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Introduction: To target psychological support to cancer patients most in need of support, screening for psychological distress has been advocated and, in some settings, also implemented. Still, no prior studies have examined the appropriate 'dosage' and whether screening for distress before cancer treatment may be sufficient or if further screenings during treatment are necessary. We examined the development in symptom trajectories for breast cancer patients with low distress before surgery and explored potential risk factors for developing burdensome symptoms at a later point in time.

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Background: Point-of-care ultrasound (POCUS) can be used in a variety of clinical settings and is a safe and powerful tool for ultrasound-trained healthcare providers, such as physicians and nurses; however, the effectiveness of ultrasound education for nursing students remains unclear. This prospective cohort study aimed to examine the sustained educational impact of bladder ultrasound simulation among nursing students.

Methods: To determine whether bladder POCUS simulation exercises sustainably improve the clinical proficiency regarding ultrasound examinations among nursing students, evaluations were conducted before and after the exercise and were compared with those after the 1-month follow-up exercise.

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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'.

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Metabolic syndrome (Mets) in adolescents is a growing public health issue linked to obesity, hypertension, and insulin resistance, increasing risks of cardiovascular disease and mental health problems. Early detection and intervention are crucial but often hindered by complex diagnostic requirements. This study aims to develop a predictive model using NHANES data, excluding biochemical indicators, to provide a simple, cost-effective tool for large-scale, non-medical screening and early prevention of adolescent MetS.

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Exploring the potential of advanced artificial intelligence technology in predicting microsatellite instability (MSI) and Ki-67 expression of endometrial cancer (EC) is highly significant. This study aimed to develop a novel hybrid radiomics approach integrating multiparametric magnetic resonance imaging (MRI), deep learning, and multichannel image analysis for predicting MSI and Ki-67 status. A retrospective study included 156 EC patients who were subsequently categorized into MSI and Ki-67 groups.

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