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UNSTRUCTURED Major depressive episode (MDE) is a common clinical syndrome. It can be found in different pathologies such as mood depressive disorder (MDD), bipolar disorder (BD), post-traumatic stress disorder (PTSD) or even occur in a context of psychotrauma. However, only one syndrome is described in international classifications (DSM 5/ICD 11) which do not take into account the underlying pathology at the origin of the MDE. Clinical interviews are currently the best source of information to obtain the etiological diagnosis of MDE. Nevertheless, it does not allow an early diagnosis and there are no objective measures of extracted clinical information. To remedy this, the use of digital tools and their correlation with clinical symptomatology seems promising. We review the state of the art on the use of digital tools during clinical interviews for etiological diagnosis of MDE. For this purpose, we were interested in four frequently found clinical conditions in which MDE can occur: MDD, BD, PTSD and/or trauma investigated by the means of automated voice analysis, behaviour analysis by video and physiological measures by heart rate variability (HRV) and electrodermal activity (EDA). This paper gives a detailed overview of studies on different digital markers associated with MDE which could be useful for differential diagnosis. A digital phenotype of MDE seem to emerge consisting of modifications in speech features (namely temporal, prosodic, spectral, sources, formants and in speech content), modifications in nonverbal behaviour (Head, hand, body and eyes movement, facial expressivity and gaze) and a decrease in physiological measurements (HRV and EDA). We found similarities but also differences when MDE occur in MDD, BD or PTSD. However, comparative studies were rare in BD or PTSD conditions which do not allow us to identify clear and distinct digital phenotypes. Our search identifies markers from several modalities that hold promise for an objective etiological diagnosis of MDE. To validate their potential, further longitudinal and prospective studies are needed.

Dans cette étude de corpus, on aborde la question du comportement dialogique de soutien en entretien clinique avec le patient souffrant de schizophrénie. Nous partons de l’analyse des interactions langagières, en présupposant que le cadre spécifique de l’interaction verbale de type clinique est un lieu d’expression privilégié de “comportement dialogique de soutien”. Sur la base d’une exploration empirique, nous proposons l’élaboration d’un modèle interlocutoire dynamique de ce que l’on pourrait interpréter comme une forme d’écoute attentive, de soutien, en nous basant notamment sur des indicateurs lexicaux particuliers que sont les backchannels. Nous décrivons les différents types d’organisation discursive au sein desquelles les interlocuteurs adoptent un comportement de soutien manifeste. Nous extrayons les propriétés des configurations discursives au sein desquelles le comportement de soutien de l’un des interlocuteurs (la psychologue, par exemple) a pour effet d’amener le locuteur (le patient, par exemple) à modifier sa stratégie argumentative. Les analyses empiriques portent sur la totalité du temps d'enregistrement de 10 entretiens réalisés avec des personnes souffrant de schizophrénie (recueillis en hôpital psychiatrique) et 10 entretiens réalisés avec des sujets témoins.

To compare environmental and culture-derived microbial communities, we performed 16S metabarcoding of uncultured samples and their culture-derived bacterial lawns. Microbial communities were obtained from freshwater river samples representative of an anthropization gradient along a river stream. Their culture-derived bacterial lawns were obtained by growing aliquots of the samples on a broad range medium and on two different semi-selective media. The V3&ndash, V4 16S rRNA region was amplified and sequenced. The bacterial diversity of water samples decreased from the upper to lower stream sampling sites and, as expected, these differences were mostly suppressed by the culture step. Overall, the diversity of cultured-derived bacterial communities reflected selectivity of each tested medium. Comparison of treatments indicated that the culture selected both detected and rare undetected environmental species. Accurate detection of rare environmental bacteria of the Pectobacterium genus by 16S metabarcoding of the culture lawn was demonstrated. Interestingly, for abundant taxa, such as those of the Pseudomonas genus, the culture/environment ratio varied between sampled sites, indicating the difficulty of comparing cultured-derived taxa abundance between environmental sites. Finally, our study also highlighted media specificity and complementarity: bacterial communities grown on the two selective media, while selecting a small set of specific species, were mostly a subset of the bacterial community observed on the broad range medium.