Le PGD d'entité au service d'une politique des données : Retour d'expérience d'AgroParisTech

National audienceLes plans de gestion de données pour les projets de recherche ont commencé à apparaître en 2017, avec le projet pilote d’Horizon 2020. Le PGD est ensuite devenu peu à peu un incontournable en France avec sa systématisation pour tous les projets ANR depuis 2019 et la nomination d’un référent qualité au niveau national. À la faveur de cette nouveauté, s’est ensuite posée la question d’une rationalisation des pratiques au niveau des unités de recherche et des plateformes : la réflexion sur le PGD d’entité de recherche démarrait. Formaliser la politique de données d’une entité, faciliter la rédaction des PGD de projet pour les chercheurs utilisant des données issues de plateformes, asseoir et valoriser des processus de qualité ; les PGD d’entités permettent de remplir de multiples objectifs

Fungi of French Guiana : diversity and distribution based on mycological surveys

International audienc

Ants of French Guiana: 16S rRNA sequence dataset

This dataset represents a reference library of DNA sequences for ants from French Guiana. A total of 3931 new sequences from the 16S rRNA gene has been generated. The reference library covers 344 species distributed in 57 genera. Overall, 3920 sequences have been assigned at the species level and 11 at the genus level. All these sequences were submitted to DDBJ/EMBL/GenBank databases in the Bioproject: PRJNA779056: 16S French Guiana Ants (Hymenoptera: Formicidae), sequence identifier KFFS00000000

Ants of French Guiana: a DNA barcode dataset

This dataset represents a DNA barcode reference library for ants from French Guiana. A total of 3931 new sequences from 16S rRNA gene has been generated. The reference library covers 344 species distributed in 57 genera. 3920 sequences are assigned at the species level and 11 at the genus level. All these sequences were submitted to DDBJ/EMBL/GenBank databases in the Bioproject: PRJNA779056: 16S French Guiana Ants (Hymenoptera: Formicidae), sequence identifier KFFS00000000

Performance of a Region of Interest–based Algorithm in Diagnosing International Society of Urological Pathology Grade Group ≥2 Prostate Cancer on the MRI-FIRST Database—CAD-FIRST Study

International audienceBackground and objective: Prostate multiparametric magnetic resonance imaging (MRI) shows high sensitivity for International Society of Urological Pathology grade group (GG) ≥2 cancers. Many artificial intelligence algorithms have shown promising results in diagnosing clinically significant prostate cancer on MRI. To assess a region-of-interest-based machine-learning algorithm aimed at characterising GG ≥2 prostate cancer on multiparametric MRI.Methods: The lesions targeted at biopsy in the MRI-FIRST dataset were retrospectively delineated and assessed using a previously developed algorithm. The Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2) score assigned prospectively before biopsy and the algorithm score calculated retrospectively in the regions of interest were compared for diagnosing GG ≥2 cancer, using the areas under the curve (AUCs), and sensitivities and specificities calculated with predefined thresholds (PIRADSv2 scores ≥3 and ≥4; algorithm scores yielding 90% sensitivity in the training database). Ten predefined biopsy strategies were assessed retrospectively.Key findings and limitations: After excluding 19 patients, we analysed 232 patients imaged on 16 different scanners; 85 had GG ≥2 cancer at biopsy. At patient level, AUCs of the algorithm and PI-RADSv2 were 77% (95% confidence interval [CI]: 70-82) and 80% (CI: 74-85; p = 0.36), respectively. The algorithm's sensitivity and specificity were 86% (CI: 76-93) and 65% (CI: 54-73), respectively. PI-RADSv2 sensitivities and specificities were 95% (CI: 89-100) and 38% (CI: 26-47), and 89% (CI: 79-96) and 47% (CI: 35-57) for thresholds of ≥3 and ≥4, respectively. Using the PI-RADSv2 score to trigger a biopsy would have avoided 26-34% of biopsies while missing 5-11% of GG ≥2 cancers. Combining prostate-specific antigen density, the PI-RADSv2 and algorithm's scores would have avoided 44-47% of biopsies while missing 6-9% of GG ≥2 cancers. Limitations include the retrospective nature of the study and a lack of PI-RADS version 2.1 assessment.Conclusions and clinical implications: The algorithm provided robust results in the multicentre multiscanner MRI-FIRST database and could help select patients for biopsy.Patient summary: An artificial intelligence-based algorithm aimed at diagnosing aggressive cancers on prostate magnetic resonance imaging showed results similar to expert human assessment in a prospectively acquired multicentre test database

French national epidemiology of bacterial superinfections in ventilator-associated pneumonia in patients infected with COVID-19: the COVAP study

International audienceAbstract Background Description and comparison of bacterial characteristics of ventilator-associated pneumonia (VAP) between critically ill intensive care unit (ICU) patients with COVID-19-positive, COVID + ; and non-COVID-19, COVID-. Methods Retrospective, observational, multicenter study that focused on French patients during the first wave of the pandemic (March–April 2020). Results 935 patients with identification of at least one bacteriologically proven VAP were included (including 802 COVID +). Among Gram-positive bacteria, S. aureus accounted for more than two-thirds of the bacteria involved, followed by Streptococcaceae and enterococci without difference between clinical groups regarding antibiotic resistance. Among Gram-negative bacteria, Klebsiella spp. was the most frequently observed bacterial genus in both groups, with K. oxytoca overrepresented in the COVID- group (14.3% vs . 5.3%; p < 0.05). Cotrimoxazole-resistant bacteria were over-observed in the COVID + group (18.5% vs . 6.1%; p <0.05), and after stratification for K. pneumoniae (39.6% vs . 0%; p <0.05). In contrast, overrepresentation of aminoglycoside-resistant strains was observed in the COVID- group (20% vs . 13.9%; p < 0.01). Pseudomonas sp. was more frequently isolated from COVID + VAPs (23.9% vs . 16.7%; p <0.01) but in COVID- showed more carbapenem resistance (11.1% vs . 0.8%; p <0.05) and greater resistance to at least two aminoglycosides (11.8% vs . 1.4%; p < 0.05) and to quinolones (53.6% vs . 7.0%; p <0.05). These patients were more frequently infected with multidrug-resistant bacteria than COVID + (40.1% vs . 13.8%; p < 0.01). Conclusions The present study demonstrated that the bacterial epidemiology and antibiotic resistance of VAP in COVID + is different from that of COVID- patients. These features call for further study to tailor antibiotic therapies in VAP patients