In Western countries, diseases related to foods represent a major issue in public poliy. Overweight and obesity are increasing at an alarming rate in the world and in Europe more particularly. Obesity is one of the most serious public health problems because it increases significantly the risk of many chronic diseases such as cardiovascular disease and type 2 diabete. Nutrition is a major health determinant and is one of the key priorities in public health policy, especially in Europe. The comsumption of cereals-based foods with low glycemic indexes, high micronutrients and fibers contents are highly recommended. The target of this work, is to provide new solutions for cereal based foods: the knowledge and understanding on the in vivo fate will be used to define structural features to gain in foods. The objective of this proposal is to use new genetic resources and to assess the role of the role of viscosity on gastric emptying and the kinetic aspects of starch digestion. The digestibility of starch in foods varies widely and can be affected by high content of viscous soluble dietary fiber constituents and relatively high amylose / amylopectine ratios. Amylose content also influences some functional properties of starches like swelling power, solubility, in vitro glycemic index and viscosity. Thus, the strategy of this work is based upon the complementarity of the research teams and upon the integration of various scientific disciplines, from genetics of wheat grain to the human subject while passing by in vitro and animal studies. Natural biodiversity present in a core collection of bread wheat (Clermont-Ferrand) will be examined in order to bring out new wheat varieties containing high amylose contents. These varieties will be selected by the use of molecular and biochemical markers, by phenotyping using a new experimental device based upon image analysis of seeds sections, by biochemical analyses and by nutritional investigations. Viscosity of the ingested meal, gastric function in vivo and the nutritive impact of cereal products with high amylose content will be evaluated using an artificial stomach, a porcine model and a human panel.

Each year in Europe, more than 300,000 newborns are born prematurely. Among them, very premature babies have a 10-20% risk of contracting late neonatal infections, with a high risk of mortality or neurobehavioural sequelae. However, the ability to quickly and accurately diagnose the risk of infection in premature newborns based on clinical assessment and laboratory blood tests remains a challenge. In a personalized and precision medicine approach, the proposed project aims to validate and deploy a new non-invasive medical decision support system for early diagnosis of infection in premature newborns. It aims to prepare a proposal for the call H2020 SC1-BHC-06-2020: Digital diagnostics - developing tools for supporting clinical decisions by integrating various diagnostic data from WP2020. Digi-Sepsis uses artificial intelligence methods to detect early infections. It builds on the experience and major progress made in the previous H2020 Digi-NewB project (www.digi-newb.eu) which ends in February 2020. This project resulted in the development of a proof of concept based on a multi-source database of more than 400 patients, an interface validated by ergonomic experts with the nursing staff, a validated and operational architecture for a randomized study, by carrying out a first phase of tests in hospital/neonatology with monitoring of the risk of sepsis. This approach aims to reduce morbidity and mortality associated with late neonatal infections. It requires continuous, real-time measurement of a set of clinical (medical record) and physiological (signal processing) variables and their association with recent and developing biological tools ("-omic", immunological biomarkers and PCR). The Digi-Sepsis project aims to meet the following specific objectives: The issues that will be addressed in the present study are: (i) To propose a definition of the early phase of sepsis which is necessary to evaluate the accuracy and predictive value of the proposed approach. (ii) To evaluate the accuracy of the proposed multi-dimensional approach in terms of sensitivity, specificity, predictive values and significance for the clinicians (iii) To demonstrate a benefice for the patients in the use of AI for the early diagnosis of infection before the occurrence of established sepsis, (iv) To evaluate the perception of the proposed approach by patients, parents and health care givers. In the long term, it is expected that new therapeutic strategies will be developed that combine early treatment with a reduction in the inappropriate use of antibiotics.

Hypertrophic cardiomyopathy (HCM) is the most common inherited heart disease (prevalence 1:200 - 1:500), manifested by thickening of cardiac walls, increasing risks of arrhythmia, and sudden cardiac death. HCM affects all ages - it is the leading cause of death among young athletes. Comorbidities due to gene mutations include altered vascular control, and, caused by HCM, ischemia, stroke, dementia, or psychological and social difficulties. Multiple causal mutations and variations in cellular processes lead to highly diverse phenotypes and disease progression. However, HCM is still diagnosed as one single disease, leading to suboptimal care. SMASH-HCM will develop a digital-twin platform to dramatically improve HCM stratification and disease management, both for clinicians and patients. Multilevel and multiorgan dynamic biophysical and data-driven models are integrated in a three-level deep phenotyping approach designed for fast uptake into the clinical workflow. SMASH-HCM unites 8 research partners, 3 hospitals, 3 SMEs, and a global health-technology corporation in collaboration with patients to advance the state of the art in human digital-twins: including in-vitro tools, in-silico from molecular to systemic level models, structured and unstructured data analysis, explainable artificial intelligence - all integrated into a decision support solution for both healthcare professionals and patients. SMASH-HCM delivers new insights into HCM, improved patient care and guidance, validated preclinical tools, and above all, a first HCM stratification and management strategy, validated in a pilot clinical trial, and tested with end users. Thus providing a cost efficient and effective solution for this complex disease. SMASH-HCM develops a strategy towards fast regulatory approval. In reaching its goals, SMASH-HCM serves as a basis for future digital-twin platforms for other cardiac diseases integrating models and data from various scales and sources.