Santé des personnes LGBTI+ : enjeux scientifiques et perspectives de santé publique.

Despite advances in recent decades, health inequalities faced by sexual, gender and gendered minorities remain poorly recognized. Yet the barriers to accessing prevention and care encountered by these populations are widely documented in international literature. The health of LGBTI+ people is still largely understood and structured as a sexual health issue related to sexually transmitted infections, leaving other crucial issues in the background. Some questions remain hidden, in particular the realities experienced by bisexual, lesbian, intersex people, as well as the diversity of transidentities. This issue is a first on several levels. It is indeed the first time that a scientific journal publishes a collection of this scope in the French-speaking world. Moreover, the issue brings together contributions that reflect the diversity of actors and actresses in this field. Finally, the issue's French-speaking focus allows for diversified geographical insights. This issue is structured around three main parts: 1) Thinking, categorizing: from research to health promotion; 2) Describing health realities: apprehending needs and understanding obstacles; 3) Intervening in the field of sexual, gender and gendered minorities health.

Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge.

Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-art connectivity methods using novel large-scale numerical phantoms. The diffusion signal for the phantoms was obtained from Monte Carlo simulations. The results of the challenge suggest that methods selected by the 14 teams participating in the challenge can provide high correlations between estimated and ground-truth connectivity weights, in complex numerical environments. Additionally, the methods used by the participating teams were able to accurately identify the binary connectivity of the numerical dataset. However, specific false positive and false negative connections were consistently estimated across all methods. Although the challenge dataset doesn't capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods.

Investigating health services for sexual and gender minorities in France: a qualitative study protocol.

INTRODUCTION: Discrimination and structural violence experienced by sexual and gender minorities are the source of social inequalities in health. The last decade has been marked by major developments in the provision of sexual health services for these minorities in France. This paper presents the research protocol of the Services for Minorities-Lesbian Gays Bisexuals Transgender Intersex+ (SeSAM-LGBTI+) study, which aims to document the health, social and professional challenges in the organisation of current health services for sexual and gender minorities in France. METHODS AND ANALYSIS: The SeSAM-LGBTI+ study relies on a multidisciplinary qualitative study. It has two objectives: (1) to analyse the history of the development of LGBTI+ health services in France, through interviews with key informants and rights activists and through a study of archives and (2) to study the functioning and challenges of a sample of health services currently offered to LGBTI+ people in France, through a multiple case study, using a multilevel and multisited ethnography. The study will rely on approximately 100 interviews. The analysis will be based on an inductive and iterative approach, combining sociohistorical data and the cross-sectional analysis of the case studies. ETHICS AND DISSEMINATION: The study protocol has undergone a peer review by the Institut de Recherche En santé Publique's scientific committee and has been approved by the research ethical committee of Aix-Marseille University (registration number: 2022-05-12-010). The project has received funding from December 2021 to November 2024. The results of the research will be disseminated from 2023 onwards to researchers, health professionals and community health organisations.

Robust Monte-Carlo Simulations in Diffusion-MRI: Effect of the Substrate Complexity and Parameter Choice on the Reproducibility of Results.

Monte-Carlo Diffusion Simulations (MCDS) have been used extensively as a ground truth tool for the validation of microstructure models for Diffusion-Weighted MRI. However, methodological pitfalls in the design of the biomimicking geometrical configurations and the simulation parameters can lead to approximation biases. Such pitfalls affect the reliability of the estimated signal, as well as its validity and reproducibility as ground truth data. In this work, we first present a set of experiments in order to study three critical pitfalls encountered in the design of MCDS in the literature, namely, the number of simulated particles and time steps, simplifications in the intra-axonal substrate representation, and the impact of the substrate's size on the signal stemming from the extra-axonal space. The results obtained show important changes in the simulated signals and the recovered microstructure features when changes in those parameters are introduced. Thereupon, driven by our findings from the first studies, we outline a general framework able to generate complex substrates. We show the framework's capability to overcome the aforementioned simplifications by generating a complex crossing substrate, which preserves the volume in the crossing area and achieves a high packing density. The results presented in this work, along with the simulator developed, pave the way toward more realistic and reproducible Monte-Carlo simulations for Diffusion-Weighted MRI.

Towards microstructure fingerprinting: Estimation of tissue properties from a dictionary of Monte Carlo diffusion MRI simulations.

Many closed-form analytical models have been proposed to relate the diffusion-weighted magnetic resonance imaging (DW-MRI) signal to microstructural features of white matter tissues. These models generally make assumptions about the tissue and the diffusion processes which often depart from the biophysical reality, limiting their reliability and interpretability in practice. Monte Carlo simulations of the random walk of water molecules are widely recognized to provide near groundtruth for DW-MRI signals. However, they have mostly been limited to the validation of simpler models rather than used for the estimation of microstructural properties. This work proposes a general framework which leverages Monte Carlo simulations for the estimation of physically interpretable microstructural parameters, both in single and in crossing fascicles of axons. Monte Carlo simulations of DW-MRI signals, or fingerprints, are pre-computed for a large collection of microstructural configurations. At every voxel, the microstructural parameters are estimated by optimizing a sparse combination of these fingerprints. Extensive synthetic experiments showed that our approach achieves accurate and robust estimates in the presence of noise and uncertainties over fixed or input parameters. In an in vivo rat model of spinal cord injury, our approach provided microstructural parameters that showed better correspondence with histology than five closed-form models of the diffusion signal: MMWMD, NODDI, DIAMOND, WMTI and MAPL. On whole-brain in vivo data from the human connectome project (HCP), our method exhibited spatial distributions of apparent axonal radius and axonal density indices in keeping with ex vivo studies. This work paves the way for microstructure fingerprinting with Monte Carlo simulations used directly at the modeling stage and not only as a validation tool.