Search Results (1,158)

In an era where artificial intelligence is rapidly expanding into creative domains, the challenge of modeling human-like cognition and emotion in generative processes becomes increasingly central. The present study was made in connection with the exhibition of Munch’s works held in Rome from February to June 2025. Indeed, the paper introduces the concept of a Cognitive Digital Twin grounded in the Super Time-Cognitive Neural Network (STCNN) framework and applies it to the case of Edvard Munch, the iconic Norwegian expressionist. The proposed system—Munch Digital Twin—goes beyond static generative models by integrating temporal, emotional, and cognitive dimensions through a complex-valued time representation t = a + i·b, where a denotes chronological time and b encodes imagination, memory, and creativity. We define Edge Knowledge as an output-stage re-ranking criterion that admits a generated response only where corpus evidence, knowledge-graph constraints and the LLM surface jointly agree (the boundary, or ‘edge’, between documented identity and machine inference). STCNN allows this twin to process real inputs (text, visual prompts, emotional cues) and generate outputs that reflect both the rational and expressive styles of Munch. The imaginary components of the network enable speculative and affective expansions of known artworks—such as reinterpreting The Scream under new emotional or social contexts. This paper presents the theoretical underpinnings of cognitive digital twins, the architecture of the STCNN-based model, and a prototype implementation trained on Munch’s paintings, letters, and critical essays. The system—comprising a GPT-4-Turbo cloud profile and a 4-bit LLaMA-2-13B edge profile for language, Stable Diffusion 1.5 + LoRA for image generation, a Neo4j knowledge graph, and FAISS retrieval—is trained on approximately 600 letters, 100 artworks, and Munch’s diaries and criticism, and evaluated across 100 interactive sessions with 14 students and expert raters. Headline results against an unconditioned baseline include CLIPScore +13.8%, FID −25.5% (small-sample, indicative), and emotion-cosine similarity +44.9%. Ethical implications surrounding posthumous digital emulation, authorship, and emotional manipulation are also discussed. The Munch Digital Twin represents a new paradigm in AI-driven art, where machines do not merely replicate, but collaborate across time with human legacies, enabling an anticipatory and emotionally intelligent form of computational creativity. This work is primarily a conceptual and architectural contribution, supported by a proof-of-concept prototype and a preliminary, non-controlled user study; the quantitative results are indicative and not yet confirmatory. Full article

This paper argues that art and aesthetic engagement are not cultural luxuries but structural necessities for any cognitive system operating in post-scarcity information environments—a claim derived from a unified structural theory of complex systems grounded in the persistence principle. The argument proceeds in three stages. First, drawing on the formal theory of structural viability, the paper demonstrates that when a cognitive agent’s basic needs are satisfied and information is abundant, the agent’s viability set expands until utilitarian goal structures degenerate. Non-utilitarian goal-setting—creative activity pursued beyond direct utility—remains the only structurally available mode of purposeful agency. Second, the paper provides a neuroaesthetic grounding for this claim: predictive processing, derived as the unique optimal cognitive architecture under physical constraints, entails that creative engagement generates prediction errors whose resolution activates reward pathways shared with reproductive behavior—both being instances of generating new structure. Active perception of art constitutes co-creation through the same mechanism. Third, the paper addresses the role of artificial intelligence: AI accelerates the arrival of post-scarcity conditions and serves as an instrument of creative engagement, but passive consumption of AI-generated content without the agent’s own goal-setting fails to fulfill the structural function of art and deepens the very condition it could remedy. The framework contributes to neuroaesthetics by providing a substrate-independent formal foundation for understanding why cognitive systems require art—not merely enjoy it—and why this requirement intensifies as post-scarcity conditions approach. Full article

The term ‘dementia’ encompasses a diverse group of progressive neurodegenerative disorders, the common feature of which is the deterioration of higher cortical functions. This process not only involves memory deficits and language communication disorders, but also executive dysfunction and loss of emotional control, which ultimately leads to a complete loss of the patient’s independence. Within this group of disorders, Alzheimer’s disease (AD) presents the most serious clinical challenge, characterized by a unique neuropathological triad: the presence of extracellular β-amyloid plaques, intracellular neurofibrillary tangles of tau protein, and widespread dysfunction of cholinergic transmission. The cholinergic hypothesis remains the cornerstone of the current understanding of cognitive impairment in AD. It posits that progressive dementia is caused by the selective degeneration of neurons in the anterior basal forebrain, resulting in a drastic reduction in acetylcholine (ACh) levels in the synaptic cleft. In the absence of a causal treatment, acetylcholinesterase inhibitors (AChEIs) remain the standard of care. Their pharmacological action is based on the inhibition of the AChE enzyme, which allows neurotransmission deficits to be compensated for by prolonging the half-life of acetylcholine at the synapse. This literature review presents a synthesis of the efficacy and safety of classic and novel AChEIs. A comprehensive search of the PubMed, Scopus, and Cochrane Library databases was conducted for clinical data published up to 2026. Evidence from key trials indicates that standard AChEIs induce significant cognitive stabilization compared to placebo, with rivastigmine maximizing daily living parameters via transdermal delivery. However, their therapeutic impact remains strictly symptomatic without arresting neurodegeneration. Conversely, emerging agents like huperzine A and the translation-blocker Posiphen demonstrate disease-modifying potential by modulating CSF biomarkers associated with amyloid and tau proteins. Clinically, while traditional regimens are limited by gastrointestinal toxicities, transitioning toward innovative multi-target structures represents a necessary shift to address both cognitive decline and neurodegeneration. Full article

Chronic pain is increasingly understood as a multidimensional condition in which, in a substantial subgroup of patients, a protective symptom can evolve into a persistent maladaptive disorder of the nervous system, while in others it may remain closely tied to ongoing mechanical or structural factors. Central sensitization (CS) represents a key mechanism underlying this transition, characterized by enhanced neural responsiveness and impaired endogenous pain inhibition, leading to a dissociation between pain and tissue pathology. The aim of this narrative review is to critically discuss current evidence on CS as a mechanism-based explanation for persistent pain, using lumbar disk herniation (LDH) as a clinical model of the radiological-clinical mismatch, and to discuss its direct implications for identifying sensitized phenotypes, multimodal assessment, and rehabilitation strategies. A total of 77 sources published between 2006 and 2026 were synthesized. These reviewed sources demonstrate that identification of the sensitized phenotype requires a multimodal assessment approach combining self-report measures, such as the Central Sensitization Inventory (CSI), with psychophysical methods including quantitative sensory testing (QST) and conditioned pain modulation (CPM). Cognitive-emotional factors are also critical, as postoperative kinesiophobia affects approximately 38.3% of LDH patients and is associated with increased pain intensity and reduced self-efficacy. Management strategies reported in these publications focus on mechanism-based interventions, particularly pain neuroscience education (PNE) and graded, time-contingent exercise, which aim to modify pain-related cognitions and restore endogenous inhibitory processes. These approaches may be supported by adjunctive therapies, including dry needling (DN), electro-dry needling (EDN), centrally acting pharmacological agents (e.g., serotonin–norepinephrine reuptake inhibitors [SNRIs] and gabapentinoids), and psychologically informed treatments such as cognitive behavioral therapy (CBT). While surgical decompression may reduce CS-related symptoms, preoperative sensitization does not necessarily predict poorer outcomes, highlighting the interaction between peripheral and central mechanisms. Adopting a sensitization-informed perspective may encourage a broader integration of contemporary pain models alongside traditional structural views in lumbar disc herniation clinical care. Full article

Objectives: Neuroinflammation is recognized as a significant characteristic of Alzheimer’s disease (AD). Currently, there is a notable absence of effective pharmacological agents to prevent or treat neuroinflammatory processes associated with AD. Heat shock protein 70 (HSP70) is pivotal in the progression of neuroinflammation. In this study, we explored the potential of maltol, a Maillard reaction product derived from red ginseng, as a therapeutic agent for neuroinflammation. Methods: In vitro, HMC3 microglial cell models were developed to examine the regulatory effects of gradient concentrations of maltol (12.5, 25, 50 μM) on the TLR4/MyD88/NF-κB p65 signaling pathway, neuroinflammation, and pyroptosis. Analyses of the GEO database and Gene Set Enrichment Analysis (GSEA) were performed to identify the core targets of maltol, followed by HSP70 gene silencing experiments to validate the targeted regulatory mechanism. Results: Maltol significantly mitigated LPS-induced neuronal damage and cognitive deficits in mice. It effectively suppressed microglia-mediated neuroinflammation and pyroptosis, reversed oxidative stress-induced neuronal ferroptosis, and inhibited neuronal apoptosis. In vitro experiments demonstrated that maltol obstructed TLR4/MyD88 binding, thereby inhibiting NF-κB p65-mediated neuroinflammation and pyroptosis, while also alleviating excessive ROS accumulation to enhance oxidative stress and ferroptosis. Bioinformatics analysis identified HSP70 as a crucial target for the anti-inflammatory and antioxidant effects of maltol. Subsequent gene silencing experiments confirmed that maltol exerted its inhibitory effects on LPS-induced neuroinflammation and pyroptosis in an HSP70-dependent manner. Conclusions: Maltol exhibits significant protective effects against Alzheimer’s disease-related neuroinflammation, oxidative stress, pyroptosis, and ferroptosis through the targeting of HSP70. This study elucidates the molecular mechanisms by which maltol improves neuroinflammatory injury and provides a novel theoretical foundation and therapeutic strategy for the intervention of Alzheimer’s disease neuroinflammation using traditional Chinese medicine. Full article

[d=LE]Transitions of control between automated driving systems and human drivers remain safety-relevant and cognitively demanding moments in human–automation interaction. Recent studies show that transition performance depends not only on takeover timing or response speed but also on traffic complexity, driver readiness, automation limitations, trust calibration, and situational-awareness recovery. As in-vehicle interaction evolves toward conversational and agentic AI assistance, takeover support also becomes a problem of governing how natural-language AI systems communicate with the driver under uncertainty.Transitions of control between automated driving systems and human drivers remain safety-relevant and cognitively demanding moments in human-automation interaction. Recent studies suggest that transition performance should not be assessed only through takeover timing or response speed since control resumption quality also depends on traffic complexity, driver readiness, automation limitations, and situational awareness recovery. [d=LE]This paper proposes a digital-twin-mediated framework for human-aware takeover support in automated driving. In this framework, the companion AI is treated as an assumed LLM-based in-vehicle conversational or agentic assistant used as an advisory interaction component. The contribution is defined at the architectural level: human, vehicle, and context/road digital twins provide structured semantic state abstractions through a semantic state interface exposing confidence, freshness, provenance, and consistency metadata, while a trustworthy companion AI (TCAI) layer grounds, constrains, validates, and governs companion AI output proposals before HMI delivery.This paper motivates and defines a trustworthy companion AI (TCAI) layer for human-aware transition support in automated driving. The TCAI is conceived as a bounded, supervised, and explainable advisory agent that supports the driver without entering the safety-critical vehicle-control loop. It reasons over structured semantic state abstractions derived from a human digital twin, a vehicle digital twin, and a context/road digital twin, exposing driver readiness, automation capability, and contextual urgency in a form that supports traceable, uncertainty-aware, and degradation-aware assistance. [d=LE]Building on the research on driver-state monitoring, adaptive HMI, trust calibration, explainability, conversational assistance, and human assistance systems (HASs), the framework coordinates advisory interaction across vigilance support, contextual explanation, trust-calibrating communication, and directive handover guidance. The TCAI layer combines bounded reasoning, human-factor-derived guardrails, state-consistency management, dynamic explanation-depth control, trust-dynamics modeling, graded watchdog veto handling, mandatory access-control assumptions, and deterministic fallback. Safety-critical vehicle-control and minimum risk condition (MRC) functions remain assigned to the deterministic vehicle-control stack, while the authorized output path of the TCAI layer is validated HMI delivery.Building on the research on driver-state monitoring, adaptive HMI, trust calibration, explainability, and conversational assistance, we propose a conceptual architecture in which the TCAI coordinates multimodal assistance across different interaction conditions, including vigilance support, contextual explanation, trust-calibrating communication, and directive handover guidance. The companion does not actuate the vehicle; its outputs are constrained by runtime governance, policy enforcement, and deterministic fallback mechanisms. [d=LE]The paper concludes with a validation agenda and technical roadmap covering planned transitions, urgent handovers, degraded or adversarial conditions, temporal fusion of driver-state evidence, phase-sensitive HMI policies, trust-calibration trajectories, driver veto and partial-disabling mechanisms, and staged simulator-to-vehicle evaluation. Although motivated by SAE Level 3 automation, the framework may also inform fallback-related Level 4 scenarios in which human and automated agency must be managed under uncertainty.The paper concludes with a research roadmap for validating the proposed architecture under planned transitions, urgent handovers, and degraded or adversarial conditions. Although motivated by SAE Level 3 automation, the approach may also inform fallback-related Level 4 scenarios. Full article

Background: Anti-amyloid monoclonal antibodies represent the first disease-modifying therapeutic strategy targeting amyloid-β pathology in early Alzheimer’s disease (AD). Although several agents have demonstrated the ability to reduce cerebral amyloid burden, their clinical efficacy and safety remain subjects of substantial scientific and regulatory debate. This study aimed to synthesize randomized evidence evaluating the benefit–risk profile of anti-amyloid monoclonal antibodies in biomarker-confirmed early AD. Methods: A systematic review and classical pairwise meta-analysis of randomized controlled trials (RCTs) was conducted following the PRISMA 2020 guidelines. PubMed/MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials were searched for phase III placebo-controlled trials evaluating lecanemab, donanemab, aducanumab, and gantenerumab in patients with mild cognitive impairment due to AD or mild AD dementia with biomarker confirmation of amyloid pathology. The primary outcome was change from baseline in the Clinical Dementia Rating–Sum of Boxes (CDR-SB) at the longest available follow-up. Safety outcomes included amyloid-related imaging abnormalities with edema or effusion (ARIA-E), amyloid-related imaging abnormalities with hemorrhage (ARIA-H), serious adverse events, and treatment discontinuation. Random-effects meta-analyses were performed. Results: Six randomized comparisons derived from four phase III trials involving 7695 participants met the eligibility criteria. Anti-amyloid monoclonal antibodies were associated with a statistically significant slowing of clinical progression compared with placebo (pooled mean difference in CDR-SB: −0.42 points; 95% CI −0.59 to −0.25; I² = 78%). The observed effect was primarily driven by trials of lecanemab and donanemab, whereas aducanumab demonstrated discordant results across trials and gantenerumab showed no clinically meaningful benefit. Despite statistical significance, the magnitude of the pooled effect approached the lower boundary of the minimal clinically important difference reported for CDR-SB in early AD. Treatment was associated with a markedly increased risk of ARIA-E (pooled risk ratio 10.1; 95% CI 7.8–13.0), with moderate heterogeneity across studies. Most ARIA-E events were asymptomatic and detected through protocol-mandated MRI monitoring. Conclusions: In biomarker-confirmed early Alzheimer’s disease, anti-amyloid monoclonal antibodies produce a statistically significant but modest slowing of clinical decline accompanied by a substantially increased risk of ARIA. The benefit–risk profile appears heterogeneous across individual antibodies and may reflect pharmacological differences in amyloid targeting and clearance mechanisms. These findings support cautious, individualized use of anti-amyloid therapies and highlight the need for longer-term studies to determine whether short-term slowing of decline translates into clinically meaningful disease modification. Full article

This study examines the development and pedagogical impact of an AI-based pedagogical agent designed for modern e-learning environments. The research addresses a key challenge in digital education: the lack of personalization and immediate feedback in traditional e-learning systems. AI-driven agents “support and motivate learners through instructional interaction” and provide adaptive, data-driven learning experiences that surpass the limitations of rule-based systems. The study begins with a systematic literature review following PRISMA 2020, analyzing 46 publications from 2020 to 2025 to identify current AI architectures, pedagogical roles, and the empirical evidence of learning impact. The findings highlight the growing use of machine learning, deep learning, multimodal analytics, and large language models in educational agents. These systems perform roles such as tutor, coach, evaluator, dialogue partner, and consultant, offering cognitive, metacognitive, emotional, and analytical support. Modern agents “continuously monitor user interaction, analyze engagement, and adapt learning content”, enabling highly personalized learning pathways. The study also presents the design of a multimodal pedagogical agent capable of explanation, task generation, diagnostics, and adaptive feedback. Experimental results with students (n = 20) show improved performance, reduced errors, and higher engagement when learning with the agent. Overall, the research demonstrates that AI-based pedagogical agents enhance learning effectiveness and support autonomous learning in higher education. Full article

Background and Objectives: With the rising number of geriatric surgical patients, postoperative cognitive dysfunction (POCD) has become a major concern, linked to impairments in memory, attention, and executive function. POCD increases morbidity, prolongs hospitalization, and diminishes quality of life. This review examines the mechanisms underlying POCD, with emphasis on neuroinflammation, blood–brain barrier (BBB) disruption, and oxidative stress, and evaluates the impact of anesthetic techniques on cognitive outcomes in the elderly. Materials and Methods: This narrative review used a targeted literature search to identify relevant clinical, translational, and mechanistic evidence on POCD in older surgical patients. The evidence was synthesized qualitatively, with attention to heterogeneity in study populations, anesthetic techniques, cognitive assessment methods, and follow-up duration. Results: Neuroinflammation, BBB compromise, oxidative stress, perioperative stress responses, and patient vulnerability appear to contribute to POCD. Evidence comparing anesthetic techniques remains heterogeneous. Some studies suggest associations between general anesthesia, volatile agents, and early postoperative cognitive changes, whereas other comparative and randomized studies do not demonstrate consistent long-term cognitive differences between general, regional, neuraxial, volatile, and intravenous anesthetic approaches. Regional and neuraxial techniques may reduce anesthetic or opioid exposure in selected patients, but they should not be interpreted as definitively superior for POCD prevention. Adjunctive and multimodal strategies, including dexmedetomidine and non-opioid analgesics, show potential benefits, although evidence remains variable. Conclusions: Individualized anesthetic planning, early risk stratification, avoidance of excessive anesthetic depth, hemodynamic optimization, multimodal analgesia, and postoperative recovery strategies may help reduce modifiable contributors to POCD. Current evidence does not support a definitive hierarchy of anesthetic techniques for preventing POCD, and further high-quality studies are needed. Full article

Purpose-aware AI systems are increasingly deployed in safety-critical, multi-agent, and human-facing environments, where they must transform heterogeneous data into timely, explainable, and goal-aligned decisions under uncertainty. Existing architectures often couple perception, reasoning, communication, and security only at the pipeline level. This creates a research gap in unified semantic transformation, purpose-oriented judgment, bounded imperfection handling, and semantic self-protection. To address this gap, this paper proposes a DIKWP+BUG semantic–cognitive reference architecture for artificial-consciousness-oriented computing, without claiming definitive artificial consciousness. The architecture represents cognition through the Data–Information–Knowledge–Wisdom–Purpose (DIKWP) model and uses BUG theory to model bounded approximation, incomplete evidence, and confidence miscalibration in cross-dimensional reasoning. The model is mapped to an Artificial Consciousness Processing Unit (ACPU) reference substrate, an Artificial Consciousness Operating System (ACOS), a DIKWP semantic communication subsystem, and a concept–semantic fused security subsystem. The components are implemented through runtime emulation and evaluated in smart-city governance, autonomous-driving, and medical-triage simulations. Compared with selected baselines, the prototype increased cognitive throughput from $4.5\mathrm{k}$ to $7.8\mathrm{k}$ logged events, reduced perception–action latency from $340\mathrm{ms}$ to $120\mathrm{ms}$, reduced CPU utilization from $95\%$ to $68\%$, lowered smart-city congestion duration by $30\%$, improved emergency response time by approximately $40\%$, achieved 0 collisions versus approximately $2/10$ baseline IoV runs, and improved medical-triage accuracy from $85\%$ to $92\%$. These online-runtime results provide initial feasibility evidence under controlled simulation conditions; they do not include offline model-preparation costs and therefore should not be interpreted as end-to-end lifecycle speedups. Matched-compute ablation, statistical benchmarking, hardware prototyping, and real-world validation remain future work. Full article

This contribution examines the role of artificial intelligence technologies in the co-construction of social reality, with specific attention to AI-generated data as emergent agents of knowledge production. Building on perspectives from science and technology studies and recent debates on algomorphic sociology, the contribution conceptualizes generative AI systems not as research instruments, but as active participants in epistemic processes. The analysis argues that AI-generated data exhibit a performative character: they do not simply represent social phenomena but actively contribute to their stabilization, classification, and circulation. This performativity fosters a shift from researcher-centered interpretation toward hybrid configurations in which meaning emerges through human–machine assemblages. Through a theoretical synthesis of recent methodological and epistemological reflections, the contribution highlights a transition from anthropocentric models of knowledge production to post-anthropocentric, relational frameworks in which agency, cognition, and sense-making are distributed across sociotechnical networks. The contribution concludes by outlining the implications of this shift for the future of digital social research and also for reflexivity, methodological design, and the ethics of social research, advocating a critical and adaptive stance toward AI as a co-producer of knowledge rather than a subordinate analytical tool. Full article

Resource allocation for cognitive Vehicle-to-Everything (V2X) networks is challenging due to dynamic spectrum sharing, strong interference coupling, and stringent latency constraints for safety-critical Vehicle-to-Vehicle (V2V) traffic. Although recent Multi-Agent Reinforcement Learning (MARL) approaches report promising gains, many evaluations are conducted at limited and fixed network scales, which restricts insights into scalability under dense spectrum reuse. This paper investigates cooperative multi-agent learning for interference-aware and deadline-constrained V2X resource management. We propose a Q-value Transformation (QTRAN)-based value decomposition framework under centralized training with decentralized execution (CTDE) for joint resource-block and power allocation among V2V agents. The proposed approach is implemented in a realistic V2V/V2I simulator incorporating Manhattan grid mobility, fast fading, explicit cross-tier and co-channel interference, and per-link payload/deadline dynamics. Beyond communication-level performance, improved timely delivery of V2V safety messages can support cooperative maneuvering, collision avoidance, platooning, and infrastructure-assisted traffic management. Extensive simulations across varying numbers of V2V agents benchmark QTRAN against independent learning baselines including MARL and centralized single-agent learning (SARL). Results show that QTRAN improves performance compared with the selected learning baselines and enhances the throughput–reliability trade-off under interference-coupled spectrum reuse. For instance, at $N_{\mathrm{V}2\mathrm{V}}=20$, QTRAN achieves a V2V rate of $0.194\pm 0.004$ and a V2I rate of $9.117\pm 0.213$, while reaching a V2V success rate of $0.812\pm 0.017$ with a low Deadline Miss Ratio of $0.001\pm 0.000$. At higher density ($N_{\mathrm{V}2\mathrm{V}}=50$), QTRAN sustains strong reliability (V2V success rate of $0.719\pm 0.006$ and Completion Ratio of $0.716\pm 0.006$) while maintaining competitive infrastructure throughput (V2I rate of $9.251\pm 0.114$). These results indicate that QTRAN effectively captures non-linear interference interactions, enabling coordinated decentralized spectrum and power decisions under the adopted density-based evaluation setting, thereby enhancing V2V reliability and throughput in cognitive Internet of Vehicles. Full article

The increasing integration of generative artificial intelligence (AI) into teacher education and pedagogical supervision requires explanatory frameworks capable of clarifying how AI-generated feedback may support professional learning processes. Existing research has predominantly focused on technological adoption, implementation challenges, and user perceptions, while comparatively limited attention has been devoted to the cognitive and reflective mechanisms involved in AI-supported pedagogical supervision. In response to this gap, this article proposes a theory-building conceptual framework explaining how AI-supported pedagogical supervision may influence pedagogical decision-making through sequential mechanisms involving feedback quality, cognitive processing, and reflective practice. Drawing on feedback theory, Cognitive Load Theory, reflective practice literature, and distributed cognition perspectives, the proposed framework conceptualises AI not as a direct instructional agent, but as a support system embedded within professional pedagogical reasoning processes. To facilitate future empirical investigation, the article proposes a validation framework based on covariance-based Structural Equation Modelling (CB-SEM). This methodological specification is intended solely as a research agenda for subsequent studies and does not constitute empirical testing of the model. As a conceptual contribution, the article advances a theoretically integrated explanation of how AI-generated feedback may influence professional learning processes. By articulating feedback quality, cognitive processing, reflective practice, and pedagogical decision-making within a coherent framework, it offers a foundation for future empirical research and theory development in AI-supported pedagogical supervision. Full article

Background and Objectives: Quality of life (QoL) has become an essential outcome in patients with metastatic prostate cancer, particularly in the era of androgen receptor (AR)-targeted therapies. Although these agents improve survival, their differential impact on QoL and the role of psychological factors remain incompletely understood. This study aimed to evaluate QoL, functional outcomes, and psychological status, and to identify factors associated with poor QoL in a real-world cohort. Materials and Methods: This prospective cross-sectional, single-center observational study included 130 patients with metastatic prostate cancer receiving AR-targeted therapies (abiraterone, enzalutamide, or apalutamide/darolutamide). QoL was assessed using the EORTC QLQ-C30 questionnaire, and psychological status was evaluated using the Hospital Anxiety and Depression Scale (HADS). Patients were stratified according to treatment groups, and comparisons were performed using appropriate statistical tests. Logistic regression analyses were conducted to determine factors independently associated with poor QoL. Results: Exploratory differences in global QoL were observed among treatment groups (p = 0.007), with lower global QoL scores in the abiraterone group and numerically higher emotional and cognitive functioning scores in the enzalutamide group. Symptom analysis demonstrated higher nausea/vomiting scores in the abiraterone group (p = 0.022), whereas other symptom domains were comparable across treatment groups. In multivariable analysis, anxiety (odds ratio [OR]: 6.62) and depression (OR: 3.40) were independently associated with poor QoL, while treatment type was not independently associated with poor QoL after multivariable adjustment. Conclusions: Although unadjusted QoL scores differed across AR-targeted therapy groups, psychological factors—particularly anxiety and depression—were significantly associated with poorer QoL in patients with metastatic prostate cancer. These findings highlight the importance of integrating routine psychosocial assessment and supportive care strategies into clinical practice to optimize patient-centered outcomes. However, given the cross-sectional and exploratory nature of the study, the findings should be interpreted cautiously. Full article

I present a quantum-tunnelling oscillator model as a universal dynamical engine for two paradigmatic problems in quantum cognition theory—optical illusion perception and group decision making—where individuals are treated as quantum-mechanical agents whose choices shift through context-dependent transitions rather than simple probabilities. I show that, when networked together, these units form a quantum-cognitive neural system that reproduces familiar collective and perceptual phenomena while naturally accommodating counterintuitive processes that challenge classical models. Bridging ideas from quantum cognition theory and neural networks, this approach offers a compact, physically grounded way to describe how real individuals and groups think, perceive and decide. Full article