From the publisher: "Background: The introduction of whole new foods in a population may lead to sensitization and food allergy. This constitutes a potential public health problem and a challenge to risk assessors and managers as the existing understanding of the pathophysiological processes and the currently available biological tools for prediction of the risk for food allergy development and the severity of the reaction are not sufficient. There is a substantial body of in vivo and in vitro data describing molecular and cellular events potentially involved in food sensitization. However, these events have not been organized in a sequence of related events that is plausible to result in sensitization, and useful to challenge current hypotheses. The aim of this manuscript was to collect and structure the current mechanistic understanding of sensitization induction to food proteins by applying the concept of adverse outcome pathway (AOP). Main body: The proposed AOP for food sensitization is based on information on molecular and cellular mechanisms and pathways evidenced to be involved in sensitization by food and food proteins and uses the AOPs for chemical skin sensitization and respiratory sensitization induction as templates. Available mechanistic data on protein respiratory sensitization were included to fill out gaps in the understanding of how proteins may affect cells, cell-cell interactions and tissue homeostasis. Analysis revealed several key events (KE) and biomarkers that may have potential use in testing and assessment of proteins for their sensitizing potential. Conclusion: The application of the AOP concept to structure mechanistic in vivo and in vitro knowledge has made it possible to identify a number of methods, each addressing a specific KE, that provide information about the food allergenic potential of new proteins. When applied in the context of an integrated strategy these methods may reduce, if not replace, current animal testing approaches. The proposed AOP will be shared at the www.aopwiki.org platform to expand the mechanistic data, improve the confidence in each of the proposed KE and key event relations (KERs), and allow for the identification of new, or refinement of established KE and KERs." Authors: Jolanda H. M. van BilsenEmail author, Edyta Sienkiewicz-Szłapka, Daniel Lozano-Ojalvo, Linette E. M. Willemsen, Celia M. Antunes, Elena Molina, Joost J. Smit, Barbara Wróblewska, Harry J. Wichers, Edward F. Knol, Gregory S. Ladics, Raymond H. H. Pieters, Sandra Denery-Papini, Yvonne M. Vissers, Simona L. Bavaro, Colette Larré, Kitty C. M. Verhoeckx and Erwin L. Roggen
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Cervical spinal manipulation (CSM) and cervical mobilization are frequently used in patients with neck pain and headache. Pre-manipulative cervical instability and arterial integrity tests appear to be unreliable in identifying patients at risk for adverse events. It would be valuable if patients at risk could be identified by specific characteristics during the preliminary screening.Objective was to identify characteristics of 1) patients, 2) practitioners, 3) treatment process and 4) adverse events (AE) occurring after CSM or cervical mobilization.A systematic search was performed in PubMed, Embase, CINAHL, Web-of-science, AMED, and ICL (Index Chiropractic Literature) up to December 2014.Of the initial 1043 studies, 144 studies were included, containing 227 cases. 117 cases described male patients with a mean age of 45 (SD 12) and a mean age of 39 (SD 11) for females. Most patients were treated by chiropractors (66%). Manipulation was reported in 95% of the cases, and neck pain was the most frequent indication. Cervical arterial dissection (CAD) was reported in 57% (P = 0.21) of the cases and 45.8% had immediate onset symptoms. The overall distribution of gender for CAD is 55% (n = 71) for female and therefore opposite of the total AE.Patient characteristics were described poorly. No clear patient profile, related to the risk of AE after CSM, could be extracted. However, women seem more at risk for CAD. There seems to be under-reporting of cases. Further research should focus on a more uniform and complete registration of AE using standardized terminology.
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.
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.
