography (CCTA) between January and December 2018, including 111 patients with MB and 139 control patients. Least absolute shrinkage and selection operator (LASSO) and multivariable Cox regression analyses were used to screen for significant parameters that were included to develop a novel predictive nomogram model. Receiver operating characteristic curve, calibration curve, and decision curve analyses were used to evaluate the performance and clinical utility of the nomogram. RESULTS: A predictive nomogram was constructed in 111 patients with MB, 34 of whom (30.9%) had recurrent chest pain. The significant predictors screened out by the LASSO regression included age, sex, branch type MB, and systolic compression index. The area under the curves (AUCs) for recurrent chest pain at 12, 24, and 36 months were 0.688, 0.742, and 0.729, respectively, indicating remarkable accuracy of the nomogram. The calibration curve and decision curve analyses indicated a good agreement with the observations and utility of the nomogram. CONCLUSIONS: This study presents a high-accuracy nomogram to predict recurrent chest pain in patients with MB. This model incorporates clinical risk factors and CT imaging features and can be conveniently used to facilitate the individualised prediction. KEY POINTS: • Symptomatic patients with myocardial bridging often present with recurrent chest pain. • The potential predictors of recurrent chest pain in patients with myocardial bridging were age, sex, branch type MB, and systolic compression index. • Nomogram based on clinical CT imaging features is valuable to predict recurrent chest pain in patients with myocardial bridging.

Host miRNAs-microbiota interactions in gastric cancer.

It is widely acknowledged that gastric cancer seriously affects the quality of life and survival of patients. The correlation between the microbiota and gastric cancer has attracted extensive attention in recent years, nonetheless the specific mechanism of its impact on gastric cancer remain largely unclear. Recent studies have shown that in addition to its role in the host's inflammatory and immune response, the microbiota can also affect the occurrence and development of gastric cancer by affecting the expression of miRNAs. This paper brings together all currently available data on miRNAs, microbiota and gastric cancer, and preliminarily describes the relationship among them.

Polydatin ameliorates hepatic ischemia-reperfusion injury by modulating macrophage polarization.

BACKGROUND: Polydatin, a glucoside of resveratrol, has been shown to have protective effects against various diseases. However, little is known about its effect on hepatic ischemia-reperfusion (I/R) injury. This study aimed to elucidate whether polydatin protects liver against I/R-induced injury and to explore the underlying mechanism. METHODS: After gavage feeding polydatin once daily for a week, mice underwent a partial hepatic I/R procedure. Serum alanine aminotransferase (ALT)/aspartate aminotransferase (AST), hematoxylin-eosin (H&E) and TdT-mediated dUTP nick-end labeling (TUNEL) staining were used to evaluate liver injury. The severity related to the inflammatory response and reactive oxygen species (ROS) production was also investigated. Furthermore, immunofluorescence and Western blotting were used to detect macrophage polarization and the NF-κB signaling pathway in macrophages. RESULTS: Compared with the I/R group, polydatin pretreatment significantly attenuated I/R-induced liver damage and apoptosis. The oxidative stress marker (dihydroethidium fluorescence, malondialdehyde, superoxide dismutase and glutathione peroxidase) and I/R related inflammatory cytokines (interleukin-1ß, interleukin-10 and tumor necrosis factor-α) were significantly suppressed after polydatin treatment. In addition, the result of immunofluorescence indicated that polydatin reduced the polarization of macrophages toward M1 macrophages both in vivo and in vitro. Western blotting showed that polydatin inhibited the pro-inflammatory function of RAW264.7 via down-regulating the NF-κB signaling pathway. CONCLUSIONS: Polydatin protects the liver from I/R injury by remodeling macrophage polarization via NF-κB signaling.

Reduced plasma level of basic fibroblast growth factor is associated with incomplete device endothelialization at six months following left atrial appendage closure.

OBJECTIVES: To investigate whether inflammatory and growth factors (IGFs) were associated with incomplete device endothelialization (IDE) at 6 months after successful left atrial appendage closure (LAAC). BACKGROUND: IDE after LAAC is correlated with device-related thrombus (DRT) formation and subsequent thromboembolic events. However, biomarkers for early detection of IDE remain lacking. METHODS: Plasma levels of IGFs including basic fibroblast growth factor (bFGF), platelet derived growth factor (PDGF), stromal cell derived factor (SDF)-1a, transforming growth factor (TGF)-ß1, vascular growth factor receptor-1 (VEGF-R1) and von Willebrand factor (vWF) were determined using ELISA kits in 55 consecutive patients with atrial fibrillation (AF) at 6 months after LAAC with Watchman devices. The status of device endothelialization was assessed by transesophageal echocardiography and cardiac CT. RESULTS: IDE and complete device endothelialization(CDE)were detected in 38 and 17 patients, respectively. Among the six IGFs, only plasma level of bFGF was significantly lower in patients with IDE compared to those with CDE (303.49 ± 246.84 vs. 556.31 ± 197.84 pg/ml, p < 0.001). C-statistics of plasma bFGF for discriminating patients with IDE from those with CDE was 0.785 (95 % CI: 0.663-0.907, p < 0.001), with a cut-off value of 440.52pg/ml (sensitivity 0.765; specificity 0.789). Multivariate logistic regression model showed that lower bFGF was an independent factor for IDE (OR: 11.752, 95 % CI: 2.869-48.144, P = 0.001). bFGF improved the classification of patients (NRI: 0.677,95 % CI: 0.320-1.033, p = 0.004). CONCLUSIONS: Reduced plasma bFGF level confers an increased risk for IDE after LAAC. Further prospective studies are warranted to examine if bFGF could serve as a biomarker for IDE post LAAC.

Influence of malformation of right coronary artery originating from the left sinus in hemodynamic environment.

BACKGROUND: The anomalous origin of the right coronary artery (RCA) from the left coronary artery sinus (AORL) is one of the abnormal origins of the coronary arteries. Most of these issues rarely have any effects on human health, but some individuals may exhibit symptoms, such as myocardial ischemia or even sudden death. Recently, researchers have investigated the AORL through clinical cases, but studies based on computational fluid dynamics (CFD) have rarely been reported. In this study, the hemodynamic changes between the normal origin of the RCA and the AORL are compared based on numerical simulation results. METHODS: Realistic three-dimensional (3D) models of the 16 normal right coronary arteries and 26 abnormal origins of the RCAs were constructed, respectively. The blood flow was numerically simulated using the ANSYS software. This study used a one-way fluid-solid coupling finite element model, wherein the blood is assumed to be an incompressible Newtonian fluid, and the vessel is assumed to be made of an isotropic linear elastic material. RESULTS: The cross-sectional area differences between the inlet of the normal group and that of the abnormal group were significant (P < 0.0001). Moreover, there were significant differences in the volumetric flow (P = 0.0001) and pressure (P = 0.0002). Positive correlation exists for the ratio of the cross-sectional area of the RCA to the inlet area of the ascending aorta (AAO), and the ratio of the inlet volumetric flow of the RCA to the volumetric flow of the AAO, in the normal (P = 0.0001, r = 0.8178) and abnormal (P = 0.0033, r = 0.6107) groups. CONCLUSION: This study demonstrates that the cross-sectional area of the AORL inlet may cause ischemia symptoms. The results obtained by this study may contribute to the further understanding of the clinical symptoms of the AORL based on the hemodynamics.

A velocity-based impedance control system for a low impact docking mechanism (LIDM).

In this paper, an impedance control algorithm based on velocity for capturing two low impact docking mechanisms (LIDMs) is presented. The main idea of this algorithm is to track desired forces when the position errors of two LIDMs are random by designing the relationship between the velocity and contact forces measured by a load sensing ring to achieve low impact docking. In this paper, the governing equation of an impedance controller between the deviation of forces and velocity is derived, and simulations are designed to verify how impedance parameters affect the control characteristics. The performance of the presented control algorithm is validated by using the MATLAB and ADAMS software for capturing simulations. The results of capturing simulations demonstrate that the impedance control algorithm can respond fast and has excellent robustness when the environmental errors are random, and the contact forces and torques satisfy the low impact requirements.

Revolutionary multi-omics analysis revealing prognostic signature of thyroid cancer and subsequent in vitro validation of SNAI1 in mediating thyroid cancer progression through EMT.

Thyroid carcinoma (TC), the most commonly diagnosed malignancy of the endocrine system, has witnessed a significant rise in incidence over the past few decades. The integration of scRNA-seq with other sequencing approaches offers researchers a distinct perspective to explore mechanisms underlying TC progression. Therefore, it is crucial to develop a prognostic model for TC patients by utilizing a multi-omics approach. We acquired and processed transcriptomic data from the TCGA-THCA dataset, including mRNA expression profiles, lncRNA expression profiles, miRNA expression profiles, methylation chip data, gene mutation data, and clinical data. We constructed a tumor-related risk model using machine learning methods and developed a consensus machine learning-driven signature (CMLS) for accurate and stable prediction of TC patient outcomes. 2 strains of undifferentiated TC cell lines and 1 strain of PTC cell line were utilized for in vitro validation. mRNA, protein levels of hub genes, epithelial-mesenchymal transition (EMT)-associated phenotypes were detected by a series of in vitro experiments. We identified 3 molecular subtypes of TC based on integrated multi-omics clustering algorithms, which were associated with overall survival and displayed distinct molecular features. We developed a CMLS based on 28 hub genes to predict patient outcomes, and demonstrated that CMLS outperformed other prognostic models. TC patients of relatively lower CMLS score had significantly higher levels of T cells, B cells, and macrophages, indicating an immune-activated state. Fibroblasts were predominantly enriched in the high CMLS group, along with markers associated with immune suppression and evasion. We identified several drugs that could be suitable for patients with high CMLS, including Staurosporine_1034, Rapamycin_1084, gemcitabine, and topotecan. SNAI1 was elevated in both undifferentiated TC cell lines, comparing to PTC cells. Knockdown of SNAI1 reduced the cell proliferation and EMT phenotypes of undifferentiated TC cells. Our findings highlight the importance of multi-omics analysis in understanding the molecular subtypes and immune characteristics of TC, and provide a novel prognostic model and potential therapeutic targets for this disease. Moreover, we identified SNAI1 in mediating TC progression through EMT in vitro.

An Enhanced IDBO-CNN-BiLSTM Model for Sentiment Analysis of Natural Disaster Tweets.

The Internet's development has prompted social media to become an essential channel for disseminating disaster-related information. Increasing the accuracy of emotional polarity recognition in tweets is conducive to the government or rescue organizations understanding the public's demands and responding appropriately. Existing sentiment analysis models have some limitations of applicability. Therefore, this research proposes an IDBO-CNN-BiLSTM model combining the swarm intelligence optimization algorithm and deep learning methods. First, the Dung Beetle Optimization (DBO) algorithm is improved by adopting the Latin hypercube sampling, integrating the Osprey Optimization Algorithm (OOA), and introducing an adaptive Gaussian-Cauchy mixture mutation disturbance. The improved DBO (IDBO) algorithm is then utilized to optimize the Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) model's hyperparameters. Finally, the IDBO-CNN-BiLSTM model is constructed to classify the emotional tendencies of tweets associated with the Hurricane Harvey event. The empirical analysis indicates that the proposed model achieves an accuracy of 0.8033, outperforming other single and hybrid models. In contrast with the GWO, WOA, and DBO algorithms, the accuracy is enhanced by 2.89%, 2.82%, and 2.72%, respectively. This study proves that the IDBO-CNN-BiLSTM model can be applied to assist emergency decision-making in natural disasters.

Brain Network for Exploring the Change of Brain Neurotransmitter 5-Hydroxytryptamine of Autism Children by Resting-State EEG.

The study was aimed at understanding the brain network and the change rule of brain neurotransmitter 5-hydroxytryptamine (5-HT) in autism children through resting-state electroencephalogram (EEG). 20 autistic children in hospital were selected and defined as the observation group. Meanwhile, 20 healthy children were defined as the control group. EEG signals were collected for the two groups. Fuzzy C-means (FCM) algorithm was used to extract features of EEG signals, and DTF was applied for the causal association between multichannel EEG signals. The two groups were compared for the average function value and regional efficiency of the brain neurotransmitter 5-HT. The results showed that the classification accuracy of frontal F7 channel, left frontal FP1 channel, and temporal T6 channel was 95.2%, 95.3%, and 91.2%, respectively. The average of high beta frequency band, low beta frequency band, theta frequency band, and alpha frequency band in the control group was significantly higher than that in the observation group under the optimal threshold (P < 0.05). Compared with normal subjects (34.27), the average function of 5-HT in the brain was 20.13 in patients with low function and 45.74 in patients with hyperfunction. In conclusion, FCM algorithm can feature extraction of EEG signals, especially in the frontal F7 channel, the left frontal FP1 channel, and the TEMPORAL T6 channel, which has high classification accuracy and can well express the EEG signals of autistic children. The level of 5-HT in autistic children is lower than that in healthy people, and it is closely related to loneliness and depression.

Identification of Tumor Mutation Burden, Microsatellite Instability, and Somatic Copy Number Alteration Derived Nine Gene Signatures to Predict Clinical Outcomes in STAD.

Genomic features, including tumor mutation burden (TMB), microsatellite instability (MSI), and somatic copy number alteration (SCNA), had been demonstrated to be involved with the tumor microenvironment (TME) and outcome of gastric cancer (GC). We obtained profiles of TMB, MSI, and SCNA by processing 405 GC data from The Cancer Genome Atlas (TCGA) and then conducted a comprehensive analysis though "iClusterPlus." A total of two subgroups were generated, with distinguished prognosis, somatic mutation burden, copy number changes, and immune landscape. We revealed that Cluster1 was marked by a better prognosis, accompanied by higher TMB, MSIsensor score, TMEscore, and lower SCNA burden. Based on these clusters, we screened 196 differentially expressed genes (DEGs), which were subsequently projected into univariate Cox survival analysis. We constructed a 9-gene immune risk score (IRS) model using LASSO-penalized logistic regression. Moreover, the prognostic prediction of IRS was verified by receiver operating characteristic (ROC) curve analysis and nomogram plot. Another independent Gene Expression Omnibus (GEO) contained specimens from 109 GC patients was designed as an external validation. Our works suggested that the 9-gene-signature prediction model, which was derived from TMB, MSI, and SCNA, was a promising predictive tool for clinical outcomes in GC patients. This novel methodology may help clinicians uncover the underlying mechanisms and guide future treatment strategies.