Adverse Outcome Pathways (AOPs) are conceptual frameworks that tie an initial perturbation (molecular initiat- ing event) to a phenotypic toxicological manifestation (adverse outcome), through a series of steps (key events). They provide therefore a standardized way to map and organize toxicological mechanistic information. As such, AOPs inform on key events underlying toxicity, thus supporting the development of New Approach Methodologies (NAMs), which aim to reduce the use of animal testing for toxicology purposes. However, the establishment of a novel AOP relies on the gathering of multiple streams of evidence and infor- mation, from available literature to knowledge databases. Often, this information is in the form of free text, also called unstructured text, which is not immediately digestible by a computer. This information is thus both tedious and increasingly time-consuming to process manually with the growing volume of data available. The advance- ment of machine learning provides alternative solutions to this challenge. To extract and organize information from relevant sources, it seems valuable to employ deep learning Natural Language Processing techniques. We review here some of the recent progress in the NLP field, and show how these techniques have already demonstrated value in the biomedical and toxicology areas. We also propose an approach to efficiently and reliably extract and combine relevant toxicological information from text. This data can be used to map underlying mechanisms that lead to toxicological effects and start building quantitative models, in particular AOPs, ultimately allowing animal-free human-based hazard and risk assessment.
Abstract Managing adverse drug reactions (ADRs) is a challenge, especially because most healthcare professionals are insufficiently trained for this task. Since context-based clinical pharmacovigilance training has proven effective, we assessed the feasibility and effect of a creating a team of Junior-Adverse Drug Event Managers (J-ADEMs). The J-ADEM team consisted of medical students (1st–6th year) tasked with managing and reporting ADRs in hospitalized patients. Feasibility was evaluated using questionnaires. Student competence in reporting ADRs was evaluated using a case-control design and questionnaires before and after J-ADEM program participation. From Augustus 2018 to Augustus 2019, 41 students participated in a J-ADEM team and screened 136 patients and submitted 65 ADRs reports to the Netherlands Pharmacovigilance Center Lareb. Almost all patients (n = 61) found it important that “their” ADR was reported, and all (n = 62) patients felt they were taken seriously by the J-ADEM team. Although attending physicians agreed that the ADRs should have been reported, they did not do so themselves mainly because of a “lack of knowledge and attitudes” (50%) and “excuses made by healthcare professionals” (49%). J-ADEM team students were significantly more competent than control students in managing ADRs and correctly applying all steps for diagnosing ADRs (control group 38.5% vs. intervention group 83.3%, p < 0.001). The J-ADEM team is a feasible approach for detecting and managing ADRs in hospital. Patients were satisfied with the care provided, physicians were supported in their ADR reporting obligations, and students acquired relevant basic and clinical pharmacovigilance skills and knowledge, making it a win-win-win intervention.
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Background: Dental fear and uncooperative behavior can hinder dental treatment quality. Pediatric Procedural Sedation and Analgesia (PPSA) is used to facilitate treatment when the coping capacity is exceeded. Out-of-hospital PPSA has been associated with more adverse outcomes compared to when it is used in hospital-based settings. The updated Dutch PPSA guidelines have increased costs and raised concerns about the accessibility of specialized high-quality dental care for children in the Netherlands. This study aimed to investigate the impact of the updated 2017 guidelines on the occurrence rate of adverse events during PPSA in twelve Dutch dental clinics. Methods: The data of 25,872 children who were treated at twelve dental clinics between 1997 and 2019 were analyzed. A logistic two-level mixed-effects model was used to estimate the updated guidelines’ impacts on adverse events. Results: The OR of the occurrence rate of an adverse event adjusted for age, weight, and duration of treatment was 0.75 (95% CI 0.64–0.89) after the implementation of the updated guidelines. This outcome was significant with p = 0.001, indicating a protective effect. Conclusions: Our findings demonstrate that there was a significant reduction in adverse events after the implementation of the updated guideline and highlight the importance of adhering to evidence-based practices in out-of-hospital dental clinics.
Movebite aims to combat the issue of sedentary behavior prevalent among office workers. A recent report of the Nederlandse Sportraad reveal a concerning trend of increased sitting time among Dutch employees, leading to a myriad of musculoskeletal discomforts and significant health costs for employers due to increased sick leave. Recognizing the critical importance of addressing prolonged sitting in the workplace, Movebite has developed an innovative concept leveraging cutting-edge technology to provide a solution. The Movebite app seamlessly integrates into workplace platforms such as Microsoft Teams and Slack, offering a user-friendly interface to incorporate movement into their daily routines. Through scalable AI coaching and real-time movement feedback, Movebite assists individuals in scheduling and implementing active micro-breaks throughout the workday, thereby mitigating the adverse effects of sedentary behavior. In collaboration with the Avans research group Equal Chance on Healthy Choices, Movebite conducts user-centered testing to refine its offerings and ensure maximum efficacy. This includes testing initiatives at sports events, where the diverse crowd provides invaluable feedback to fine-tune the app's features and user experience. The testing process encompasses both quantitative and qualitative approaches based on the Health Belief Model. Through digital questionnaires, Movebite aims to gauge users' perceptions of sitting as a health threat and the potential benefits of using the app to alleviate associated risks. Additionally, semi-structured interviews delve deeper into user experiences, providing qualitative insights into the app's usability, look, and feel. By this, Movebite aims to not only understand the factors influencing adoption but also to tailor its interventions effectively. Ultimately, the goal is to create an environment encouraging individuals to embrace physical activity in small, manageable increments, thereby fostering long-term engagement promoting overall well-being.Through continuous innovation and collaboration with research partners, Movebite remains committed to empowering individuals to lead healthier, more active lifestyles, one micro-break at a time.
