from the article: "Abstract: The oral mucosa is the first immune tissue that encounters allergens upon ingestion of food. We hypothesized that the bio-accessibility of allergens at this stage may be a key determinant for sensitization. Light roasted peanut flour was suspended at various pH in buffers mimicking saliva. Protein concentrations and allergens profiles were determined in the supernatants. Peanut protein solubility was poor in the pH range between 3 and 6, while at a low pH (1.5) and at moderately high pHs (>8), it increased. In the pH range of saliva, between 6.5 and 8.5, the allergens Ara h2 and Ara h6 were readily released, whereas Ara h1 and Ara h3 were poorly released. Increasing the pH from 6.5 to 8.5 slightly increased the release of Ara h1 and Ara h3, but the recovery remained low (approximately 20%) compared to that of Ara h2 and Ara h6 (approximately 100% and 65%, respectively). This remarkable difference in the extraction kinetics suggests that Ara h2 and Ara h6 are the first allergens an individual is exposed to upon ingestion of peanut-containing food. We conclude that the peanut allergens Ara h2 and Ara h6 are quickly bio-accessible in the mouth, potentially explaining their extraordinary allergenicity."
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from the article: "Abstract: The oral mucosa is the first immune tissue that encounters allergens upon ingestion of food. We hypothesized that the bio-accessibility of allergens at this stage may be a key determinant for sensitization. Light roasted peanut flour was suspended at various pH in buffers mimicking saliva. Protein concentrations and allergens profiles were determined in the supernatants. Peanut protein solubility was poor in the pH range between 3 and 6, while at a low pH (1.5) and at moderately high pHs (>8), it increased. In the pH range of saliva, between 6.5 and 8.5, the allergens Ara h2 and Ara h6 were readily released, whereas Ara h1 and Ara h3 were poorly released. Increasing the pH from 6.5 to 8.5 slightly increased the release of Ara h1 and Ara h3, but the recovery remained low (approximately 20%) compared to that of Ara h2 and Ara h6 (approximately 100% and 65%, respectively). This remarkable difference in the extraction kinetics suggests that Ara h2 and Ara h6 are the first allergens an individual is exposed to upon ingestion of peanut-containing food. We conclude that the peanut allergens Ara h2 and Ara h6 are quickly bio-accessible in the mouth, potentially explaining their extraordinary allergenicity."
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Data is widely recognized as a potent catalyst for advancing healthcare effectiveness, increasing worker satisfaction, and mitigating healthcarecosts. The ongoing digital transformation within the healthcare sector promises to usher in a new era of flexible patient care, seamless inter-provider communication, and data-informed healthcare practices through the application of data science. However, more often than not data lacks interoperability across different healthcare institutions andare not readily available for analysis. This inability to share data leads to a higher administrative burden for healthcare providers and introduces risks when data is missing or when delays occur. Moreover, medical researchers face similar challenges in accessing medical data due to thedifficulty of extracting data from applications, a lack of standardization, and the required data transformations before it can be used for analysis. To address these complexities, a paradigm shift towards a data-centricapplication landscape is essential, where data serves as the bedrock of the healthcare infrastructure and is application agnostic.In short, a modern way to think about data in general is to go from an application driven landscape to a data driven landscape, which willallow for better interoperability and innovative healthcare solutions.
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Developing a framework that integrates Advanced Language Models into the qualitative research process.Qualitative research, vital for understanding complex phenomena, is often limited by labour-intensive data collection, transcription, and analysis processes. This hinders scalability, accessibility, and efficiency in both academic and industry contexts. As a result, insights are often delayed or incomplete, impacting decision-making, policy development, and innovation. The lack of tools to enhance accuracy and reduce human error exacerbates these challenges, particularly for projects requiring large datasets or quick iterations. Addressing these inefficiencies through AI-driven solutions like AIDA can empower researchers, enhance outcomes, and make qualitative research more inclusive, impactful, and efficient.The AIDA project enhances qualitative research by integrating AI technologies to streamline transcription, coding, and analysis processes. This innovation enables researchers to analyse larger datasets with greater efficiency and accuracy, providing faster and more comprehensive insights. By reducing manual effort and human error, AIDA empowers organisations to make informed decisions and implement evidence-based policies more effectively. Its scalability supports diverse societal and industry applications, from healthcare to market research, fostering innovation and addressing complex challenges. Ultimately, AIDA contributes to improving research quality, accessibility, and societal relevance, driving advancements across multiple sectors.
The projectThe overarching goal of DIGNITY, DIGital traNsport In and for socieTY, is to foster a sustainable, integrated and user-friendly digital travel eco-system that improves accessibility and social inclusion, along with the travel experience and daily life of all citizens. The project delves into the digital transport eco-system to grasp the full range of factors that might lead to disparities in the uptake of digitalised mobility solutions by different user groups in Europe. Analysing the digital transition from both a user and provider’s perspective, DIGNITY looks at the challenges brought about by digitalisation, to then design, test and validate the DIGNITY approach, a novel concept that seeks to become the ‘ABCs for a digital inclusive travel system’. The approach combines proven inclusive design methodologies with the principles of foresight analysis to examine how a structured involvement of all actors – local institutions, market players, interest groups and end users – can help bridge the digital gap by co-creating more inclusive mobility solutions and by formulating user-centred policy frameworks.The objectivesThe idea is to support public and private mobility providers in conceiving mainstream digital products or services that are accessible to and usable by as many people as possible, regardless of their income, social situation or age; and to help policy makers formulate long-term strategies that promote innovation in transport while responding to global social, demographic and economic changes, including the challenges of poverty and migration.The missionBy focusing on and involving end-users throughout the process of designing policies, products, or services, it is possible to reduce social exclusion while boosting new business models and social innovation. The end result that DIGNITY is aiming for is an innovative decision support tool that can help local and regional decision-makers formulate digitally inclusive policies and strategies, and digital providers design more inclusive products and services.The approachThe DIGNITY approach combines analysis with concrete actions to make digital mobility services inclusive over the long term. The approach connects users’ needs and requirements with the provision of mobility services, and at the same time connects those services to the institutional framework. It is a multi-phase process that first seeks to understand and bridge the digital gap, and then to test, evaluate and fine-tune the approach, so that it can be applied in other contexts even after the project’s end.Partners: ISINNOVA (Italy), Mobiel 21 (Belgium), Universitat Politechnica deCatalunya Spain), IZT (Germany), University of Cambridge (UK), Factualconsulting (Spain), Barcelona Regional Agencia (Spain), City of Tilburg(Netherlands), Nextbike (Germany), City of Ancona (Italy), MyCicero (Italy),Conerobus (Italy), Vlaams Gewest (Belgium)
In the coming decades, a substantial number of electric vehicle (EV) chargers need to be installed. The Dutch Climate Accord, accordingly, urges for preparation of regional-scale spatial programs with focus on transport infrastructure for three major metropolitan regions among them Amsterdam Metropolitan Area (AMA). Spatial allocation of EV chargers could be approached at two different spatial scales. At the metropolitan scale, given the inter-regional flow of cars, the EV chargers of one neighbourhood could serve visitors from other neighbourhoods during days. At the neighbourhood scale, EV chargers need to be allocated as close as possible to electricity substations, and within a walkable distance from the final destination of EV drivers during days and nights, i.e. amenities, jobs, and dwellings. This study aims to bridge the gap in the previous studies, that is dealing with only of the two scales, by conducting a two-phase study on EV infrastructure. At the first phase of the study, the necessary number of new EV chargers in 353 4-digit postcodes of AMA will be calculated. On the basis of the findings of the Phase 1, as a case study, EV chargers will be allocated at the candidate street parking locations in the Amsterdam West borough. The methods of the study are Mixed-integer nonlinear programming, accessibility and street pattern analysis. The study will be conducted on the basis of data of regional scale travel behaviour survey and the location of dwellings, existing chargers, jobs, amenities, and electricity substations.
