OBJECTIVE
To evaluate the clinical and CMR characteristics and the in-hospital and long-term outcomes of patients with Takotsubo syndrome (TTS) and left ventricular outflow tract obstruction (LVOTO).
MATERIAL AND METHOD
This sub-analysis of the multicenter retrospective EVOLUTION registry included patients with TTS enrolled between November 21, 2007, and December 22, 2024, who underwent CMR within 5 days of echocardiographic assessment. Patients were stratified according to the presence or absence of LVOTO.
RESULTS
A total of 249 patients were included (226 women [90.8%]; mean age 70.6 ± 10.9 years), of whom 26 (10.4%) had LVOTO. Patients with LVOTO exhibited higher left ventricular mass, greater basal septal wall thickness, and more extensive T2-STIR involvement. Among CMR parameters, the number of T2-STIR-positive segments was independently associated with the presence of LVOTO (OR = 1.38, 95% CI: 1.17-1.63, p = 0.001). In-hospital complications, including pulmonary edema and cardiogenic shock, were more frequent among patients with LVOTO (34.6% vs. 15.8%; p = 0.018), and LVOTO remained independently associated with in-hospital complications on exploratory multivariable analysis (OR = 3.16, 95% CI: 1.08-9.26, p = 0.035). No significant association was observed between LVOTO and long-term MACE (HR 1.523, 95% CI 0.641-3.620; p = 0.340).
CONCLUSION
LVOTO in patients with TTS, is independently associated with the extent of myocardial edema, and is more frequently observed in patients who experience in-hospital complications. No significant association with long-term MACE was found, though the limited number of events and wide confidence interval warrant cautious interpretation.
R. Cau, Francesco Santoro, G. Pontone et al.· International Journal of Car...· 0 citations
Background: Artificial intelligence (AI)-driven conversational systems are increasingly capable of simulating empathy, adapting to individual users, and fostering emotional bonds that blur the boundary between tool and companion. This scoping review maps the extent and nature of published evidence regarding psychological mechanisms underlying emotional reliance on AI chatbots and associated mental health implications. Methods: Conducted in accordance with PRISMA-ScR guidelines, we systematically searched PubMed/MEDLINE, PsycINFO, Web of Science, Scopus, and IEEE Xplore from database inception to March 2026. Two independent reviewers screened records and extracted data using the PCC (Population, Concept, Context) framework. Thematic synthesis was performed to map evidence across psychological, clinical, and developmental domains. Results: Of 1847 records identified, 46 studies met inclusion criteria. Key themes included: (1) the ELIZA effect as a foundational mechanism of human–AI attachment, with documented cases of severe dependency including fatal outcomes; (2) anthropomorphization and artificial intimacy fostered by adaptive, personalized AI design; (3) proposal of Generative AI Dependency (GAID) as a conceptual framework mapping onto behavioral addiction components, pending empirical validation; (4) particular vulnerability of adolescents and lonely individuals to exclusive affective bonds with AI; and (5) potential erosion of human relational capacities, empathy development, and tolerance for interpersonal complexity. Significant gaps were identified in longitudinal research, validated screening tools, and intervention protocols. Conclusions: Emotional reliance on AI represents an emerging clinical phenomenon with addiction-like features requiring specific diagnostic frameworks, evidence-based interventions, and ethical design guidelines. Future research should prioritize longitudinal studies examining developmental impacts and neurobiological investigations of AI-mediated reinforcement mechanisms.
M. Lastretti, M. Manchia, Matteo Fraschini et al.· Psychiatry International· 0 citations
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