Self-Reported Outcomes (SRO) such as anxiety or quality of life are frequently assessed with questionnaires to better understand the subjective experience of individuals and to inform healthcare decision-making. Faced with a major life event (e.g. COVID-19 crisis), people may change their perception of the SRO and of the questionnaire, a phenomenon called response shift (RS). If RS is not accounted for, the estimation of longitudinal changes in SRO may be biased and the clinical interpretation may be obfuscated. Furthermore, RS may provide more insight into changes in SRO in the face of a major event and may be linked with psychological adaptation. Most of RS analyses based on statistical methods consider two measurement occasions (before and after a major event). Yet, changes of SRO over time are often assessed over multiple time points. In this context, the trajectory of the construct is continuous by essence and RS could also be envisioned as a continuous process. Furthermore, the exact timing of assessments may also vary across individuals even if visits are usually planned. Thus, the timescale of the study has often to be treated as continuous rather than discrete with equally spaced time points. RS analyses also generally assume that most individuals experience RS in the same way regardless of individual characteristics. However, it is likely that RS may occur for some individuals only, in different ways and at different times. As such, trajectories of RS are likely to be heterogeneous within a sample. Last, RS detection methods most often perform RS analysis at the domain level using subscale scores combining several items. These methods cannot distinguish which items are specifically affected by RS and domain-level RS analysis might not always appropriately reflect what is going on at the item level, especially if RS has opposite effects depending on the item. Thus, item-level RS analyses would give more insight on RS and on the interpretation of psychological adaptation. Methods able to detect and adjust for item-level RS over multiple time points in studies with continuous time while accounting for heterogeneity in trajectories of RS are lacking. The RESCUE project aims to develop a method for RS analysis at the item level in longitudinal SRO studies across multiple time points. RS has never been defined nor operationalized in a continuous timescale. We plan to define RS over multiple time points in a multidisciplinary framework. Rasch models have shown good performances for item-level RS detection between two time points with heterogeneous RS. RS over multiple time points will be operationalized in an appropriate continuous time model, an innovative combination of linear mixed models and Rasch models. An item-level RS detection method using this continuous-time Rasch model will then be proposed. It will aim at investigating RS in depth in order to detect RS, examine items expected to be affected by RS, type and size of RS effects. To ensure that the proposed method for RS analysis is valid and reliable, different simulation studies will be performed all along the project to assess the performances of the method. The method will be automated in a module of a statistical software. To overcome practical issues related to the analysis of real datasets, we will apply the proposed method to study the changes and RS in anxiety and depression of healthcare workers of comprehensive cancer centers during the Covid-19 pandemic. This application will also illustrate how RS analyses allow getting more insight into the adaptive processes in face of a health crisis. A work on graphical representations of results of longitudinal SRO changes and RS analysis will aim at providing understandable and meaningful reports of the results to promote knowledge translation. Indeed, a survey will assess data visualization preferences and understanding of the results in a multidisciplinary framework.

In order to promote the results of research projects funded by the ANR, volunteer coordinators will be supported by Nantes Université outreach team to design and deploy a scientific mediation tool. This will take the form of a fun and interactive workshop that can be presented at events for the general public or schools.

Background: Pneumonia is the leading cause of communicable diseases and the second cause of disability-adjusted life-years in the world. Pneumonia can be acquired in the community or during hospitalization. Severe pneumonia frequently evolves towards acute respiratory distress syndrome, and we observed that current therapies do not produce the expected favorable outcome for 25 to 35% of patients. Our team developed a reappraisal of the pathophysiology of pneumonia, proposing restoration of normal host-microbiome interactions as a primary therapeutical target. We hypothesis that the restoration of normal respiratory microbiome composition and functions has the potential to restore lung homeostasis and enhance the treatment of pneumonia. Objectives: Our main objectives are to define at specie levels a commensal consortium whose elimination is associated with pneumonia severity, to describe the metabolite and peptide productions of this bacterial consortium, to test the effects of a probiotic composed of this lung specific consortium on pathogen multiplication and microbiome composition (inter-species communication) and on lung mucosal immunity (host-microbiome interactions). Methods: Using multi-omics data integration, we will define consortia composed of bacterial species, metabolites, and peptides which will be associated with pneumonia severity and treatment failure. We will then investigate in vitro and in murine models their effects in modulating interspecies bacterial cooperation mechanisms and inhibiting the pathogen’s growth, and on host immune defenses. Perspectives: The SYMBIOLUNG project will help define the commensal bacterial mediators involved in lung homeostasis, thus filling a significant knowledge gap essential for developing lung-specific microbiome-targeted approaches. Thus, if our hypothesis is demonstrated, the impact of our work will be tremendous for patients with pneumonia and for healthcare workers, considering the number of target patients.

Design of concrete structures involve the consideration of their exposure to predict their service life from engineering or more sophisticated models if the effective diffusion coefficient is known. This parameter is generally obtained from natural diffusion or migration tests on concrete samples. Because of calibrations, most of the existing models models do not link the effective diffusion coefficient to the physical and chemical properties of the material at the molecular scale which affect the diffusivity in the gel pores of hydrates. In this project, we propose a multiscale model to predict the diffusion coefficient in cement pastes with partial substitution of blast furnace slag and metakaolin, for which the main hydrated products are calcium aluminosilicates hydrates (C-A-S-H). At first, we will study the diffusion of ions in gel pores on molecular models of C-(A)-S-H considering different pore solutions (bi-species and multispecies), different pore sizes, and different C/S and A/S ratios to cover the ranges observed in SCM blended cementitious materials. Scanning electron microscope image analysis coupled with energy-dispersive X-ray spectroscopy, together with 29Si and 27Al nuclear magnetic resonance (NMR) measurements will validate the molecular models. The molecular approach will highlight the electrical double layer (EDL) phenomenon, whose effect on diffusion has been demonstrated. The influence of the EDL will be quantified experimentally using different methods of zeta potential measurement. Secondly, the nanoscale (~100 nm) will be taken into account with a nanoparticle interaction model. Several representative elementary volumes will be created to reproduce experimental observations from mercury intrusion porosimetry, nitrogen adsorption and proton NMR. Finally, based on a hydration model, we will make a transition to the scale of a representative elementary volume of cement paste using homogenization methods. Migration tests will permit to compare the diffusion coefficients calculated by our multi-scale model. The ambition of this project is to improve the fundamental understanding on the diffusion of ionic species in the cement paste and to answer the following question: how does the composition of the binder affect the diffusion in concrete at different scales? This study could pave the way for better concrete design with a view to a more accurate prediction of the service life of structures.