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With the rapid growth of the software industry, the software supply chain (SSC) has become the most intricate system in the complete software life cycle, and the security threat situation is becoming increasingly severe. For the description of the SSC, the relevant research mainly focuses on the perspective of developers, lacking a comprehensive understanding of the SSC. This paper proposes a chain portrait framework of the SSC based on a resource perspective, which comprehensively depicts the threat model and threat surface indicator system of the SSC. The portrait model includes an SSC threat model and an SSC threat indicator matrix. The threat model has 3 levels and 32 dimensions and is based on a generative artificial intelligence model. The threat indicator matrix is constructed using the Attack Net model comprising 14-dimensional attack strategies and 113-dimensional attack techniques. The proposed portrait model’s effectiveness is verified through existing SSC security events, domain experts, and event visualization based on security analysis models.

Rev-erb α, known as nuclear receptor 1D1 (NR1D1), regulates circadian rhythm, modulates glucose and lipid metabolism, and inflammatory response. However, little is known about the effect of Rev-erb agonist on the progression of myocardial infarction (MI) and heart failure. To investigate it, wild-type male mice underwent sham-operation or permanent ligation of the left anterior descending coronary artery to create MI model. Rev-erb agonist SR9009 (100 mg/kg/day) or vehicle was intraperitoneally administered. Echocardiography was performed to evaluate cardiac function 1 week after surgery. The gene and protein expression levels in the left ventricles (LVs) were determined with real-time PCR, western blotting, and immunofluorescence. Moreover, immune cell infiltration into the LVs was analyzed by flow cytometry. Survival rate and reduced LV function were significantly improved by the treatment with SR9009 after MI. The expression level and plasma concentration of brain natriuretic peptide were significantly lower in MI mice treated with SR9009 (MI+SR) than in MI mice treated with vehicle (MI+V). Moreover, the mRNA expression levels of inflammatory-related molecules such as Il6, Mcp1, Ly6g, Cd11b, matrix metallopeptidase (Mmp)9, and the protein expression levels of phosphorylated NF-κB p65, phosphorylated ERK, and phosphorylated p38 were also significantly lower in MI+SR than in MI+V. Immunofluorescence intensity for MMP-9 was enhanced in the LVs, but was less so in MI+SR than in MI+V. Furthermore, infiltrations of neutrophils and proinflammatory macrophages in the LVs were dramatically increased in MI+V and were significantly suppressed in MI+SR. Rev-erb agonist SR9009 treatment inhibited post-MI mortality and improved cardiac function through modulating inflammation and remodeling process.

Aim: Recruitment of neutrophils to the heart following acute myocardial infarction (MI) initiates inflammation and contributes to adverse post-infarct left ventricular (LV) remodeling. However, therapeutic inhibition of neutrophil recruitment into the infarct zone has not been beneficial in MI patients, suggesting a possible dual role for neutrophils in inflammation and repair following MI. Here, we investigate the effect of neutrophils on cardiac fibroblast function following MI. Methods and Results: We found that co-incubating neutrophils with isolated cardiac fibroblasts enhanced the production of provisional extracellular matrix proteins and reduced collagen synthesis when compared to control or co-incubation with mononuclear cells. Furthermore, we showed that neutrophils are required to induce the transient up-regulation of transforming growth factor (TGF)-ß1 expression in fibroblasts, a key requirement for terminating the pro-inflammatory phase and allowing the reparatory phase to form a mature scar after MI. Conclusion: Neutrophils are essential for both initiation and termination of inflammatory events that control and modulate the healing process after MI. Therefore, one should exercise caution when testing therapeutic strategies to inhibit neutrophil recruitment into the infarct zone in MI patients.

Diabetic cardiomyopathy (DM CMP) is defined as cardiomyocyte damage and ventricular dysfunction directly associated with diabetes independent of concomitant coronary artery disease or hypertension. Matrix metalloproteinases (MMPs), especially MMP-2, have been reported to underlie the pathogenesis of DM CMP by increasing extracellular collagen content. We hypothesized that two discrete MMP-2 isoforms (full length MMP-2, FL-MMP-2; N-terminal truncated MMP-2, NTT-MMP-2) are induced by high glucose stimulation in vitro and in an experimental diabetic heart model. Rat cardiomyoblasts (H9C2 cells) were examined to determine whether high glucose can induce the expression of the two isoforms of MMP-2. For the in vivo study, we used the streptozotocin-induced DM mouse heart model and age-matched controls. The changes of each MMP-2 isoform expression in the diabetic mice hearts were determined using quantitative real-time polymerase chain reaction (qRT-PCR). Immunohistochemical stains were conducted to identify the location and patterns of MMP-2 isoform expression. Echocardiography was performed to compare and analyze the changes in cardiac function induced by diabetes. Quantitative RT-PCR and immunofluorescence staining showed that the two MMP-2 isoforms were strongly induced by high glucose stimulation in H9C2 cells. Although no definite histologic features of diabetic cardiomyopathy were observed in diabetic mice hearts, left ventricular systolic dysfunction was determined by echocardiography. Quantitative RT-PCR and IHC staining showed this abnormal cardiac function was accompanied with the increases in the mRNA levels of the two isoforms of MMP-2 and related to intracellular localization. Two isoforms of MMP-2 were induced by high glucose stimulation in vitro and in a Type 1 DM mouse heart model. Further study is required to examine the role of these isoforms in DM CMP.

Periostin, a novel matricellular protein, is recently reported to play a crucial role in tissue remodeling and is highly expressed under fibrotic conditions. This study was undertaken to assess the role of periostin in scleroderma. Using skin from patients and healthy donors, the expression of periostin was assessed by immunohistochemistry and immunoblotting analyses. Furthermore, we investigated periostin(-/-) (PN(-/-)) and wild-type (WT) mice to elucidate the role of periostin in scleroderma. To induce murine cutaneous sclerosis, mice were subcutaneously injected with bleomycin, while untreated control groups were injected with phosphate-buffered saline. Bleomycin-induced fibrotic changes were compared in PN(-/-) and WT mice by histological analysis as well as by measurements of profibrotic cytokine and extracellular matrix protein expression levels in vivo and in vitro. To determine the downstream pathway involved in periostin signaling, receptor neutralizing antibody and signal transduction inhibitors were used in vitro. Elevated expression of periostin was observed in the lesional skin of patients with scleroderma compared with healthy donors. Although WT mice showed marked cutaneous sclerosis with increased expression of periostin and increased numbers of myofibroblasts after bleomycin treatment, PN(-/-) mice showed resistance to these changes. In vitro, dermal fibroblasts from PN(-/-) mice showed reduced transcript expression of alpha smooth actin and procollagen type-I alpha 1 (Col1α1) induced by transforming growth factor beta 1 (TGFβ1). Furthermore, recombinant mouse periostin directly induced Col1α1 expression in vitro, and this effect was inhibited by blocking the αv integrin-mediated PI3K/Akt signaling either with anti-αv functional blocking antibody or with the PI3K/Akt kinase inhibitor LY294002. Periostin plays an essential role in the pathogenesis of Bleomycin-induced scleroderma in mice. Periostin may represent a potential therapeutic target for human scleroderma.

Telocytes (TCs) are a novel type of interstitial cells only recently described. This study aimed at characterizing and quantifying TCs and telopodes (Tps) in normal and diseased hearts. We have been suggested that TCs are influenced by the extracellular matrix (ECM) composition. We used transmission electron microscopy and c‐kit immunolabelling to identify and quantify TCs in explanted human hearts with heart failure (HF) because of dilated, ischemic or inflammatory cardiomyopathy. LV myectomy samples from patients with aortic stenosis with preserved ejection fraction and samples from donor hearts which could not be used for transplantation served as controls. Quantitative immunoconfocal analysis revealed that 1 mm2 of the normal myocardium contains 14.9 ± 3.4 TCs and 41.6 ± 5.9 Tps. As compared with the control group, the number of TCs and Tps in HF decreased more than twofold. There were no differences between HF and control in the number of Ki67‐positive TCs. In contrast, terminal deoxynucleotidyl transferase‐mediated dUTP nick end labelling‐positive TCs increased threefold in diseased hearts as compared to control. Significant inverse correlations were found between the amount of mature fibrillar collagen type I and the number of TCs (r = −0.84; P < 0.01) and Tps (r = −0.85; P < 0.01). The levels of degraded collagens showed a significant positive relationship with the TCs numbers. It is concluded that in HF the number of TCs are decreased because of higher rates of TCs apoptosis. Moreover, our results indicate that a close relationship exists between TCs and the ECM protein composition such that the number of TCs and Tps correlates negatively with the amount of mature fibrillar collagens and correlates positively with degraded collagens.

Cardiovascular disease is the leading cause of mortality globally, necessitating precise and prompt predictive instruments to enhance patient outcomes. In recent years, machine learning methodologies have demonstrated significant potential in enhancing the precision and efficacy of health-related predictions, especially in the identification of heart disease. The dataset used in this study came from the UC Irvine Machine Learning Repository and included data from Cleveland, Switzerland, Hungary, Long Beach, and Statlog. We selected seven of the 1,190 cases, each with 12 attributes, for analysis. We used different machine learning models, like Random Forest, K-Nearest Neighbors, Logistic Regression, Naïve Bayes, Gradient Boosting, AdaBoost, XGBoost, and Bagged Trees, to check performance using accuracy, precision, recall, F1-score, and ROC-AUC. K-fold cross-validation (K = 10, K = 5) was conducted to guarantee the robustness and generalizability of these models. Random Forest exhibited remarkable stability, attaining 94% accuracy with K = 10 and 92% with K = 5, whereas XGBoost had a minor decrease during cross-validation (90% for K = 10, 89% for K = 5). KNN demonstrated possible overfitting, evidenced by a notable decline in accuracy (71% for K = 10, 72% for K = 5). XGBoost and Bagged Trees achieved the highest accuracy of 93%, followed by Random Forest and KNN at 91%. Furthermore, Random Forest and Bagged Trees exhibited the highest ROC-AUC values at 95%, and XGBoost demonstrated a ROC-AUC of 94%. The results demonstrate the effectiveness of ensemble methods in predicting cardiac diseases, along with the potential for future advancement through the incorporation of hybrid models and advanced survival analysis techniques.

Background: Stroke is a devastating condition encompassing a wide range of pathophysiological entities that include thrombosis, hemorrhage, and embolism. Current diagnosis of stroke relies on physician clinical examination and is further supplemented with various neuroimaging techniques. A single set or multiple sets of blood biomarkers that could be used in an acute setting to diagnosis stroke, differentiate between stroke types, or even predict an initial/reoccurring stroke would be extremely valuable. Content: We discuss the current classification, diagnosis, and treatment of stroke, focusing on use of novel biomarkers (either solitary markers or multiple markers within a panel) that have been studied in a variety of clinical settings. Summary: The current diagnosis of stroke remains hampered and delayed due to lack of a suitable mechanism for rapid (ideally point-of-care), accurate, and analytically sensitive biomarker-based testing. There is a clear need for further development and translational research in this area. Potential biomarkers identified need to be transitioned quickly into clinical validation testing for further evaluation in an acute stroke setting; to do so would impact and improve patient outcomes and quality of life.

Globally, cardiovascular diseases remain among the leading causes of mortality, highlighting the urgent need for innovative research models. Consequently, the development of accurate models that simulate cardiac function holds significant scientific and clinical value for both disease research and therapeutic interventions. Cardiac organoids, which are three-dimensional structures derived from the induced differentiation of stem cells, are particularly promising. These organoids not only replicate the autonomous beating and essential electrophysiological properties of the heart but are also widely employed in studies related to cardiac diseases, drug efficacy testing, and regenerative medicine. This review comprehensively surveys the various fabrication techniques used to create cardiac organoids and their diverse applications in modeling a range of cardiac diseases. We emphasize the role of advanced technologies in enhancing the maturation and functionality of cardiac cells, ensuring that these models closely resemble native cardiac tissue. Furthermore, we discuss monitoring techniques and evaluation parameters critical for assessing the performance of cardiac organoids, considering the complex interactions within multi-organ systems. This approach is vital for enhancing precision and efficiency in drug development, allowing for more effective therapeutic strategies. Ultimately, this review aims to provide a thorough and innovative perspective on both fundamental research and clinical treatment of cardiovascular diseases, offering insights that could pave the way for future advancements in understanding and addressing these prevalent health challenges. Graphical abstract

MSCs are widely applied to regenerate heart tissue in myocardial diseases but when grown in standard two-dimensional (2D) cultures exhibit limited potential for cardiac repair and develop fibrogenic features with increasing culture time. MSCs can undergo partial cardiomyogenic differentiation, which improves their cardiac repair capacity. When applied to collagen patches they may improve cardiac tissue regeneration but the mechanisms remain elusive. Here, we investigated the regenerative properties of MSCs grown in a collagen scaffold as a three-dimensional (3D) culture system, and performed functional analysis using an engineered heart tissue (EHT) model. We showed that the expression of cardiomyocyte-specific proteins by MSCs co-cultured with rat neonatal cardiomyocytes was increased in collagen patches versus conventional cultures. MSCs in 3D collagen patches were less fibrogenic, secreted more cardiotrophic factors, retained anti-apoptotic and immunomodulatory function, and responded less to TLR4 ligand lipopolysaccharide (LPS) stimulation. EHT analysis showed no effects by MSCs on cardiomyocyte function, whereas control dermal fibroblasts abrogated the beating of cardiac tissue constructs. We conclude that 3D collagen scaffold improves the cardioprotective effects of MSCs by enhancing the production of trophic factors and modifying their immune modulatory and fibrogenic phenotype. The improvement in myocardial function by MSCs after acquisition of a partial cardiac cell-like phenotype is not due to enhanced MSC contractility. A better understanding of the mechanisms of MSC-mediated tissue repair will help to further enhance the therapeutic potency of MSCs.