Conectar
by Shili Yang, Huaiquan Liu, Haiyang Kou, Lingyan Lai, Xinyan Zhang, Yunling Xu, Yu Sun, Bo Chen
Ochratoxin A (OTA), a food-borne mycotoxin, has been implicated in hepatotoxicity and potential carcinogenic processes, yet the molecular links between OTA exposure and hepatocellular carcinoma (HCC) remain incompletely understood. This study used an integrated computational workflow to prioritize candidate targets and pathways potentially linking OTA exposure with HCC. OTA-related and HCC-related targets were collected from public databases, intersected, and subjected to functional enrichment analysis. Transcriptomic data from the GSE36376 discovery dataset were analyzed to identify differentially expressed genes, followed by LASSO and SVM-RFE feature selection, immune-cell deconvolution, molecular docking, and molecular dynamics simulation. A total of 214 overlapping OTA-HCC-associated targets were identified and were enriched in pathways related to signal transduction, apoptosis, metabolism, and immune regulation. In GSE36376, 443 differentially expressed genes were identified using p 1, and overlap analysis yielded 13 shared target genes. Five candidate targets, CYP3A4, KIFC1, AKR1C3, CA2, and TTR, were further prioritized. KIFC1 and AKR1C3 were upregulated in HCC samples, whereas CYP3A4, CA2, and TTR were downregulated. These genes showed apparent discriminatory ability within the discovery dataset, with AUC values ranging from 0.866 to 0.958. Molecular docking predicted favorable OTA-target interactions, with docking energies ranging from −7.4 to −10.8 kcal/mol. CYP3A4 showed the lowest predicted docking energy (−10.8 kcal/mol) and was further evaluated by molecular dynamics simulation, with a protein-fitted OTA RMSD of 1.435 ± 0.097 nm and complex Rg of 2.308 ± 0.010 nm during the equilibrated 20–100 ns trajectory. Overall, this study provides a reproducible hypothesis-generating framework for exploring potential metabolic, genomic-instability-related, and immune-microenvironment links between OTA exposure and HCC. Future validation in independent datasets and experimental models will be important to further assess the biological relevance of these candidate targets and pathways.To examine the relationship among leadership, clinical teaching competencies, and structural empowerment of nursing clinical instructors in China.
A cross-sectional study.
A total of 152 nurses who come from three Grade A tertiary hospitals located in Beijing, Kunming, and Liaoning Province, China, completed an online questionnaire that included general information, clinical teaching information, the Conditions of Work Effectiveness Questionnaire-II, nurse leadership, and structural empowerment. SPSS 26.0 and AMOS 26.0 were used for normality test, descriptive statistics, correlation analysis, regression analysis, and structural equation model.
The study revealed that nurse leadership (r = 0.402) and structural empowerment (r = 0.568) both positively correlated with clinical teaching competencies. Specifically, the level of nurse leadership exhibited a low but direct positive effect on these competencies (β = 0.22), while the level of structural empowerment demonstrated a moderate direct positive effect (β = 0.56).
Enhancing nurse leadership and structural empowerment positively influence the clinical teaching competencies of nursing instructors.
Constructing a structural equation model to describe the relationship between leadership, structural empowerment, and teaching ability can provide the most intuitive direction for future research, so as to better improve the teaching ability of clinical nursing teachers.
No patient or public contribution.
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.
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