Background: Tetranectin (CLEC3B), a plasminogen-binding protein involved in fibrinolysis and tissue remodeling, has been increasingly studied as a potential diagnostic and prognostic biomarker in cardiovascular disease (CVD). This review synthesizes current evidence on its clinical utility across heart failure (HF), coronary artery disease (CAD), and related conditions. Objectives: To systematically evaluate and synthesize published clinical evidence on the diagnostic and prognostic value of tetranectin in cardiovascular diseases. Methods: A systematic search of PubMed, Google Scholar, and Scopus (January 2010–June 2025) identified original human studies examining associations between tetranectin (CLEC3B) and cardiovascular diseases, including heart failure, coronary artery disease, myocardial infarction, and cardiometabolic conditions. Eligible studies included adult cohorts with observational designs; experimental, in vitro, and pediatric studies were excluded. Two reviewers independently extracted data on study design, population characteristics, biomarker assessment, and outcomes, resolving discrepancies by consensus. Results: Twelve studies were included. Tetranectin levels were consistently lower in patients with CAD, MI, and advanced HF compared to controls. Higher circulating TN levels were associated with reduced risk of HF onset, cardiovascular death, and hospitalization. In two studies, combining tetranectin with NT-proBNP improved diagnostic accuracy over NT-proBNP alone. Mechanistic studies revealed correlations between TN expression and fibrosis-related gene pathways, supporting its biological relevance. Conclusions: Tetranectin shows consistent promise as a diagnostic and prognostic biomarker in cardiovascular disease, particularly in heart failure and coronary artery disease. Its involvement in fibrotic remodeling, plasminogen activation, and vascular homeostasis underlines biological pathways relevance. Combining tetranectin with established biomarkers may improve cardiovascular risk stratification and guide more personalized therapeutic strategies. Further large-scale and longitudinal studies are needed to validate its clinical utility across diverse settings.

Background/Objectives: As artificial intelligence systems become increasingly embedded in everyday cognitive tasks, human–AI interaction is no longer limited to tool use but evolves into a dynamic process of mutual adaptation. While extensive research has examined algorithmic learning, far less attention has been given to how users progressively adapt to AI systems. This paper introduces the concept of algorithmic habituation, defined as the gradual accommodation of users to the regularities and predictive patterns of AI systems. The objective is to provide a neurocognitive and systems-based framework that explains this phenomenon. Methods: The study develops a conceptual and integrative framework grounded in classical theories of habituation, neuroplasticity, predictive processing, and systems theory. Building on these foundations, we propose a mechanistic model of human–AI co-adaptation, conceptualized as a recursive feedback loop involving repeated interaction, pattern recognition, expectation stabilization, and cognitive economy. In addition, a typology of algorithmic habituation is advanced, alongside proposed empirical pathways for future validation, including scale development, experimental paradigms, and longitudinal designs. Results: The proposed framework suggests that repeated interaction with AI systems leads to stabilization of cognitive expectations, reduced cognitive effort, and increased behavioral standardization. This process extends beyond perceptual habituation into higher-order domains, including decision-making, creativity, and moral judgment. The typology identifies four primary forms of algorithmic habituation: cognitive, decisional, creative, and moral. The model predicts both adaptive outcomes (efficiency, reduced cognitive load) and maladaptive consequences (reduced reflexivity, automation bias, and potential erosion of critical thinking). Conclusions: Algorithmic habituation represents a novel construct at the intersection of neuroscience, cognitive psychology, and human–AI interaction. By framing user adaptation as a form of neurocognitively grounded habituation within recursive systems, this paper contributes a new perspective to understanding AI integration in human cognition. The framework has implications for digital wellbeing, education, and AI ethics, and opens multiple avenues for empirical research.

The transmission of trauma across generations has traditionally been explained through genetic, epigenetic, familial, and attachment-based mechanisms. While these approaches have generated valuable insights, they remain insufficient for explaining the persistence of psychological vulnerability in contexts where the original traumatic event has disappeared but maladaptive patterns continue to emerge across generations. This paper introduces the Contextual Resonance Theory of Psychopathology (CRTP), a novel cognitive–systemic framework proposing that transgenerational trauma is maintained through the recursive reproduction of trauma-shaped environments rather than exclusively through biological inheritance. According to this model, traumatic experiences generate adaptive cognitive, emotional, and behavioral responses that become embedded within family narratives, relational structures, institutional practices, and cultural expectations. Over time, these adaptations evolve into self-maintaining contextual systems that continuously communicate threat, unpredictability, and hypervigilance to subsequent generations. The theory introduces the concepts of contextual traumatic inertia, distributed psychopathology, and recursive contextual resonance to explain how vulnerability becomes socially reproduced even in the absence of direct trauma exposure. Drawing on predictive processing, ecological psychology, systems theory, and contemporary trauma research, the paper proposes that psychopathology emerges from persistent interactions between individuals and pathogenic environments rather than solely from intrapsychic dysfunction. Furthermore, a complementary mechanism termed corrective recontextualization is proposed as a pathway for reversing transgenerational vulnerability through repeated exposure to safe, predictable, and psychologically restorative contexts. The framework offers an integrative perspective capable of bridging cognitive neuroscience, developmental psychopathology, trauma studies, and mental health intervention research. Clinical, educational, organizational, and societal implications are discussed, alongside future directions for empirical validation and neurocognitive investigation.