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Objectives: Explore humanitarian healthcare professionals' (HCPs) perceptions about implementing children's palliative care and to identify their educational needs and challenges, including learning topics, training methods, and barriers to education.

Methods: Humanitarian HCPs were interviewed about perspectives on children's palliative care and preferences and needs for training. Interviews were transcribed, coded, and arranged into overarching themes.

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Abstinence self-efficacy, belief in one's ability to abstain, has been identified as a predictor of substance use behavior change. Yet, many people who use substances do not want to abstain. Self-efficacy for achieving a range of goals (i.

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Background: Early anal canal cancer is frequently treated with endoscopic submucosal dissection (ESD) to preserve anal function. However, if the lesion is in the anal canal, then significant difficulties such as bleeding and challenges associated with scope manipulation can arise.

Case Summary: A 70-year-old woman undergoing follow-up after transverse colon cancer surgery was diagnosed with anal canal cancer extending to the dentate line.

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Introduction: This study aimed to investigate the current level of knowledge about lung cancer among urban residents in Sichuan Province and to assess its influence on their willingness to choose county-level or lower-level medical institutions for cancer screening.

Methods: A total of 31,184 urban residents of Sichuan Province were included in the cross-sectional study. Binary logistic regression and propensity score matching (PSM) were used to assess the influence effect.

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Introduction: Cyber situational awareness is critical for detecting and mitigating cybersecurity threats in real-time. This study introduces a comprehensive methodology that integrates the Isolation Forest and autoencoder algorithms, Structured Threat Information Expression (STIX) implementation, and ontology development to enhance cybersecurity threat detection and intelligence. The Isolation Forest algorithm excels in anomaly detection in high-dimensional datasets, while autoencoders provide nonlinear detection capabilities and adaptive feature learning.

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