Insulin resistance and coronary inflammation in patients with coronary artery disease: a cross-sectional study.

BACKGROUND: Insulin resistance (IR) is associated with coronary artery disease (CAD) severity. However, its underlying mechanisms are not fully understood. Therefore, our study aimed to explore the relationship between IR and coronary inflammation and investigate the synergistic and mediating effects of coronary inflammation on the association between IR and CAD severity. METHODS: Consecutive patients with CAD who underwent coronary angiography and coronary computed tomography angiography between April 2018 and March 2023 were enrolled. The triglyceride-glucose index (TyG index) and peri-coronary adipose tissue (PCAT) attenuation around the proximal right coronary artery (RCA) were used to evaluate IR and coronary inflammation, respectively. The correlation between the TyG index and PCAT attenuation was analyzed using linear regression models. Logistic regression models were further used for investigating the correlation of the TyG index and PCAT attenuation with CAD severity. A mediation analysis assessed the correlation between IR and CAD severity mediated by coronary inflammation. RESULTS: A total of 569 participants (mean age, 62 ± 11 years; 67.8% men) were included in the study. PCAT attenuation was positively associated with the TyG index (r = 0.166; P < 0.001). After adjusting for potential confounders, the per standard deviation increment in the TyG index was associated with a 1.791 Hounsfield unit (HU) increase (95% confidence interval [CI], 0.920-2.662 HU; P < 0.001) in the PCAT attenuation. In total, 382 (67.1%) patients had multivessel CAD. The patients in the high-TyG index/high PCAT attenuation group had approximately 3.2 times the odds of multivessel CAD compared with those in the low-TyG index/low PCAT attenuation group (odds ratio, 3.199; 95%CI, 1.826-5.607; P < 0.001). Mediation analysis indicated that PCAT attenuation mediated 31.66% of the correlation between the TyG index and multivessel CAD. CONCLUSIONS: The TyG index positively correlated with PCAT attenuation in patients with CAD. The TyG index and PCAT attenuation showed a synergistic correlation with multivessel CAD. Furthermore, PCAT attenuation partially mediated the relationship between the TyG index and CAD severity. Controlling inflammation in patients with high IR and coronary inflammation may provide additional benefits.

A co-assembly process for high strength and injectable dual network gels with sustained doxorubicin release performance.

Adopting a non-covalent co-assembly strategy shows great potential in loading drugs efficiently and safely in drug delivery systems. However, finding an efficient method for developing high strength gels with thixotropic characteristics is still challenging. In this work, by hybridizing the low molecular weight gelator fluorenylmethyloxycarbonyl-phenylalanine (Fmoc-F) (first single network, 1st SN) and alginate (second single network, 2nd SN) into a dual network (DN) gel, gels with high strength as well as thixotropy were prepared efficiently. The DN gels showed high strength (103 Pa in SN gels and 105 Pa in DN gels) and thixotropic characteristics (yield strain <25%; recovery ratio >85% within 100 seconds). The application performance was verified by loading doxorubicin (DOX), showing better encapsulation capacity (77.06% in 1st SN, 59.11% in 2nd SN and 96.71% in DN) and sustained release performance (lasting one week under physiological conditions) than single network gels. Experimental and DFT results allowed the elaboration of the specific non-covalent co-assembly mechanism for DN gel formation and DOX loading. The DN gels were formed by co-assembly driven by H-bond and π-π stacking interactions and then strengthened by Ca2+-coupling. Most DOX molecules co-assembled with Fmoc-F and alginate through π-π stacking and H-bond interactions (DOX-I), with a few free DOX molecules (DOX-II) left. Proven by the release dynamics test, DOX was released through a diffusion-erosion process, in an order of DOX-I first and then DOX-II. This work suggests that non-covalent co-assembly is a useful technique for effective material strengthening and drug delivery.

Automatic Modulation Classification for Underwater Acoustic Communication Signals Based on Deep Complex Networks.

Automatic modulation classification (AMC) is an important method for monitoring and identifying any underwater communication interference. Since the underwater acoustic communication scenario is full of multi-path fading and ocean ambient noise (OAN), coupled with the application of modern communication technology, which is usually susceptible to environmental influences, automatic modulation classification (AMC) becomes particularly difficult when it comes to an underwater scenario. Motivated by the deep complex networks (DCN), which have an innate ability to process complex data, we explore DCN for AMC of underwater acoustic communication signals. To integrate the signal processing method with deep learning and overcome the influences of underwater acoustic channels, we propose two complex physical signal processing layers based on DCN. The proposed layers include a deep complex matched filter (DCMF) and deep complex channel equalizer (DCCE), which are designed to remove noise and reduce the influence of multi-path fading for the received signals, respectively. Hierarchical DCN is constructed using the proposed method to achieve better performance of AMC. The influence of the real-world underwater acoustic communication scenario is taken into account; two underwater acoustic multi-path fading channels are conducted using the real-world ocean observation dataset, white Gaussian noise, and real-world OAN are used as the additive noise, respectively. Contrastive experiments show that the AMC based on DCN can achieve better performance than the traditional deep neural network based on real value (the average accuracy of the DCN is 5.3% higher than real-valued DNN). The proposed method based on DCN can effectively reduce the influence of underwater acoustic channels and improve the AMC performance in different underwater acoustic channels. The performance of the proposed method was verified on the real-world dataset. In the underwater acoustic channels, the proposed method outperforms a series of advanced AMC method.

Feature Fusion Based on Graph Convolution Network for Modulation Classification in Underwater Communication.

Automatic modulation classification (AMC) of underwater acoustic communication signals is of great significance in national defense and marine military. Accurate modulation classification methods can make great contributions to accurately grasping the parameters and characteristics of enemy communication systems. While a poor underwater acoustic channel makes it difficult to classify the modulation types correctly. Feature extraction and deep learning methods have proven to be effective methods for the modulation classification of underwater acoustic communication signals, but their performance is still limited by the complex underwater communication environment. Graph convolution networks (GCN) can learn the graph structured information of the data, making it an effective method for processing structured data. To improve the stability and robustness of AMC in underwater channels, we combined the feature extraction and deep learning methods by fusing the multi-domain features and deep features using GCN. The proposed method takes the relationships among the different multi-domain features and deep features into account. Firstly, a feature graph was built using the properties of the features. Secondly, multi-domain features were extracted from the received signals and deep features were extracted from the signals using a deep neural network. Thirdly, we constructed the input of GCN using these features and the graph. Then, the multi-domain features and deep features were fused by the GCN. Finally, we classified the modulation types using the output of GCN by way of a softmax layer. We conducted the experiments on a simulated dataset and a real-world dataset, respectively. The results show that the AMC based on GCN can achieve a significant improvement in performance compared to the current state-of-the-art methods. Our approach is robust in underwater acoustic channels.

Domination of H-Bond Interactions in the Solvent-Triggering Gelation Process.

Gels prepared with the solvent-triggering method are attractive for their easy and fast preparation; however, the role of solvents in this process remains unclear, which hinders the efficient and accurate control of desired gel properties. In this study, the role of solvents in the solvent-triggering gelation process is studied using 9-fluorenylmethoxycarbonyl (Fmoc)-protected diphenylalanine (Fmoc-FF) as the gelator. Density functional theory (DFT)-based calculations and corresponding wavefunction analyses are conducted to identify the H-bonding interaction sites between the molecules. The calculation results clearly annotate the activating role of DMF and the triggering role of H2O in the gelation process. The solvation of Fmoc-FF by DMF can activate the H-bonding sites on the peptide chain, showing a conformation reversal and higher electrostatic potentials. Then, the H-bonding between Fmoc-FF and H2O is facilitated to trigger gelation. The physical Fmoc-FF/DMF/H2O gels show easily tuned mechanical strengths (G' of 102-105 Pa), injectable potentials (general yield strain < 100%), and stable recoverability (80-98% within 100 s). The regulation of these properties depends on not only the gelator concentration but also the H-bonding interactions with solvent molecules, which have seldom been studied in detail before. By understanding the effect of solvents, low-molecular-weight gelator-based gels can be designed, prepared, and tuned efficiently for potential applications.

MicroRNA-2861 and microRNA-5115 regulates myocardial ischemia-reperfusion injury through the GPR30/mTOR signaling pathway by binding to GPR30.

Myocardial ischemia-reperfusion (I/R) injury, a major contributor to morbidity and mortality, represents a combination of intrinsic cellular response to ischemia and the extrinsic acute inflammatory response. In the present study, microarray analysis of GSE67308 and GSE50885 identified differentially expressed GPR30 and upstream regulatory miR-2861 and miR-5115 in myocardial I/R. Furthermore, GPR30 was confirmed as a common target gene of miR-2861 and miR-5115, and miR-2861 and miR-5115 inhibited GPR30 expression. Poor expression of GPR30 was identified in the myocardial I/R injury mouse model. Overexpressed GPR30 led to alleviated the pathological conditions, diminished myocardial infarct size and apoptosis of myocardial tissue in mice. Moreover, miR-2861 and miR-5115 were found to be highly expressed in the myocardial I/R injury mouse model and to subsequently accelerate the disease progression. Notably, PR30 curtailed the development of myocardial I/R injury through activation of the mTOR signaling pathway. The key findings suggested that miR-2861 and miR-5115 blocked the activation of the GPR30/mTOR signaling pathway by targeting GPR30, thereby accelerating myocardial I/R injury in mice.

MMP9, CXCR1, TLR6, and MPO participant in the progression of coronary artery disease.

Coronary artery disease (CAD) is the most frequent cardiovascular disease, which is induced by the decreased myocardial blood supply. The present study is conducted to understand the mechanisms of CAD. The GSE98583, GSE69587, and GSE71226 datasets from the Gene Expression Omnibus database were obtained. The differentially expressed genes (DEGs) were analyzed by the limma package, then the DEGs appeared in two or three datasets were selected as the coregulated genes using the VENNY tool, followed by enrichment analysis using DAVID tool. Protein-protein interaction (PPI) network, microRNA-transcription factor-target regulatory network, and drug-gene network were visualized. Finally, quantitative PCR and dual-luciferase reporter assay were conducted to validate the expression of key genes and the target relationship. There were 221 coregulated genes in GSE98583, GSE69587, and GSE71226. Besides, four pathways and 23 functional terms for co-upregulated genes, and 11 functional terms for co-downregulated genes were enriched. The degrees of PPI network nodes matrix metallopeptidase 9 (MMP9), C-X-C motif chemokine receptor 1 (CXCR1), toll-like receptor 6 (TLR6), and myeloperoxidase (MPO) were relatively higher. Moreover, MPO could interact with MMP9, CXCR1, and TLR6 in the PPI network. In the regulatory network, TLR6 and MMP9 separately were targeted by miR-3960 and v-rel avian reticuloendotheliosis viral oncogene homolog A (RELA). Additionally, MMP9, CXCR1, and MPO were involved in the drug-gene network. The expression of MMP9, CXCR1, TLR6, and MPO were significantly upregulated in CAD samples than control, and miR-3960 could bind to TLR6 to inhibit its expression. CXCR1 and MPO might be involved in the progression of CAD. Besides, miR-3960 might function in the pathogenesis of CAD through targeting TLR6, and RELA might exert its role in CAD via targeting MMP9.

Atherosclerosis-associated endothelial cell apoptosis by miRNA let7-b-mediated downregulation of HAS-2.

MicroRNAs (miRNAs) play essential roles in the regulation and pathophysiology of various types of human diseases including atherosclerosis. Increasing numbers of miRNAs have been identified to be important regulators in the progression of atherosclerosis by regulating gene expression. However, functional miRNAs and the underlying mechanisms involved in atherosclerosis need fully elucidation. In the present study, the function of miRNA let-7b was investigated in human aortic endothelial cells (HAECs). The results showed that downregulation of let-7b in the high-fat diet mice and HAECs was inversely correlated with the expression level of HAS-2. upregulation of let-7b significantly reduced apoptosis of HAECs. The results also revealed that HAS-2 was a target gene of let-7b and HAS-2 reduction reversed the antiapoptotic effect of let-7b through regulation of the P13K/Akt pathway. These results together suggest the potential of regulating the let-7b expression and endothelial apoptosis against development and progression of atherosclerosis.

Ship Type Classification by Convolutional Neural Networks with Auditory-like Mechanisms.

Ship type classification with radiated noise helps monitor the noise of shipping around the hydrophone deployment site. This paper introduces a convolutional neural network with several auditory-like mechanisms for ship type classification. The proposed model mainly includes a cochlea model and an auditory center model. In cochlea model, acoustic signal decomposition at basement membrane is implemented by time convolutional layer with auditory filters and dilated convolutions. The transformation of neural patterns at hair cells is modeled by a time frequency conversion layer to extract auditory features. In the auditory center model, auditory features are first selectively emphasized in a supervised manner. Then, spectro-temporal patterns are extracted by deep architecture with multistage auditory mechanisms. The whole model is optimized with an objective function of ship type classification to form the plasticity of the auditory system. The contributions compared with an auditory inspired convolutional neural network include the improvements in dilated convolutions, deep architecture and target layer. The proposed model can extract auditory features from a raw hydrophone signal and identify types of ships under different working conditions. The model achieved a classification accuracy of 87.2% on four ship types and ocean background noise.

Potential biomarkers for heart failure.

In this study, we identified candidate biomarkers for heart failure (HF). The gene expression profile GSE57338, containing 117 ischemic cardiomyopathic HF and 136 control samples, was downloaded and analyzed using various bioinformatics approaches. In total, 376 differentially expressed genes (DEGs) were identified, and four modules were explored in protein-protein interaction networks. DEGs (including ankyrin repeat and SOCS box-containing 14 [ASB14]) in the modules were mainly categorized by the function. Several relationships including interferon regulatory factor 1 (IRF1)-C-C motif chemokine ligand 5 (CCL5) were revealed in the transcription factor microRNA target gene regulatory network. Gene-drug analysis revealed 11 DEGs (such as the cluster of differentiation 163 [CD163]) for the target drugs. Data verification analysis identified 118 overlapping DEGs including ASB14, CD163, and CCL5. ASB14 may be involved in HF progression via protein ubiquitination and CCL5 may be involved in HF via the IRF1-CCL5 interaction. Genes including CD163 are potential biomarkers for HF.

A Deep Convolutional Neural Network Inspired by Auditory Perception for Underwater Acoustic Target Recognition.

Underwater acoustic target recognition (UATR) using ship-radiated noise faces big challenges due to the complex marine environment. In this paper, inspired by neural mechanisms of auditory perception, a new end-to-end deep neural network named auditory perception inspired Deep Convolutional Neural Network (ADCNN) is proposed for UATR. In the ADCNN model, inspired by the frequency component perception neural mechanism, a bank of multi-scale deep convolution filters are designed to decompose raw time domain signal into signals with different frequency components. Inspired by the plasticity neural mechanism, the parameters of the deep convolution filters are initialized randomly, and the is n learned and optimized for UATR. The n, max-pooling layers and fully connected layers extract features from each decomposed signal. Finally, in fusion layers, features from each decomposed signal are merged and deep feature representations are extracted to classify underwater acoustic targets. The ADCNN model simulates the deep acoustic information processing structure of the auditory system. Experimental results show that the proposed model can decompose, model and classify ship-radiated noise signals efficiently. It achieves a classification accuracy of 81.96%, which is the highest in the contrast experiments. The experimental results show that auditory perception inspired deep learning method has encouraging potential to improve the classification performance of UATR.

Competitive Deep-Belief Networks for Underwater Acoustic Target Recognition.

Underwater acoustic target recognition based on ship-radiated noise belongs to the small-sample-size recognition problems. A competitive deep-belief network is proposed to learn features with more discriminative information from labeled and unlabeled samples. The proposed model consists of four stages: (1) A standard restricted Boltzmann machine is pretrained using a large number of unlabeled data to initialize its parameters; (2) the hidden units are grouped according to categories, which provides an initial clustering model for competitive learning; (3) competitive training and back-propagation algorithms are used to update the parameters to accomplish the task of clustering; (4) by applying layer-wise training and supervised fine-tuning, a deep neural network is built to obtain features. Experimental results show that the proposed method can achieve classification accuracy of 90.89%, which is 8.95% higher than the accuracy obtained by the compared methods. In addition, the highest accuracy of our method is obtained with fewer features than other methods.

Compression of a Deep Competitive Network Based on Mutual Information for Underwater Acoustic Targets Recognition.

The accuracy of underwater acoustic targets recognition via limited ship radiated noise can be improved by a deep neural network trained with a large number of unlabeled samples. However, redundant features learned by deep neural network have negative effects on recognition accuracy and efficiency. A compressed deep competitive network is proposed to learn and extract features from ship radiated noise. The core idea of the algorithm includes: (1) Competitive learning: By integrating competitive learning into the restricted Boltzmann machine learning algorithm, the hidden units could share the weights in each predefined group; (2) Network pruning: The pruning based on mutual information is deployed to remove the redundant parameters and further compress the network. Experiments based on real ship radiated noise show that the network can increase recognition accuracy with fewer informative features. The compressed deep competitive network can achieve a classification accuracy of 89.1 % , which is 5.3 % higher than deep competitive network and 13.1 % higher than the state-of-the-art signal processing feature extraction methods.

Auditory Inspired Convolutional Neural Networks for Ship Type Classification with Raw Hydrophone Data.

Detecting and classifying ships based on radiated noise provide practical guidelines for the reduction of underwater noise footprint of shipping. In this paper, the detection and classification are implemented by auditory inspired convolutional neural networks trained from raw underwater acoustic signal. The proposed model includes three parts. The first part is performed by a multi-scale 1D time convolutional layer initialized by auditory filter banks. Signals are decomposed into frequency components by convolution operation. In the second part, the decomposed signals are converted into frequency domain by permute layer and energy pooling layer to form frequency distribution in auditory cortex. Then, 2D frequency convolutional layers are applied to discover spectro-temporal patterns, as well as preserve locality and reduce spectral variations in ship noise. In the third part, the whole model is optimized with an objective function of classification to obtain appropriate auditory filters and feature representations that are correlative with ship categories. The optimization reflects the plasticity of auditory system. Experiments on five ship types and background noise show that the proposed approach achieved an overall classification accuracy of 79.2%, which improved by 6% compared to conventional approaches. Auditory filter banks were adaptive in shape to improve accuracy of classification.

Protective Effects of MicroRNA-126 on Human Cardiac Microvascular Endothelial Cells Against Hypoxia/Reoxygenation-Induced Injury and Inflammatory Response by Activating PI3K/Akt/eNOS Signaling Pathway.

OBJECTIVE: This study explored the protective effects of the microRNA-126 (miR-126)-mediated PI3K/Akt/eNOS signaling pathway on human cardiac microvascular endothelial cells (HCMECs) against hypoxia/reoxygenation (H/R)-induced injury and the inflammatory response. METHODS: Untreated HCMECs were selected for the control group. After H/R treatment and cell transfection, the HCMECs were assigned to the H/R, miR-126 mimic, mimic-negative control (NC), miR-126 inhibitor, inhibitor-NC, wortmannin (an inhibitor of PI3K) and miR-126 mimic + wortmannin groups. Super oxide dismutase (SOD), nitric oxide (NO), vascular endothelial growth factor (VEGF) and reactive oxygen species (ROS) were measured utilizing commercial kits. Quantitative real-time polymerase chain reaction (qRT-PCR) and enzyme-linked immunosorbent assay (ELISA) were performed to detect miR-126 expression and the mRNA and protein expression of inflammatory factors. Western blotting was used to determine the expression of key members in the PI3K/Akt/eNOS signaling pathway. ACCK-8 assay and flow cytometry were employed to examine cell proliferation and apoptosis, respectively. The angiogenic ability in each group was detected by the lumen formation test. RESULTS: Compared to the control group, p/t-PI3K, p/t-Akt and p/t-eNOS expression, NO, VEGF and SOD levels, cell proliferation and in vitro lumen formation ability were decreased, while the ROS content, interleukin (IL)-6, IL-10 and tumor necrosis factor (TNF)-α expression and cell apoptosis were significantly increased in the H/R, mimic-NC, miR-126 inhibitor, inhibitor-NC, wortmannin and miR-126 mimic + wortmannin groups. Additionally, in comparison with the H/R group, the miR-126 mimic group had elevated p/t-PI3K, p/t-Akt and p/t-eNOS expression, increased NO, VEGF and SOD contents, and strengthened cell proliferation and lumen formation abilities but also exhibited decreased ROS content, reduced IL-6, IL-10 and TNF-α expressions, and weakened cell apoptosis, while the miR-126 inhibitor and wortmannin group exhibited the opposite results. Furthermore, decreased p/t-PI3K, p/t-Akt and p/t-eNOS expressions, decreased NO, VEGF and SOD contents, cell proliferation and lumen formation abilities, as well as increased ROS content, increased IL-6, IL-10 and TNF-α expression, and increased cell apoptosis were observed in the miR-126 mimic + wortmannin group compared to themiR-126 mimic group. CONCLUSIONS: These findings indicated that miR-126 protects HCMECs from H/R-induced injury and inflammatory response by activating the PI3K/Akt/ eNOS signaling pathway.

CNTN-1 Enhances Chemoresistance in Human Lung Adenocarcinoma Through Induction of Epithelial-Mesenchymal Transition by Targeting the PI3K/Akt Pathway.

BACKGROUND/AIMS: Chemoresistance has been a major obstacle to the effective treatment of lung cancer. Previously, we found that contactin-1 (CNTN-1) is related to cisplatin resistance in lung adenocarcinoma. Here, we aimed to investigate the underlying mechanism behind the role of CNTN-1 in cisplatin resistance in lung adenocarcinoma. METHODS: EMT-associated phenotypes, including alterations in cellular morphology and marker (E-cadherin, N-cadherin and Vimentin) expression, were compared between A549 cells and A549/DDP cells (a cisplatin-resistant cell line of lung adenocarcinoma with abnormal CNTN-1 expression) by using real-time time PCR and Western blotting. Other methods, including CNTN-1 overexpression in A549 cells and CNTN-1 knockdown in A549/DDP cells, were also used to investigate the role of CNTN-1 in mediating the EMT phenotype and thr resulting cisplatin resistance and malignant progression of cancer cells in vitro and in vivo. RESULTS: A549/DDP cells exhibited an EMT phenotype and aggravated malignant behaviors. CNTN-1 knockdown in A549/DDP cells partly reversed the EMT phenotype, increased drug sensitivity, and attenuated the malignant progression whereas CNTN-1 overexpression in A549 cells resulted in the opposite trend. Furthermore, the PI3K/Akt pathway was involved in the effects of CNTN-1 on EMT progression in A549/DDP cells, verified by the xenograft mouse model. CONCLUSION: CNTN-1 promotes cisplatin resistance in human cisplatin-resistant lung adenocarcinoma through inducing the EMT process by activating the PI3K/Akt signaling pathway. CNTN-1 may be a potential therapeutic target to reverse chemoresistance in cisplatin-resistant lung adenocarcinoma.

[Short- or long-outcome of early tirofiban in ST-segment elevated acute myocardial infarction undergoing elective percutaneous coronary intervention].

OBJECTIVE: To explore the optimal timing of tirofiban early treatment in ST-segment elevated acute myocardial infarction (STEMI) undergoing elective percutaneous coronary intervention (PCI). METHODS: A total of 118 consecutive STEMI patients were enrolled in the study. They were randomly assigned to the tirofiban early treatment group with tirofiban administrated routinely at ≥ 4 hours prior to angiography or the control group with tirofiban provisional administrated during or after angiography. Thrombolysis in myocardial infarction (TIMI) flow, creatine kinase MB isoenzyme (CK-MB) levels, acute thrombus events, efficacy and safety endpoints at Day 7, Day 30, 6 months and 1 year (efficacy endpoints: death, myocardial infarction, target vessel revascularization and ischemic stroke; safety endpoints: bleeding and thrombocytopenia) were observed and compared between the two groups. RESULTS: A total of 104 STEMI patients underwent elective PCI with 52 patients in each group and the baseline characteristics were balanced between the two groups. Tirofiban was administered (5.9 ± 2.9) hours earlier in the tirofiban early treatment group than the control group. No statistical difference was observed between the two groups in TIMI flow before[grade 0: 18 (34.6%) vs 19 (36.5%) , grade 3: 28 (53.8%) vs 27 (51.9%)] and after PCI[grade 3: 52 (100.0%) vs 51 (98.1%)]. No difference was shown between the two groups in CK-MB levels before PCI [(12.9 ± 5.1) U/L vs (12.0 ± 5.2) U/L, P > 0.05]; and the increase of CK-MB 12-24 hours after PCI [(1.0 ± 6.2) U/L vs (2.3 ± 8.3) U/L, P > 0.05]. The incidence of acute thrombus events was similar (7.7% vs 15.4%, P > 0.05). No statistical difference was observed between the two groups in the efficacy endpoints at Day 7 (0.0% vs 7.7%, P > 0.05), Day 30 (0.0% vs 7.8%, P > 0.05), 6 months (2.0% vs 9.8%, P > 0.05) and 1 year (2.2% vs 9.8%, P > 0.05). Similar incidence was shown in the slight bleeding (15.4% vs 5.8%, P > 0.05) and the slight thrombocytopenia (0.0% vs 1.9%, P > 0.05), while no severe to moderate bleeding or severe thrombocytopenia happened in both groups. CONCLUSION: Tirofiban early treatment is not better than the tirofiban bailout treatment during or after PCI in STEMI patients undergoing elective PCI. Trail registration ChiCTR-TRC-10000809.

Ligustrazine alleviates the progression of coronary artery calcification by inhibiting caspase-3/GSDME mediated pyroptosis.

Coronary artery calcification (CAC) is an early marker for atherosclerosis and is mainly induced by the osteoblast-like phenotype conversion of vascular smooth muscle cells (VSMCs). Recent reports indicate that NOD-like receptor protein 3 (NLRP3)-mediated pyroptosis plays a significant role in the calcification of vascular smooth muscle cells (VSMCs), making it a promising target for treating calcific aortic valve disease (CAC). Ligustrazine, or tetramethylpyrazine (TMP), has been found effective in various cardiovascular and cerebrovascular diseases and is suggested to inhibit NLRP3-mediated pyroptosis. However, the function of TMP in CAC is unknown. Herein, influences of TMP on ß-glycerophosphate (ß-GP)-stimulated VSMCs and OPG-/- mice were explored. Mouse Aortic Vascular Smooth Muscle (MOVAS-1) cells were stimulated by ß-GP with si- caspase-3, si- Gasdermin E (GSDME) or TMP. Increased calcification, reactive oxygen species (ROS) level, Interleukin-1beta (IL-1ß) and Interleukin-18 (IL-18) levels, lactate dehydrogenase (LDH) release, enhanced apoptosis, and activated cysteine-aspartic acid protease-3 (caspase-3)/GSDME signaling were observed in ß-GP-stimulated MOVAS-1 cells, which was sharply alleviated by si-caspase-3, si-GSDME or TMP. Furthermore, the impact of TMP on the ß-GP-induced calcification and injury in MOVAS-1 cells was abolished by raptinal, an activator of caspase-3. Subsequently, OPG-/- mice were dosed with TMP or TMP combined with raptinal. Calcium deposition, increased nodules, elevated IL-1ß and IL-18 levels, upregulated CASP3 and actin alpha 2, smooth muscle (ACTA2), and activated caspase-3/GSDME signaling in OPG-/- mice were markedly alleviated by TMP, which were notably reversed by the co-administration of raptinal. Collectively, TMP mitigated CAC by inhibiting caspase-3/GSDME mediated pyroptosis.

Caspase-3/gasdermin-E axis facilitates the progression of coronary artery calcification by inducing the release of high mobility group box protein 1.

Coronary artery calcification (CAC) is commonly observed in atherosclerotic plaques, which is a pathogenic factor for severe coronary artery disease (CAD). The phenotype changes of vascular smooth muscle cells (VSMCs) are found to participate in CAC progression, which is mainly induced by vascular inflammation and oxidative stress (OS). HMGB1, a critical inflammatory cytokine, is recently reported to induce arterial calcification, which is regulated by the Caspase-3/gasdermin-E (GSDME) axis. However, the function of the Caspase-3/GSDME axis in CAC is unknown. Herein, the involvement of the Caspase-3/GSDME axis in CAC was studied to explore the possible targets for CAC. CAC model was constructed in mice, which was verified by red cytoplasm in coronary artery tissues, increased macrophage infiltration, aggravated inflammation, and enhanced RAGE signaling, accompanied by an increased release of HMGB1 and an activated Caspase-3/ GSDME axis. In ß-GP-treated MOVAS-1 cells, calcification, the ROS accumulation, enhanced LDH and HMGB1 release, enlarged macrophage production, aggravated inflammation, and activated RAGE signaling were observed, which were markedly abolished by the transfection of si-HMGB1 and si-GSDME. Moreover, the calcification deposition, the activity of Caspase-3/ GSDME axis, release of HMGB1, macrophage infiltration, cytokine production, and RAGE signaling in CAC mice were notably alleviated by VSMCs-specific GSDME knockdown, not by hematopoietic stem cells (HSCs)-specific GSDME knockdown. Collectively, Caspase-3/GSDME axis facilitated the progression of CAC by inducing the release of HMGB1.

Identification of key genes related to heart failure by analysis of expression profiles.

Introduction: To elucidate the candidate biomarkers involved in the pathogenesis process of heart failure (HF) via analysis of differentially expressed genes (DEGs) of the dataset from the Gene Expression Omnibus (GEO). Material and methods: The GSE76701 gene expression profiles regarding the HF and control subjects were respectively analysed. Briefly, DEGs were firstly identified and subjected to Cytoscape plug-in ClueGO + CluePedia and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. A protein-protein interaction (PPI) network was then built to analyse the interaction between DEGs, followed by the construction of an interaction network by combining with hub genes with the targeted miRNA genes of DEGs to identify the key molecules of HF. In addition, potential drugs targeting key DEGs were sought using the drug-gene interaction database (DGIdb), and a drug-mRNA-miRNA interaction network was also constructed. Results: A total of 489 DEGs were verified between HF and control, which mainly enriched in type I interferon and leukocyte migration according to molecular function. Significantly increased levels of GAPDH, GALM1, MMP9, CCL5, and GNAL2 were found in the HF setting and were identified as the hub genes based on the PPI network. Furthermore, according to the drug-mRNA-miRNA network, FCGR2B, CCND1, and NF-κb, as well as corresponding miRNA-605-5p, miRNA-147a, and miRNA-671-5p were identified as the drug targets of HF. Conclusions: The hub genes GAPDH, GALM1, MMP9, CCL5, and GNAL2 were significantly increased in HF. miRNA-605-5p, miRNA-147a, and miRNA-671-5p were predicted as the drug target-interacted gene-miRNA of HF.