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Objectives: Monitoring tools that could provide quick predictions of tuberculosis (TB) treatment outcomes are urgently needed. Here, we assessed whether the evolution of selected biomarkers of innate immunity may help monitoring TB treatment response within 2 weeks of treatment initiation. Methods: ANRS12394-LILAC-TB was a proof-of-concept prospective study: adults with a rifampicin-susceptible TB who are HIV-negative and HIV-infected documented by a positive Xpert MTB/RIF test were enrolled in Cambodia and Côte d'Ivoire. Plasma concentrations of interleukin-1 receptor antagonist (IL-1Ra), interferon-γ–induced protein-10 and clusters of differentiation (CD) (scavenging CD163) were measured by commercial enzyme-linked immunosorbent assay kits. A Wilcoxon test for paired data was used for longitudinal comparisons. Results: A total of 55 patients were enrolled (women: 31%, median age: 37 years; median CD4 count in the 10 of 13 participants with HIV: 53 cells/mm3). Overall, 83% were considered in TB treatment success. Compared with baseline, the IL-1Ra plasma levels significantly decreased as soon as week (W) 1, independent of HIV status (−71% in HIV-positive vs −33% in HIV-negative; P

Objective This study aims to evaluate whether the first wave of the COVID-19 pandemic resulted in a deterioration in the quality of care for socially and/or clinically vulnerable stroke and ST-segment elevation myocardial infarction (STEMI) patients.Design Two cohorts of STEMI and stroke patients in the Aquitaine neurocardiovascular registry.Setting Six emergency medical services, 30 emergency units, 14 hospitalisation units and 11 catheterisation laboratories in the Aquitaine region in France.Participants This study involved 9218 patients (6436 stroke and 2782 STEMI patients) in the neurocardiovascular registry from January 2019 to August 2020.Primary outcome measures Care management times in both cohorts: first medical contact-to-procedure time for the STEMI cohort and emergency unit admission-to-imaging time for the stroke cohort. Associations between social (deprivation index) and clinical (age >65 years, neurocardiovascular history) vulnerabilities and care management times were analysed using multivariate linear mixed models, with an interaction on the time period (pre-wave, per-wave and post-first COVID-19 wave).Results The first medical contact procedure time was longer for elderly (p

Shape vectorization is a key stage of the digitization of large-scale historical maps, especially city maps that exhibit complex and valuable details. Having access to digitized buildings, building blocks, street networks and other geographic content opens numerous new approaches for historical studies such as change tracking, morphological analysis and density estimations. In the context of the digitization of Paris atlases created in the 19th and early 20th centuries, we have designed a supervised pipeline that reliably extract closed shapes from historical maps. This pipeline is based on a supervised edge filtering stage using deep filters, and a closed shape extraction stage using a watershed transform. It relies on probable multiple suboptimal methodological choices that hamper the vectorization performances in terms of accuracy and completeness. Objectively investigating which solutions are the most adequate among the numerous possibilities is comprehensively addressed in this paper. The following contributions are subsequently introduced: (i) we propose an improved training protocol for map digitization; (ii) we introduce a joint optimization of the edge detection and shape extraction stages; (iii) we compare the performance of state-of-the-art deep edge filters with topology-preserving loss functions, including vision transformers; (iv) we evaluate the end-to-end deep learnable watershed against Meyer watershed. We subsequently design the critical path for a fully automatic extraction of key elements of historical maps. All the data, code, benchmark results are freely available at https://github.com/soduco/Benchmark_historical_map_vectorization.