Atherosclerosis is a chronic inflammatory disease that is promoted, among others, by pro-inflammatory cytokines such as IL-1β and IL-18 produced by NLRP 3 inflammasome. Development of atherosclerotic lesions is also affected by leptin. Furthermore, inflammasome’s action is interfered with other inflammatory diseases, like diabetes. On the other hand, colchicine is reported to act as anti-inflammatory agent inhibiting inflammasome’s action and stabilizing atherosclerotic lesions. The purpose of this study is to investigate the effect of per os colchicine on the de novo formation of atherosclerotic lesions and on the levels of IL-18, leptin and insulin in cholesterol-fed rabbits. Twenty-three male, 2 months old New Zealand White rabbits, were seperated in 3 groups and were fed with different types of diet for 7 weeks: standard, cholesterol 1% w/w and cholesterol 1% w/w plus colchicine 2 mg/kg body weight. Blood was collected for biochemical measurements and conduction of ELISA for leptin, IL-18 and insulin. Histologic examination of stained with eosin and hematoxylin aorta specimens was performed. Aortic intimal thickness was evaluated using image analysis. The statistical analysis included non-parametric tests: a) paired-sample Wilcoxon test, b) Spearman correlation coefficient and c) Kruscal-Wallis test. Triglerycide levels were decreased in cholesterol plus colchicine group in the end of the experiment (p < 0.05), whereas the cholesterol group had increased levels. No statistical differences were observed in the levels of IL-18, leptin and insulin between groups. Likewise, there was neither any correlation between IL-18, leptin and intima thickness nor between IL-18 and glucose and between leptin and weight. In cholesterol and colchicine group there was a strong positive correlation between IL-18 and insulin levels in the 4th week (r s = .66, n = 10, p < 0.05), whereas in the 7th week this correlation became strong negative (r s = −.86, n = 10, p < 0.05). Finally, intima thickness in the ascending and thoracic aorta of the cholesterol and colchicine group was significantly greater than that of the other groups (p < 0.05). Per os administration of colchicine did not influence atherosclerosis progression in cholesterol-fed rabbits, levels of IL-18, insulin and leptin. We encountered the attenuating role of colchicine on TG levels.

Minimally invasive cardiac surgery: in the pursuit to treat more and hurt less Dimos Karangelis, Vasiliki Androutsopoulou, Aphrodite Tzifa, George Chalikias, Dimitrios Tziakas, Fotis Mitropoulos, Dimitrios Mikroulis

Heart failure (HF) poses a substantial global burden due to its high morbidity, mortality, and healthcare costs. Accurate prognostication is crucial for optimizing treatment, resource allocation, and patient counseling. Prognostic tools range from simple clinical scores such as ADHERE and MAGGIC to more complex models incorporating biomarkers (e.g., NT-proBNP, sST2), imaging, and artificial intelligence techniques. In acute HF, models like EHMRG and STRATIFY aid early triage, while in chronic HF, tools like SHFM and BCN Bio-HF support long-term management decisions. Despite their utility, most models are limited by poor generalizability, reliance on static inputs, lack of integration into electronic health records, and underuse in clinical practice. Novel approaches involving machine learning, multi-omics profiling, and remote monitoring hold promise for dynamic and individualized risk assessment. However, these innovations face challenges regarding interpretability, validation, and ethical implementation. For prognostic models to transition from theoretical promise to practical impact, they must be continuously updated, externally validated, and seamlessly embedded into clinical workflows. This review emphasizes the potential of prognostic models to transform HF care but cautions against uncritical adoption without robust evidence and practical integration. In the evolving landscape of HF management, prognostic models represent a hopeful avenue, provided their limitations are acknowledged and addressed through interdisciplinary collaboration and patient-centered innovation.

Artificial intelligence (AI) is rapidly reshaping adult cardiac surgery, enabling more accurate diagnostics, personalized risk assessment, advanced surgical planning, and proactive postoperative care. Preoperatively, deep-learning interpretation of ECGs, automated CT/MRI segmentation, and video-based echocardiography improve early disease detection and refine risk stratification beyond conventional tools such as EuroSCORE II and the STS calculator. AI-driven 3D reconstruction, virtual simulation, and augmented-reality platforms enhance planning for structural heart and aortic procedures by optimizing device selection and anticipating complications. Intraoperatively, AI augments robotic precision, stabilizes instrument motion, identifies anatomy through computer vision, and predicts hemodynamic instability via real-time waveform analytics. Integration of the Hypotension Prediction Index into perioperative pathways has already demonstrated reductions in ventilation duration and improved hemodynamic control. Postoperatively, machine-learning early-warning systems and physiologic waveform models predict acute kidney injury, low-cardiac-output syndrome, respiratory failure, and sepsis hours before clinical deterioration, while emerging closed-loop control and remote monitoring tools extend individualized management into the recovery phase. Despite these advances, current evidence is limited by retrospective study designs, heterogeneous datasets, variable transparency, and regulatory and workflow barriers. Nonetheless, rapid progress in multimodal foundation models, digital twins, hybrid OR ecosystems, and semi-autonomous robotics signals a transition toward increasingly precise, predictive, and personalized cardiac surgical care. With rigorous validation and thoughtful implementation, AI has the potential to substantially improve safety, decision-making, and outcomes across the entire cardiac surgical continuum.

Lymph node evaluation is a central determinant of oncologic quality in the surgical management of non-small-cell lung cancer (NSCLC). Accurate assessment of hilar and mediastinal lymph nodes underpins pathologic staging, informs postoperative treatment decisions, and remains essential for prognostic stratification and assessment of resection completeness. Although international guidelines provide clear recommendations, real-world data consistently demonstrate substantial variability in lymph node staging practices, with inadequate evaluation frequently observed across institutions and surgical settings. Insufficient nodal assessment, manifested as the omission of mediastinal staging, limited station sampling, or low lymph node yield, is associated with reduced nodal upstaging, inappropriate omission of adjuvant therapy, higher recurrence rates, and inferior long-term survival. Contemporary guidance from major societies, including the National Comprehensive Cancer Network, European Society of Thoracic Surgeons, International Association for the Study of Lung Cancer, and the Commission on Cancer, has increasingly converged on a station-based definition of adequacy, emphasizing systematic evaluation of both N1 and N2 nodal stations rather than reliance on absolute node counts alone. In parallel, preoperative mediastinal staging algorithms have evolved toward routine use of endobronchial and esophageal ultrasound as first-line invasive modalities, reserving surgical mediastinoscopy for selected high-risk or inconclusive cases. Evidence from randomized trials, population-level databases, and meta-analyses indicates that thorough nodal assessment improves staging accuracy and survival, while recent data support the selective use of lobe-specific or tailored lymphadenectomy in carefully staged, low-risk early disease. Finally, emerging quality improvement interventions, including standardized specimen handling, operative checklists, and multidisciplinary feedback mechanisms, have demonstrated measurable improvements in guideline adherence and patient outcomes. This narrative review integrates contemporary evidence and guideline recommendations to outline a practical framework for implementing reliable, high-quality lymph node staging in modern lung cancer surgery.

Failure to rescue (FTR), defined as death following a potentially treatable postoperative complication, has emerged as a critical quality metric in thoracic surgery. In patients undergoing lung cancer resection, who are often at high risk due to comorbidities and limited pulmonary reserve, FTR significantly influences morbidity, mortality, recovery, and overall quality of life. This review explores the multifactorial nature of FTR in lung cancer surgery, highlighting key patient-related and system-level risk factors, such as surgical complexity, delayed complication recognition, inadequate escalation of care, and limited critical care resources. Existing models for patient rescue emphasize early detection and timely intervention, but often overlook the institutional and cultural changes required for sustainable improvement. Building on current evidence and integrating Kotter’s eight-step change model, we propose a novel multidimensional roadmap to reduce FTR through proactive monitoring, structured escalation protocols, multidisciplinary coordination, and continuous learning. Finally, reducing FTR in lung cancer resection requires more than clinical responsiveness. This necessitates a systemic transformation that aligns frontline practice with institutional readiness and a culture of safety.