Ioannis Panagiotopoulos

Micro- and nanoplastics (MNPs) are gaining prominence as human-made pollutants that continuously enter the human body through ingestion, inhalation, and iatrogenic routes. Current analytical technologies utilizing vibrational chemical imaging and thermal degradation mass spectrometry have established that these materials enter blood circulation and accumulate in the human cardiovascular system (blood, arteries, plaques, thrombi, and myocardium). MNPs function as a unique combination of solid and chemical pollutants since the materials contain an insoluble polymer core, formulation-specific additives, such as phthalates and bisphenols that can leach into the surrounding milieu, and contaminants that adsorb to the surface. This phenomenon, called the “Trojan horse” effect, explains this property of MNPs. Experimental studies have demonstrated their ability to degrade the endothelial glycocalyx and uncouple endothelial nitric oxide synthase. MNPs also trigger mitochondrial stress, which activates the NLRP3 inflammasome pathway. These mechanisms can contribute to multiple cardiovascular diseases like atherosclerotic cardiovascular disease (ASCVD), acute coronary syndromes (ACS), heart failure, arrhythmia, and thromboembolism. The findings from human surgical cohorts show that there is a connection between polymer accumulation in plaques, the development of vascular inflammation, the structural vulnerability of plaques, and the incidence of major adverse cardiovascular events. However, it is important to understand that these results are not sufficient for establishing definitive clinical causality. Most human data come from cross-sectional studies that can suffer from reverse causation, such as when inflamed, hyperpermeable vascular walls allow the entrance of the pollutants without them being the direct cause of the disease. To reach a definite conclusion about the connection between MNPs and cardiovascular diseases, it is essential to conduct intervention studies and establish prospective longitudinal exposure cohorts.

Background: The growing interest in artificial intelligence (AI) in cardiothoracic surgery has led to a substantial increase in scientific publications, although its integration into routine clinical practice remains limited. Despite encouraging developments in several areas of surgical care, the clinical benefits of many proposed applications have yet to be established. Objective: This review critically examines the discrepancy between the reported capabilities of AI-based technologies and their implementation in clinical cardiothoracic surgical practice, with particular emphasis on the factors limiting their broader adoption. Methods: A semi-systematic literature review covering publications from January 2018 through September 2026 was conducted to examine the development, validation, and clinical application of AI-based tools across the preoperative, intraoperative, and postoperative stages of cardiothoracic surgical care. Particular attention was given to methodological limitations, external validation, and barriers to clinical implementation. Key Findings: Although several AI models have demonstrated promising performance in retrospective studies and controlled research settings, evidence supporting their reliability and clinical utility in routine practice remains limited. Important challenges include insufficient external and prospective validation, variable generalizability across clinical settings, limited model interpretability, and difficulties integrating AI-based systems into established surgical and intensive care workflows. Regulatory requirements, technical infrastructure, and human factors represent additional barriers to implementation. Conclusions: The clinical translation of AI in cardiothoracic surgery requires more than improvements in algorithmic performance. Greater emphasis on robust external validation, prospective clinical evaluation, workflow integration, and appropriate regulatory oversight is necessary to establish the safety, effectiveness, and practical value of these technologies. Addressing these challenges will be essential for translating advances in AI research into meaningful improvements in cardiothoracic surgical care.

Artificial intelligence (AI) is moving rapidly from retrospective prediction and image analysis into treatment selection, operative planning, intraoperative guidance, and postoperative prognostication in cardiothoracic surgery. This transition raises an ethical problem that cannot be resolved by model accuracy alone: when an algorithm begins to shape a high-stakes clinical decision, the distribution of knowledge, authority, and responsibility also changes. This review synthesizes cardiothoracic and closely related medical evidence available through August 2026, with emphasis on quantitative performance, human–AI interaction, bias, patient autonomy, and liability. The available evidence is simultaneously encouraging and cautionary. Machine-learning approaches can improve predictive performance and AI-assisted thoracic planning can reduce errors and increase procedural consistency; however, these gains have not consistently translated into superior patient outcomes. Human–AI studies similarly demonstrate that improved accuracy may coexist with automation bias and overacceptance of algorithmic recommendations. Evidence of demographic performance disparities and limitations in the representativeness of training and validation datasets further raises concerns regarding fairness and equitable access to care. On this basis, we argue that cardiothoracic AI should be governed according to the level of decision influence rather than by technology type alone. We distinguish non-delegable professional duties, distributed system responsibilities, and non-transferable patient authority, and propose an Ethical Heart Team Framework for converting algorithmic output into ethically defensible clinical action.

The exponential rise in global plastic production has resulted in the widespread environmental dissemination of micro- and nanoplastics (MNPs) across air, water, and biological systems. Inhalation of airborne MNPs represents a biologically plausible pathway of pulmonary exposure, particularly within indoor and occupational environments. Experimental evidence indicates that inhaled MNPs deposit within distal lung compartments, where their small aerodynamic diameter and surface reactivity may favor cellular uptake, oxidative stress induction, inflammatory activation, and prolonged biopersistence. Experimental studies further indicate that MNP exposure may induce DNA damage, chromosomal instability, and the dysregulation of signaling pathways involved in genomic integrity, thereby providing additional mechanistic support for their potential role in carcinogenesis. Chronic redox imbalance, macrophage dysfunction, inflammasome activation, epithelial–mesenchymal transition, and dysregulated cell adhesion collectively resemble mechanisms implicated in inflammation-associated carcinogenesis. Emerging in vitro and in vivo data further suggest that nanoplastics may function as tumor promoters or co-carcinogenic modifiers, particularly under chronic low-dose exposure or in combination with other airborne toxicants. However, human epidemiological evidence remains limited, and causality has not been established. This review synthesizes current mechanistic evidence regarding inhaled MNPs as potential modifiers of lung carcinogenesis, compares them with established inhaled carcinogens, and outlines critical research priorities necessary to clarify exposure–response relationships and clinical relevance. Current evidence supports biological plausibility rather than confirmed carcinogenic classification.

Background: The Langendorff isolated heart model remains one of the most widely used experimental platforms for cardiovascular research. However, conventional bubble oxygenation is associated with several limitations, including inefficient gas utilization and incompatibility with protein-containing perfusates due to excessive foam formation. The present study evaluated whether membrane oxygenation could improve myocardial preservation and facilitate the use of a protein-enriched perfusion solution in a constant-pressure Langendorff system. Methods: A total of 48 male Wistar rats were allocated to six experimental groups (n = 8 per group). In the first experimental series, myocardial performance was compared between a conventional bubble oxygenator, a Terumo CAPIOX^® FX05 membrane oxygenator, and a Novalung iLA membrane oxygenator. In the second series, standard Krebs–Henseleit buffer was compared with a bovine serum albumin-enriched perfusate under membrane oxygenation. Hemodynamic parameters, coronary flow, and perfusate pH were assessed throughout a 180 min ischemia–reperfusion protocol. Results: Both membrane oxygenators demonstrated significantly improved myocardial preservation compared with the conventional bubble oxygenator, as evidenced by superior systolic and diastolic function, enhanced coronary flow, and improved overall cardiac performance. No significant differences were observed between the two membrane oxygenators. Membrane oxygenation additionally enabled stable supplementation of the perfusate with bovine serum albumin, which resulted in further improvements in ventricular function and coronary perfusion. Perfusate pH remained comparable among groups. Furthermore, membrane oxygenation reduced Carbozen consumption by approximately 33%, increasing the number of experiments that could be performed using a standard gas cylinder. Conclusions: The present findings suggest that membrane oxygenation may represent a simple and effective refinement of the Langendorff isolated heart model. Beyond improving myocardial preservation, it enables the use of protein-enriched perfusates and substantially reduces gas consumption. These findings support the incorporation of membrane oxygenation into modern Langendorff systems and provide a foundation for the development of more physiologically relevant isolated organ perfusion models.

Background: Rule-based multi-agent system (MAS) architectures for healthcare coordination rely on hardcoded decision trees that cannot generalise to novel clinical scenarios or self-correct reasoning errors. These limitations are acute in surgical continuum care, where patients traverse presurgical risk stratification, intraoperative monitoring, postsurgical ICU, ward care, and remote rehabilitation over days to weeks—a complexity no fixed-policy agent architecture can address without prohibitive rule engineering. Objective: We present the first agentic large language model (LLM) framework for autonomous end-to-end surgical continuum monitoring, superseding the prior rule-based MAS Digital Twin. Six ReAct-driven tool-use agents replace fixed-policy agents with dynamic reasoning, multi-hop evidence retrieval, and Reflexion self-correction while maintaining mandatory confidence-gated Human-in-the-Loop (HITL) gating at every care-pathway-modifying decision. Methods: The framework is grounded in the ReAct paradigm and Reflexion self-evaluation, embedded within the DETER Digital Twin state engine S(t). Each agent is specified by a ReAct loop signature, a ten-function clinical tool registry, and confidence-gated HITL escalation logic. Inter-agent coordination replaces the rule-based Priority Queue Manager with an LLM-mediated Coordination Supervisor Agent reasoning over competing resource requests. Results: The framework delivers: (i) six formally specified ReAct-loop agents with explicit tool registries and authorisation boundaries; (ii) a confidence-gated HITL architecture that reduces alert fatigue while preserving safety for ambiguous clinical scenarios; (iii) an extended conflict resolution function P(p,t,context) incorporating surgical phase and DETER deterioration trajectory gradient; (iv) Reflexion self-correction with a formal N_max = 2 termination condition and Clinical Factuality Verification Layer; and (v) a multi-phase Digital Twin state engine extending S(t) to the full surgical continuum. Conclusions: The proposed framework represents a fundamental architectural departure from rule-based clinical AI—from hardcoded policies to dynamic reasoning, from static retrieval to multi-hop tool-use chains, and from fixed escalation thresholds to confidence-gated self-evaluation—providing a formally specified, clinically deployable foundation for next-generation autonomous surgical care coordination.

Background: Cardiothoracic surgery is among the most technologically advanced and resource-intensive medical specialties, placing it at the intersection of rapid digital innovation and growing demands for environmental sustainability. Addressing these parallel pressures requires integrated strategies that reconcile clinical excellence with ecological responsibility. Methods: This narrative review synthesizes PubMed-indexed literature published over the past two decades, supplemented by relevant policy documents and guidelines. The review examines digital transformation and sustainability initiatives in cardiothoracic surgery through the lens of the twin transformation framework, which conceptualizes digitalization and sustainability as interdependent and mutually reinforcing processes. Results: Key domains of digital transformation include artificial intelligence and big data-driven decision-making, robotic and minimally invasive surgical techniques, digital twins and simulation-based training, telemedicine and remote monitoring, and interoperable electronic health records. Sustainability-related themes encompass the substantial environmental burden of operating rooms, green surgical practices, sustainable procurement, and hospital-level decarbonization strategies. Emerging evidence suggests that aligning digital technologies with sustainability objectives can improve clinical outcomes, enhance operational efficiency, and reduce environmental impact. However, current evidence is largely derived from pilot studies and single-center experiences. Conclusions: Twin transformation offers a coherent and forward-looking framework for the future evolution of cardiothoracic surgery, demonstrating that digital innovation and sustainability can be synergistic rather than competing goals. While significant challenges remain—including high implementation costs, limited long-term data, and fragmented regulatory frameworks—integrating digital health technologies with sustainable practices represents a promising pathway toward high-quality, efficient, and environmentally responsible cardiothoracic care.