Accuracy of Computational Chemistry Methods to Calculate Organic Contaminant Molecular Properties

International audienceThe quantitative structure activity relationship (QSAR) methodology has been developed and extensively used to predict unknown environmental data for compounds that have not been experimentally studied yet. QSAR is based on a large series of descriptors: such as the number of atoms, the number of bonds… (descriptive), or based on the 2D structure of the molecule (connectivity indices…) or on its 3D structure (dipole moment, polarizability…). Among them, quantum-based 3D descriptors appear as promising tools to predict macroscopic environmental properties. For a set of 104 pharmaceuticals and personal care products, four quantum-based 3D descriptors (electric dipole moment, polarizability, HOMO energy and ionization potential) were calculated using different computational chemistry strategies involving a conformational search followed by local quenches within three different frameworks: density functional theory (DFT), semi-empirical Austin Model 1 (AM1) approach, and density functional based tight binding (DFTB). Comparing the results obtained using each framework highlights the necessity of a comprehensive conformational search and the use of an accurate potential for the local quenches. Using the combination of a global exploration through molecular dynamics with local quenches at B3LYP/6-31G* (DFT) allows the calculation of accurate and tractable quantum-based 3D descriptors

RePP'Air - Comprendre les mécanismes de transferts de produits phytosanitaires dans l'air pour une appropriation par la profession agricole.

The RePP'Air project aims to better understand the processes of transfer of plant protection products inthe air through the implementation of a measurement device on 7 sites in France, associated with surveysof agricultural practices on site. After 4 years of study, the RePP'Air project made it possible to betterunderstand the mechanisms influencing the transfers in the air, to sensitize the farmers and farmersorganisations, so that the agricultural sector appropriates this subject and the existing levers of action.Prospects for further work were also identified. The richness of this project also lies in its unprecedentedpartnership between agricultural actors, air actors, agronomic research and vocational schools, which hasenabled progress to be made on this topic of shared interest.Le projet RePP’Air vise à mieux appréhender les processus de transferts de produits phytosanitaires dansl'air via la mise en place d'un réseau de 7 dispositifs de mesures sur des espaces agricoles français,associé à des enquêtes de pratiques agricoles sur chaque site. Après 4 années d’étude, le projetRePP’Air a permis de mieux comprendre les mécanismes influençant les transferts dans l’air, sensibiliserla profession, de façon à ce que le secteur agricole soit d’approprie ce sujet et les leviers d’actionsexistants pour limiter les risques de transfert. Des pistes de travail à approfondir ont également étéidentifiées. La richesse de ce projet réside aussi dans son partenariat inédit entre les acteurs agricoles,les acteurs de l’air, de la recherche agronomique et de l’enseignement agricole qui a permis d’avancersur cette thématique d’intérêts partagé

Early Detection of Neurodevelopmental Disorders of Toddlers and Postnatal Depression by Mobile Health App: Observational Cross-sectional Study

BackgroundDelays in the diagnosis of neurodevelopmental disorders (NDDs) in toddlers and postnatal depression (PND) in mothers are major public health issues. In both cases, early intervention is crucial. ObjectiveWe aimed to assess if a mobile app named Malo can reduce delay in the recognition of NDD and PND. MethodsWe performed an observational, cross-sectional, data-based study in a population of young parents with a minimum of 1 child under 3 years of age at the time of inclusion and using Malo on a regular basis. We included the first 4000 users matching the criteria and agreeing to participate between November 11, 2021, and January 14, 2022. Parents received monthly questionnaires via the app, assessing skills on sociability, hearing, vision, motricity, language of their infants, and possible autism spectrum disorder. Mothers were also requested to answer regular questionnaires regarding PND, from 4-28 weeks after childbirth. When any patient-reported outcomes matched predefined criteria, an in-app notification was sent to the user, recommending the booking of an appointment with their family physician or pediatrician. The main outcomes were the median age of the infant at the time of notification for possible NDD and the median time of PND notifications after childbirth. One secondary outcome was the relevance of the NDD notification for a consultation as assessed by the physicians. ResultsAmong 4242 children assessed by 5309 questionnaires, 613 (14.5%) had at least 1 disorder requiring a consultation. The median age of notification for possible autism spectrum, vision, audition, socialization, language, or motor disorders was 11, 9, 17, 12, 22, and 4 months, respectively. The sensitivity of the alert notifications of suspected NDDs as assessed by the physicians was 100%, and the specificity was 73.5%. Among 907 mothers who completed a PND questionnaire, highly probable PND was detected in 151 (16.6%) mothers, and the median time of detection was 8-12 weeks. ConclusionsThe algorithm-based alert suggesting NDD was highly sensitive with good specificity as assessed by real-life practitioners. The app was also efficient in the early detection of PND. Our results suggest that the regular use of this multidomain familial smartphone app would permit the early detection of NDD and PND. Trial RegistrationClinicalTrials.gov NCT04958174; https://clinicaltrials.gov/ct2/show/NCT0495817