OBJECTIVE: The prediction of sepsis, especially early diagnosis, has received a significant attention in biomedical research. In order to improve current medical scoring system and overcome the limitations of class imbalance and sample size of local EHR (electronic health records), we propose a novel knowledge-transfer-based approach, which combines a medical scoring system and an ordinal logistic regression model.
METHODS: Medical scoring systems (i.e. NEWS, SIRS and QSOFA) are generally robust and useful for sepsis diagnosis. With local EHR, machine-learning-based methods have been widely used for building prediction models/methods, but they are often impacted by class imbalance and sample size. Knowledge distillation and knowledge transfer have recently been proposed as a combination approach for improving the prediction performance and model generalization. In this study, we developed a novel knowledge-transfer-based method for combining a medical scoring system (after a proposed score transformation) and an ordinal logistic regression model. We mathematically confirmed that it was equivalent to a specific form of the weighted regression. Furthermore, we theoretically explored its effectiveness in the scenario of class imbalance.
RESULTS: For the local dataset and the MIMIC-IV dataset, the VUS (the volume under the multi-dimensional ROC surface, a generalization measure of AUC-ROC for ordinal categories) of the knowledge-transfer-based model (ORNEWS) based on the NEWS scoring system were 0.384 and 0.339, respectively, while the VUS of the traditional ordinal regression model (OR) were 0.352 and 0.322, respectively. Consistent analysis results were also observed for the knowledge-transfer-based models based on the SIRS/QSOFA scoring systems in the ordinal scenarios. Additionally, the predicted probabilities and the binary classification ROC curves of the knowledge-transfer-based models indicated that this approach enhanced the predicted probabilities for the minority classes while reducing the predicted probabilities for the majority classes, which improved AUCs/VUSs on imbalanced data.
CONCLUSIONS: Knowledge transfer, which combines a medical scoring system and a machine-learning-based model, improves the prediction performance for early diagnosis of sepsis, especially in the scenarios of class imbalance and limited sample size.

In the post-Covid context, which is unstable and changing, what is the role of the team when it is reduced to operating with a growing number of temporary staff? The medical desertification that is gradually taking hold in certain regions is a cause for concern, as is the growing disaffection of nurses throughout France. It is essential to draw up a map that incorporates survival oases, in order to meet the essential need to provide care anyway, while at the same time perpetuating the process of improving care. Advanced practice nursing faces many challenges.

BACKGROUND: The effective transfer of knowledge and skills is the primary goal of nursing education. Despite this, little is known about how knowledge and skills gained through international collaborative nursing educational programs is applied by nurses.
OBJECTIVE: To describe the experiences of knowledge and skills transfer among nursing graduates from an international collaborative educational program to their clinical practice and ongoing study.
METHODS: A qualitative design was employed for this study. In 2023, fifteen interviews were conducted with nursing graduates from a Chinese-Swedish collaborative nursing educational program at Lishui University in eastern China. Directed content analysis was utilized to analyze the interview data.
RESULTS: Nursing graduates gained both knowledge and skills that surpassed the educational goals of the international collaborative program. Throughout the application and transfer of knowledge and skills, participants reported both positive and negative experiences. Notably, a gap persisted between basic nursing theoretical knowledge and clinical practice. Additionally, deficiencies were identified in the transfer of nursing research knowledge, indicating areas for improvement in future nursing education.
CONCLUSIONS: The findings suggest that knowledge and skills from an international collaborative nursing educational program can be successfully transferred to clinical nursing practice and postgraduate study. However, addressing the gap between theoretical knowledge and practice, particularly in updated basic nursing knowledge and practice, is essential. Furthermore, there is a need to enhance awareness and attitudes towards nursing research among nurses, emphasizing the importance of continuous learning.

Low-dose computed tomography (LDCT) denoising tasks face significant challenges in practical imaging scenarios. Supervised methods encounter difficulties in real-world scenarios as there are no paired data for training. Moreover, when applied to datasets with varying noise patterns, these methods may experience decreased performance owing to the domain gap. Conversely, unsupervised methods do not require paired data and can be directly trained on real-world data. However, they often exhibit inferior performance compared to supervised methods. To address this issue, it is necessary to leverage the strengths of these supervised and unsupervised methods. In this paper, we propose a novel domain adaptive noise reduction framework (DANRF), which integrates both knowledge transfer and style generalization learning to effectively tackle the domain gap problem. Specifically, an iterative knowledge transfer method with knowledge distillation is selected to train the target model using unlabeled target data and a pre-trained source model trained with paired simulation data. Meanwhile, we introduce the mean teacher mechanism to update the source model, enabling it to adapt to the target domain. Furthermore, an iterative style generalization learning process is also designed to enrich the style diversity of the training dataset. We evaluate the performance of our approach through experiments conducted on multi-source datasets. The results demonstrate the feasibility and effectiveness of our proposed DANRF model in multi-source LDCT image processing tasks. Given its hybrid nature, which combines the advantages of supervised and unsupervised learning, and its ability to bridge domain gaps, our approach is well-suited for improving practical low-dose CT imaging in clinical settings. Code for our proposed approach is publicly available at https://github.com/tyfeiii/DANRF.

Lifelong Machine Learning (LML) denotes a scenario involving multiple sequential tasks, each accompanied by its respective dataset, in order to solve specific learning problems. In this context, the focus of LML techniques is on utilizing already acquired knowledge to adapt to new tasks efficiently. Essentially, LML concerns about facing new tasks while exploiting the knowledge previously gathered from earlier tasks not only to help in adapting to new tasks but also to enrich the understanding of past ones. By understanding this concept, one can better grasp one of the major obstacles in LML, known as Knowledge Transfer (KT). This systematic literature review aims to explore state-of-the-art KT techniques within LML and assess the evaluation metrics and commonly utilized datasets in this field, thereby keeping the LML research community updated with the latest developments. From an initial pool of 417 articles from four distinguished databases, 30 were deemed highly pertinent for the information extraction phase. The analysis recognizes four primary KT techniques: Replay, Regularization, Parameter Isolation, and Hybrid. This study delves into the characteristics of these techniques across both neural network (NN) and non-neural network (non-NN) frameworks, highlighting their distinct advantages that have captured researchers\' interest. It was found that the majority of the studies focused on supervised learning within an NN modelling framework, particularly employing Parameter Isolation and Hybrid for KT. The paper concludes by pinpointing research opportunities, including investigating non-NN models for Replay and exploring applications outside of computer vision (CV).

BACKGROUND: Turkey has faced a notable escalation in earthquake disasters in the last two decades. Despite initiating a health and disaster management system, nurses\' pivotal roles and experiences in handling such crises have been disregarded.
OBJECTIVE: This qualitative study analyzed nurses\' experiences before, during, and after deployment in response to the 2023 Turkey earthquakes to enhance disaster-response efforts.
METHODS: This descriptive qualitative study was conducted between March and May 2023 using semistructured interviews with 15 nurses purposively  sampled among those who volunteered to work in the earthquake zone. The MIRACLE and COREQ guidelines were followed for reporting qualitative research.
RESULTS: The analysis exposed five main themes for pre-tasking: moral obligation, motivation, insufficient experience, balancing responsibilities, and preparation challenges. The peri-task themes include responsibilities, skills, bravery and characteristics, workload management, teamwork, and outcomes. Post-tasking has three themes: competence assessment, career goals and aspirations, and support. Training and coping with anxiety and stress are common themes for all phases.
CONCLUSIONS: Disaster relief requires a comprehensive and coordinated response from healthcare organizations, government agencies, and support systems. Providing adequate training, ensuring safety protocols, offering mental health support, and fostering a fair and supportive work environment are crucial steps in mitigating the adverse effects on nurses and, by extension, the patient care process in earthquake-affected areas.
CONCLUSIONS: Nurse training in disaster preparedness should cover various response methods and involve multiple disciplines. Managers can help by arranging drills, simulations, online courses, and workshops and promoting partnerships for improved collaboration. Psychological support should be included to address emotional challenges. Regularly updating response policies based on past experiences is crucial for preparedness and efficiency.

The management and governance of our surface waters is core to life and prosperity on our planet. However, monitoring data are not available to many potential users and the disparate nature of water bodies makes consistent monitoring across so many systems difficult. While satellite Earth observation (EO) offers solutions, there are numerous challenges that limit the use of satellite EO for water monitoring. To understand the perceptions of using satellite EO for water quality monitoring, a survey was conducted within academia and the water quality management sector. Study objectives were to assess community understanding of satellite EO water quality data, identify barriers in the adoption of satellite EO data, and analyse trust in satellite EO data. Most (40 %) participants were beginners with little understanding of satellite EO. Participants indicated problems with satellite EO data accessibility (31 %) and interpretability (26 %). Results showed a high level of trust with satellite EO data and higher trust with in-situ EO data. This study highlighted the gap between water science, applied social science, and policy. A transdisciplinary approach to managing water resources is needed to bridge water disciplines and take a key role in areas such as social issues, knowledge brokering, and translation.

Modern machine learning has the potential to fundamentally change the way bioprocesses are developed. In particular, horizontal knowledge transfer methods, which seek to exploit data from historical processes to facilitate process development for a new product, provide an opportunity to rethink current workflows. In this work, we first assess the potential of two knowledge transfer approaches, meta learning and one-hot encoding, in combination with Gaussian process (GP) models. We compare their performance with GPs trained only on data of the new process, that is, local models. Using simulated mammalian cell culture data, we observe that both knowledge transfer approaches exhibit test set errors that are approximately halved compared to those of the local models when two, four, or eight experiments of the new product are used for training. Subsequently, we address the question whether experiments for a new product could be designed more effectively by exploiting existing knowledge. In particular, we suggest to specifically design a few runs for the novel product to calibrate knowledge transfer models, a task that we coin calibration design. We propose a customized objective function to identify a set of calibration design runs, which exploits differences in the process evolution of historical products. In two simulated case studies, we observed that training with calibration designs yields similar test set errors compared to common design of experiments approaches. However, the former requires approximately four times fewer experiments. Overall, the results suggest that process development could be significantly streamlined when systematically carrying knowledge from one product to the next.

BACKGROUND: Evidence-based practice, in conjunction with optimum care quality, improves patients\' clinical outcomes. However, its implementation in daily clinical practice continues to present difficulties. The aim of this study was to identify the strategies applied by Advanced Practice Nurses (APNs) to foster adherence to clinical practice guideline recommendations.
METHODS: An exploratory qualitative study was conducted with six focus groups at three public hospitals belonging to the Balearic Islands Health Care Service (Spain). The study participants were 32 ward nurses and 5 advanced practice nurses working routinely with inpatients at these hospitals. The study was conducted from November 2020 to January 2021, using thematic analysis, based on the COREQ checklist.
RESULTS: Four major themes related to the facilitation process were identified either by RNs and APNs: the context of the project, APN contribution to nursing team management, healthcare provision on the ward, and the acquisition and application of knowledge.
CONCLUSIONS: The APNs adapted their actions to the characteristics and needs of the local context, employing strategies aimed at improving teamwork, healthcare, and knowledge management. Each of these contributions enhanced the sustainability of the changes made.

BACKGROUND: Pass\'Santé Jeunes (PSJ), initiated in 2013, addresses high-risk behaviors among young people in Bourgogne-Franche-Comté, France. The program, supported by the Agence Régionale de Santé and Promotion Santé Bourgogne-Franche-Comté, combines digital resources with local partnerships to promote healthy choices among youth.
OBJECTIVE: This article reviews the comprehensive health promotion approach of PSJ, aiming to identify key deployment strategies that could serve as a model for other regions or health promotion organizations.
RESULTS: PSJ offers validated health resources through a website tailored to different age groups and includes content for parents. The program employs a robust digital marketing strategy, enhancing visibility and engagement through social media. Collaborations with regional athletes have significantly increased outreach, with website traffic growing from 1,000 to 31,000 monthly visitors and social media reach exceeding 450,000 in 2023. Over 20,000 professionals have been trained or sensitized, fostering a network of local actors dedicated to youth health. The program\'s participatory and community-led strategies effectively mobilize various life environments to support health promotion.
CONCLUSIONS: PSJ exemplifies a successful regional health promotion model. Its comprehensive approach, integrating digital tools and local partnerships, addresses the complex determinants of youth health behaviors. Ongoing evaluation and adaptation are crucial to maintaining the program\'s relevance and effectiveness. Future efforts should focus on bridging regional disparities, enhancing youth engagement, and ensuring long-term support for local actors to sustain health promotion activities.