Conectar
by Lin Zhang, Feng Luo, Yalin Chai, Lijie Sun, Xuan Wang, Le Yin, Congjuan Luo
Acute kidney injury (AKI) remains a major clinical challenge due to its high morbidity and mortality, with ischemia-reperfusion injury (IRI) as one of its primary causes. Severe IRI-associated AKI (IRI-AKI) can progress to irreversible renal failure, yet no effective therapies are currently available. Ferroptosis, an iron-dependent regulated cell death, has recently been implicated in the pathogenesis of IRI-AKI. Moreover, IL-22 may alleviate AKI by modulating the ferroptosis process through regulation of the P62-Keap1-Nrf2 signaling axis. In this study, we examined the protective role of the immune cytokine interleukin-22 (IL-22) in IRI-AKI and its mechanistic association with ferroptosis. Using a murine IRI model and an HK-2 cell hypoxia/reoxygenation system, we systematically assessed the impact of IL-22 treatment. IL-22 administration significantly enhanced renal function, reduced histological injury, and limited both reactive oxygen species accumulation and ferroptotic cell death. Further mechanistic studies demonstrated that IL-22 suppresses ferroptosis in vitro through an Nrf2-dependent mechanism and is associated with activation of the P62-Keap1-Nrf2 signaling pathway. These findings offer experimental evidence supporting IL-22 as a potential therapy for IRI-AKI and highlight ferroptosis modulation as a promising therapeutic strategy.The aim of this study was to innovatively utilise the BERTopic model for topic modelling in order to comprehensively identify and understand the factors contributing to bed falls.
Retrospective study.
The study collected 241 reports of bed fall accidents recorded by nurses from Peking University Third Hospital Nursing Department from 2014 to 2024. Among them, 102 reports met the inclusion and exclusion criteria.
This study follows the Minimum Information for Medical AI Reporting (MINIMAR). It collected patient bed fall reports from Peking University Third Hospital between 2014 and June 2024, preprocessed the texts, utilised the BERTopic library in Python for topic modelling, and manually aggregated secondary topics by combining visualisation results and professional knowledge.
We utilised cluster bar charts to visually display the distribution of the 22 secondary topics and further consolidated them into five core topics through the use of a topic distribution diagram and a topic similarity matrix diagram. These topics were related to patient factors, ward equipment and surroundings factors, medication risk factors, caregiver factors, and nursing practice factors. The study highlights the environment's specificity in bed falls, especially bedside safety and patient-bed rail interaction.
The innovation of this study lies in the successful utilisation of BERTopic technology to identify topics of risk factors for bed falls through alternative data sources, providing a scientific basis for formulating preventive measures. The findings aim to optimise nursing processes, improve ward environments and enhance educational training, ultimately reducing patient bed falls and enhancing medical safety, nursing quality and patient experience.
This study not only helps nurses identify risk factors for patient bed falls, but also provides important guidance for developing effective prevention strategies.
No patient or public contribution applied.
To explore determinants impacting an Electronic Health Record-based information system implementation and their association with implementation fidelity based on the Theoretical Domains Framework (TDF) from nurses' perspectives.
Exploratory sequential mixed-method design.
In stage one, semi-structured interviews with 53 purposively selected nurses informed the exploration of TDF domains influencing the implementation of the information system with directed content analysis. In stage two, a cross-sectional survey, informed by the qualitative findings, was conducted among 482 nurses to identify the most relevant and relatively important TDF domains by running generalised linear regression models.
The qualitative interviews generated 13 TDF domains that were identified as major influencing factors, including technology characteristics, knowledge, attitudes, role agreement, self-efficacy, goal-setting, information circulation, and communication among nurses. Quantitative findings showed that 70% of nurses used and printed the written form through the information system, and only 34% offered verbal education consistently. Regression analysis identified nine domains that were relevant and important factors for implementation fidelity, including knowledge, skills, role identity, beliefs in consequences, beliefs in capabilities, intentions, goals, memory and decision processes, and environmental context.
Our findings confirmed previous evidence on determinants of implementing digital health technologies, including knowledge, competencies, perceived effectiveness, role agreement, intentions, decision processes, and environmental context. Additionally, we highlighted the importance of goal-setting for successful implementation.
This study investigated the relatively important associated factors that can impact the successful implementation of the nurse-led information system for post-acute care based on nurses' perspectives. These results can guide nurse practitioners in implementing similar initiatives and support evidence-based decision-making. Researchers can also further investigate the relationships between the identified determinants.
Journal Article Reporting Standards for Mixed Methods Research.
No patient or public contribution.
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