The development of unstable carotid atherosclerotic plaques is associated with the induction of neutrophil extracellular traps (NETs) via the activation of diverse inflammatory mediators in the circulating bloodstream. However, the underlying mechanisms through which NETs influence the microenvironment of atherosclerotic plaques and contribute to the development of unstable carotid plaques remain largely elusive. The objective of this study was to elucidate the role of myeloid differentiation protein 1 (MD-1, LY86)-induced NETs underlying the crosstalk between unstable plaque formation and the plaque microenvironment. We employed bioinformatics analysis to identify key genes associated with carotid-unstable plaque, followed by comprehensive validation using various experimental approaches on tissue specimens and plasma samples classified based on pathological characteristics. Patients with carotid-unstable plaques exhibited elevated plasma concentrations of MD-1 (LY86), while patients with stable plaques demonstrated comparatively lower levels. Furthermore, soluble MD-1 was found to induce the formation of NETs through activation of Toll-like receptor signaling pathway. The proliferative and immature vascularization effects of NETs on endothelial cells, as well as their inhibitory impact on cell migration, are directly correlated with the concentration of NETs. Additionally, NETs were found to activate the NF-κB signaling pathway, thereby upregulating ICAM1, VCAM1, MMP14, VEGFA, and IL6 expression in both Human umbilical vein endothelial cells (HUVECs) and HAECs. Subsequently, a significant increase in intraplaque neovascularization by NETs results in poor carotid plaque stability, and NETs in turn stimulate macrophages to produce more MD-1, generating a harmful positive feedback loop. Our findings suggest that soluble MD-1 in the bloodstream triggers the production of NETs through activation of the Toll-like receptor signaling pathway and further indicate NETs mediate a crosstalk between the microenvironment of the carotid plaque and the neovascularization of the intraplaque region. Inhibiting NETs formation or MD-1 secretion may represent a promising strategy to effectively suppress the development of unstable carotid plaques.

Ischemia-reperfusion injury (IRI) represents a critical pathological process contributing to secondary tissue damage in cardiovascular and cerebrovascular diseases. The complexity of the cell death network involved poses substantial challenges for therapeutic intervention. Among emerging forms of regulated cell death, autophagy-dependent ferroptosis and PANoptosis have attracted considerable attention. This review aims to elucidate the potential formation of a highly coordinated cell death network in myocardial and cerebral IRI through the convergence of these two pathways via shared key molecules. By examining their molecular underpinnings, we focus on core regulators such as NLRP3, STING, RIPK, GPX4, and NCOA4, which not only drive their respective pathways but may also facilitate PANoptosome assembly and integrate death signals, thereby mediating signal amplification and crosstalk. Despite inherent differences between cardiac and cerebral tissues, this network may exert synergistic effects during IRI progression by sharing upstream oxidative stress and inflammatory signals. Although current evidence is largely derived from in vitro models, the in vivo interactions and clinical translational potential of these mechanisms remain to be further validated. We propose that future therapeutic strategies may shift from targeting individual pathways to intervening at critical crosstalk nodes, offering a new direction for developing more effective protective strategies against IRI in the heart, brain, and beyond.

Objective: This meta-analysis was conducted to systematically evaluate the accuracy of image-based deep learning models for aortic dissection segmentation and diagnosis, aiming to provide an evidence base for developing intelligent detection tools. Methods: A comprehensive search was performed across the Cochrane Library, PubMed, Embase, and Web of Science to identify studies on the effectiveness of deep learning in aortic dissection segmentation or diagnosis up to November 3, 2024. Risk evaluation was carried out with the Quality Assessment of Diagnostic Accuracy Studies—2 (QUADAS-2). Results: A total of 48 studies on deep learning models for aortic dissection segmentation or diagnostic tasks were included, with 28 on segmentation tasks and 20 on diagnostic tasks. For segmentation tasks, the mean Dice coefficient was 89.2% ± 4.4% for false lumen segmentation, 90.8% ± 3.4% for true lumen segmentation, and 91.7% ± 6.1% for entire aorta segmentation. For diagnostic tasks, computed tomography (CT) based deep learning showed pooled sensitivity and specificity of 0.94 [95% confidence interval (CI): 0.89–0.96] and 0.92 (95% CI: 0.88–0.95), respectively. In terms of electrocardiogram-based deep learning, the pooled sensitivity and specificity were 0.85 (95% CI: 0.79–0.89) and 0.90 (95% CI: 0.87–0.92), respectively. Regarding the computed tomography angiography (CTA) based deep learning, the pooled sensitivity and specificity were 0.94 (95% CI: 0.90–0.96) and 0.95 (95% CI: 0.91–0.98), respectively. Additionally, some studies compared the diagnostic performance of deep learning with that of clinicians. The pooled sensitivity and specificity were 0.79 (95% CI: 0.65–0.89) and 0.95 (95% CI: 0.88–0.94), respectively. Conclusions: Image-based deep learning models demonstrated high accuracy for aortic dissection segmentation and diagnosis. They performed comparably to or better than clinicians. These findings support their potential as clinical assistive tools. Future work should prioritize multicenter validation, seamless integration of these models into clinical workflows, and enhancement of model generalizability to facilitate broader clinical adoption. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD42024619403 , identifier CRD42024619403.

This study aimed to identify biomarkers associated with MIRI and elucidate their potential mechanisms through integrated bioinformatics and experimental va