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Objectives: To incorporate a longitudinal palliative care curriculum into obstetrics and gynecology (Ob-Gyn) residency that could become standardized to ensure competencies in providing end of life (EOL) care.

Methods: This was a prospective cohort study conducted among 23 Ob-Gyn residents at a tertiary training hospital from 2021 to 2022. A curriculum intervention was provided via lecture and simulation.

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Infertility is increasing globally, affecting one in six adults due to factors like delayed childbearing and lifestyle changes. Despite the recognition of the importance of increasing fertility awareness, levels remain low. This study evaluated the perceptions of 'FActs!', a serious game aimed at improving adolescents' fertility awareness.

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Background: The aim of the present study was to investigate the willingness of elderly individuals regarding their choice of elderly care modes in underdeveloped regions of Western China and to identify the key factors influencing the willingness.

Methods: We distributed a total of 20 000 questionnaires using the multistage stratified cluster random sampling method, and successfully collected 19 460 of them. After conducting quality checks, we deemed 19 040 questionnaires valid for analysis.

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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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