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This research identifies the main themes and internal states of heart disease experience in individuals and families of patients with cardiovascular difficulties.It is applied in terms of purpose and methodologically qualitative and phenomenological. Its population is patients admitted to Baqiyatallah Azam Hospital. It includes the patients who had a long and severe chronic illness in this area (or without long and severe chronic illness). We performed in-depth semi-structured interviews with 6 patients in the CCU ward of Baqiyatallah Al-Azam Hospital. With the introducing 4 family members of the participants, the number of people with whom we worked until the final stages reached 10 (two participants refused to introduce their family members). We analyzed data using the methods proposed by Van Manen and Colaizzi.According to the results, the main themes of experiencing heart disease are initial experience of cardiovascular (malfunction) difficulty, experience of internal conditions related to cardiovascular disease, family's initial experience of cardiovascular disease, atmosphere governing the participant's life before heart disease,  the last slices of life before experiencing cardiovascular disease, perceived changes after experiencing cardiovascular disease, and experiencing cardiovascular disease. Regarding the initial experience of cardiovascular difficulty (dysfunction), sub-themes include reported symptoms, type of diagnosis, and duration of illness.

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Hypnosis, a state of focused attention and heightened suggestibility, can be used as a therapeutic tool that yields similar effects to those of medications but without known side effects. Despite extensive fascination and study, the neural mechanisms behind hypnosis remain elusive. In the current study, we undertook a systematic exploration of these neural correlates. We first extracted well-studied neurophysiological features from EEG sensors and source-localized data using spectral analysis and two measures of functional connectivity—weighted phase lag index (wPLI) and power envelope correlation (PEC). Next, we developed classification models that predicted self-rated hypnotic experience based on the extracted feature sets. Our findings reveal that gamma power computed on sensor-level data and beta PEC computed between source-localized brain networks are the top predictors of hypnosis depth. Further SHapley Additive exPlanations (SHAP) analysis suggested reduced gamma power in the midline frontal area and increased beta PEC between interhemispheric Dorsal Attention Networks (DAN) contribute to the hypnotic experience. These results broaden our understanding of the neural mechanisms of hypnosis, highlighting potential targets for future research. Moreover, this study demonstrates the potential of using machine learning techniques in understanding the neural underpinnings of hypnosis, offering a template for future investigations. his study was registered at https://doi.org/10.17605/OSF.IO/WVHDA on 30/03/2021.