Increasing evidence supports a close link between REM sleep and the consolidation of emotionally toned memories such as traumatic experiences. In order to investigate the role of sleep for the development of symptoms related to traumatic experiences, beyond experimental models in the laboratory, sleep of acutely traumatised individuals may be examined on the first night after trauma. This might allow us to identify EEG variables predicting the development of posttraumatic stress disorder (PTSD) symptoms, and guide the way to novel sleep interventions to prevent PTSD. Based on our experience, patients' acceptance of polysomnography in the first hours after treatment in an emergency room poses obstacles to such a strategy. Wearable, self-applicable sleep recorders might be an option for the investigation of sleep in the aftermath of trauma. They would considerably decrease the perceived burden for patients and thus increase the likelihood of successful patient recruitment. As one potential sleep intervention, sleep deprivation directly after trauma has been suggested to reduce the consolidation of traumatic memories and hence act as a secondary preventive measure. However, experimental data from sleep deprivation studies in healthy volunteers with the trauma film paradigm have been inconclusive regarding the beneficial or detrimental effects of sleep on traumatic memory processing. Depending on further insights into the role of sleep in traumatic memory consolidation through observational and experimental studies, several options for therapeutic sleep interventions are conceivable: besides behavioural sleep deprivation, selective REM sleep suppression or enhancement by a pharmacological intervention into the serotonergic, noradrenergic or cholinergic systems might provide novel therapeutic options. While REM-modulating drugs have been used with some success for the prevention of PTSD after trauma, they have never been tried before the first night of sleep. In conclusion, more experimental and observational research is needed before sleep interventions are performed in actual trauma victims.
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http://dx.doi.org/10.1080/20008198.2020.1740492 | DOI Listing |
BMC Endocr Disord
January 2025
Department of Respiratory and Critical Care Medicine, Renmin Hospital of Wuhan University, Wuhan, 430060, China.
Background: Menopause is a significant phase in women's health, in which the incidence of obstructive sleep apnea (OSA) is significantly increased. Body fat distribution changes with age and hormone levels in postmenopausal women, but the extent to which changes in body fat distribution affect the occurrence of OSA is unclear.
Methods: This research performed a cross-sectional analysis utilizing data from the 2015-2016 National Health and Nutrition Examination Survey (NHANES).
BMC Public Health
January 2025
Amsterdam UMC location Vrije Universiteit Amsterdam, Public and Occupational Health, De Boelelaan 1117, Amsterdam, the Netherlands.
Background: Developing interventions along with the population of interest using systems thinking is a promising method to address the underlying system dynamics of overweight. The purpose of this study is twofold: to gain insight into the perspectives of adolescents regarding: (1) the system dynamics of energy balance-related behaviours (EBRBs) (physical activity, screen use, sleep behaviour and dietary behaviour); and (2) underlying mechanisms and overarching drivers of unhealthy EBRBs.
Methods: We conducted Participatory Action Research (PAR) to map the system dynamics of EBRBs together with adolescents aged 10-14 years old living in a lower socioeconomic, ethnically diverse neighbourhood in Amsterdam East, the Netherlands.
NPJ Digit Med
January 2025
Neurofibromatosis Type 1 Center and Laboratory for Neurofibromatosis Type 1 Research, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.
Deep-learning models have shown promise in differentiating between benign and malignant lesions. Previous studies have primarily focused on specific anatomical regions, overlooking tumors occurring throughout the body with highly heterogeneous whole-body backgrounds. Using neurofibromatosis type 1 (NF1) as an example, this study developed highly accurate MRI-based deep-learning models for the early automated screening of malignant peripheral nerve sheath tumors (MPNSTs) against complex whole-body background.
View Article and Find Full Text PDFMetabolomics
January 2025
Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Background: Gestational exposure to non-persistent endocrine-disrupting chemicals (EDCs) may be associated with adverse pregnancy outcomes. While many EDCs affect the endocrine system, their effects on endocrine-related metabolic pathways remain unclear. This study aims to explore the global metabolome changes associated with EDC biomarkers at delivery.
View Article and Find Full Text PDFNPJ Digit Med
January 2025
Graduate School of Data Science, Seoul National University, Seoul, Republic of Korea.
Polysomnography (PSG) is crucial for diagnosing sleep disorders, but manual scoring of PSG is time-consuming and subjective, leading to high variability. While machine-learning models have improved PSG scoring, their clinical use is hindered by the 'black-box' nature. In this study, we present SleepXViT, an automatic sleep staging system using Vision Transformer (ViT) that provides intuitive, consistent explanations by mimicking human 'visual scoring'.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!