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Prevalence and associated factors of post-traumatic stress disorders among LGBTQI+ adults: a systematic literature review
No full textInternational audienceBACKGROUND: LGBTQI+ individuals appear to be particularly at-risk of exposure to interpersonal violence. This leads to an increased risk of developing symptoms of Post-Traumatic Stress Disorder (PTSD) or Complex Post-Traumatic Stress Disorder (C-PTSD). The objectives of this systematic review is to: (1) Compile the prevalences of PTSD/C-PTSD among LGBTQI+ individuals; (2) Compare the symptomatology of PTSD/C-PTSD according to sexual orientation and gender identity; (3) Identify the factors involved in the symptomatology of PTSD/C-PTSD among LGBTQI+ individuals. METHODS: A systematic literature review was conducted on PTSD/C-PTSD among LGBTQI+ individuals. Psychinfo, Psycharticle, Psychology and Behavioral Sciences Collection, Embase, and LGBTHealth databases were queried. Only quantitative, observational studies based on data collected after 2010 and involving LGBTQI+ adults were included. The risk of bias was assessed using the Control Guidelines Critical Appraisal Toolkit developed by the Public Health Agency of Canada. RESULTS: Out of the 7446 articles identified, 60 were included. Eighteen provided data on prevalence, and 57 on associated factors. The majority of studies were conducted in the United States. The vast majority of studies assessed PTSD using self-administered scales. Only one evaluated symptoms of C-PTSD. All included studies reported extremely high PTSD prevalence rates, with certain populations appearing particularly at risk, such as bisexual (10.3-35.7% PTSD) and transgender individuals (36.8-64.3% PTSD). Individual (e.g., financial precarity, transition, internalized stigma), interpersonal (e.g., outness, social support), organizational (e.g. health barriers), community (e.g. anti-trans discourse), and political variables (anti-trans laws project) associated with PTSD/C-PTSD symptoms have been identified. CONCLUSIONS: The results show the importance of considering PTSD/C-PTSD among LGBTQI+ individuals in clinical practice and research. Higher quality studies are needed to quantify the extent of the problem. Healthcare professionals must be trained in the specific factors that may contribute to PTSD/C-PTSD symptoms in LGBTQI+ individuals. This cannot be achieved without public policies aimed at preventing all forms of violence against the LGBTQI+ population and ensuring access to equal rights
Creation and validation of a roadside rescue skills scale for training pre-hospital medical teams: the RoadRes-Q scale
No full textInternational audienceBackground: Road traffic injuries are the leading cause of death among young people worldwide. While advances in vehicle safety have reduced some of the risks, the speed and quality of pre-hospital care are critical to prevent fatalities. In France, patients are cared for by medical teams and firefighters who must work together as closely as possible to ensure the best possible survival rate. However, there is a lack of standardised scales to assess the performance of these multidisciplinary teams. This study aimed to create and validate a roadside rescue skills assessment scale, the RoadRes-Q scale, for healthcare teams.Methods: We used a two-round Delphi method to develop the RoadRes-Q scale. A panel of 9 international roadside rescue experts, including 7 firefighters and 2 engineers in road rescue equipment, agreed to participate. The scale covers five key areas: healthcare provider protection, site securing, vehicle securing, first aid delivery, and patient extrication. The final version was tested during two one-day simulation-based training sessions, each involving 22 participants: 6 healthcare staff, 14 firefighters, and 2 simulated victims. Assessors completed the scale during and after each scenario, focusing on internal consistency and inter-observer reliability.Results: The RoadRes-Q scale consists of 60 items. Internal consistency was excellent (Cronbach’s alpha of 0.86), indicating that items were non-redundant and consistently measured the required competencies. However, inter-observer reliability was low (intra-class correlation coefficient of 0.48), suggesting variability between assessors. Satisfaction among participants to the simulation-based training courses was high, and their knowledge increased.Conclusions: The RoadRes-Q scale proved to be a valid and reliable scale for evaluating both technical and non-technical skills. While internal consistency was strong, improvements are needed in inter-observer reliability. Structured training for assessors and video-based assessments could enhance reproducibility. The RoadRes-Q scale has the potential for assessing the quality and safety of care provided by healthcare teams in roadside rescue situations.Registration: As the study did not involve interventional research or patient participation, ethics committee approval was not required, but it received approval from the scientific referents of the Faculty of Medicine of Poitiers, and participants provided informed consent for using their anonymised data
Temporal stability of inflammatory subphenotypes of acute respiratory distress syndrome: 28-day insights from the ICAR trial
No full textInternational audienceBackgroundInternational guidelines have emphasized the necessity of evaluating the temporal stability of acute respiratory distress syndrome (ARDS) subphenotypes. This study aimed to assess the temporal stability of subphenotypes of ARDS over 28 days.MethodsA reanalysis of a randomized trial was conducted, including patients with COVID-19-related moderate-to-severe ARDS across 43 centers. A K-means clustering was conducted to identify subphenotypes at 7-day intervals from inclusion to day 28. A Bayesian discrete-time Markov model was constructed to assess the temporal stability of subphenotypes.ResultsTwo subphenotypes were identified among 146 patients. At inclusion, 121 (83%) patients were in the hypoinflammatory subphenotype and 25 (17%) in the hyperinflammatory subphenotype. The hyperinflammatory subphenotype was associated with higher rates of organ failure, higher plasma levels of cytokines, chemokines, adhesion molecules, and proangiogenic factors, and lower endothelial stability than the hypoinflammatory subphenotype. The hyperinflammatory subphenotype was associated with higher 28-day mortality (13/25, 52% vs. 30/121, 25%, p = 0.001) and fewer ventilatory-free-days through day 28 (p < 0.01) than the hypoinflammatory subphenotype. In the Bayesian Markov model, over 7-day intervals, patients in the hypoinflammatory subphenotype had a higher probability of remaining hypoinflammatory (70%) or being extubated (17%) than of progressing to the hyperinflammatory subphenotype (7%). Inversely, patients in the hyperinflammatory subphenotype had a higher probability of remaining in the hyperinflammatory subphenotype (52%) or dying (23%) than of transitioning to the hypoinflammatory subphenotype (20%) or being extubated (5%).ConclusionsInflammatory subphenotypes were stable in COVID-19-related ARDS, with few transitions over 28 days. Monitoring these subphenotypes could be valuable for assessing patient trajectories and treatment responses
A study protocol exploring synchrony between mother infant and therapist during shared reading with preterm infants in a neonatal unit
No full textInternational audienceEarly parent-infant interactions are crucial for socio-emotional development; however, interventions to enhance these interactions in the context of prematurity remain underexplored. This study aims to explore the effects of shared reading on mother-preterm infant synchrony. A single-center pre-post study will be conducted at Amiens University Hospital, involving 20 preterm infants (at birth between 25 and 33 GW and at inclusion between 34 and 36 GW) and their mothers. Synchrony will be assessed at multiple levels-hormonal, physiological, behavioral, and psychological-during interactive and non-interactive periods. We hypothesize that shared reading sessions enhance physiological synchrony during interactive periods compared with non-interactive periods. Additionally, we expect post-intervention increases in oxytocin and vasopressin levels, with greater behavioral synchrony during reading sessions. Psychological synchrony will be determined through correlations between empathic moments identified by observers and increased physiological and behavioral synchrony. The findings could inform neonatal care practices, emphasizing the importance of early targeted interventions to enhance parent-infant bonding and improve developmental outcomes
Multivariate Screening and Automated Clustering of Macrophage Immunoreactome to Nanoparticles and Photothermal Therapy
No full textInternational audienceAbstract Immunotherapy aims to control the immune system against diseases such as cancer or infections. Nanotechnology is part of the armamentarium to reprogram the immune system in a spatially and temporally controlled manner. However, predicting immune responses using high‐throughput tests is challenging due to the immunoreactome's complexity and plasticity. This work presents a fast, machine learning‐assisted predictive assay to classify the multifactorial immune responses to any kind of treatments. Engineered human THP‐1 monocytes differentiated and polarized into M0, M1, and M2 macrophages are used to monitor nuclear factor Kappa B (NF‐kB) and interferon regulatory factor (IRF) pathway activations and gene expression profile in response to metallic nanoparticles (NPs), activated or not by light to induce photothermal therapy (PTT). Principal component analysis (PCA) reveals distinct responses to nanoparticles and the reprogramming toward inflammatory macrophage triggered by PTT. Gold‐iron oxide nanoflowers and magnetosomes per se favor polarization toward M2 profile, while light activation shifts this M2‐like macrophages toward an M1 phenotype. These findings, confirmed on human blood derived monocytes shed light on the intricate immunomodulatory effects of nanoparticles and PTT on macrophage behavior and provide a basis for an adaptable screening method for the predictive design of therapeutic strategies for immunotherapy in cancer and other diseases
Cibler les nouveaux neurones adultes pour corriger les déficits de pattern separation chez les souris Tg2576 modèles de la maladie d'Alzheimer
No full textInternational audienceHippocampal adult neurogenesis in mammals generates a unique population of immature granule neurons that play a crucial role in memory, learning and spatial processing. In both Alzheimer's disease (AD) patients and mouse models, this neurogenic capacity of the dentate gyrus is reduced. Mouse models of AD have shown that hippocampal adult neurogenesis is altered early in the course of the disease, contributing to hippocampaldependent memory impairments. Early stages of AD are also associated with mitochondrial dysfunction in adult-born neurons. While mitochondria have emerged as key regulators of adult neurogenesis, they have been found to be dysfunctional in AD context and to contribute to neurodegenerative diseases. In this study, we investigated the timing and nature of memory deficits in Tg2576 mice, a model of AD, focusing on tasks that depend on hippocampal neurons born in adulthood. We found that Tg2576 mice exhibit early deficits in pattern separation tasks. Notably, adult-born neurons in Tg2576 mice show reduced connectivity and mitochondrial content along with the first memory deficits. To mitigate these defects, we used NeuroD1, a potent neuronal transcription factor, to enhance the development of adult-born neurons in Tg2576 mice. Overexpression of NeuroD1 restored both mitochondrial content and spine density in these neurons to levels comparable to those in control mice. In addition, this intervention restored pattern separation performance in Tg2576 mice, highlighting the potential of strategies targeting mitochondria to correct adult neurogenesis-related memory deficits in the early stages of AD.</div
Apprentissage fédéré, des enjeux politiques: Cas du gouvernement des données hospitalières
No full textNational audienceFederated learning is a well-documented scientific and privacy-protection issue. This poster presents an aspect that is less explored in the literature: the political dimension of this research method. Using the example of a hospital research network, we show how this technical choice enables physicians who are experts in data to strengthen their position within the field of health research. Combined with other digital infrastructures, federated learning allows them to avoid being subordinated to platforms that would deprive them of control over the data produced in hospitals. It thus becomes a lever for steering scientific practices in a direction that aligns with the objectives of their professional group.L'apprentissage fédéré est un enjeu scientifique et de protection de la vie privée bien documenté. Ce poster présente un aspect moins étudié par la littérature : la dimension politique de cette méthode de recherche. En prenant l'exemple d'un réseau de recherche hospitalier, on verra comment ce choix technique permet à des médecins experts des données de renforcer leur position dans le champ de la recherche en santé. Associé à d'autres infrastructures informatiques, l'apprentissage fédéré leur permet d'éviter d'être subordonné à des plateformes qui leur feraient perdre le contrôle des données produites dans les hôpitaux. C'est un levier pour orienter les pratiques scientifiques dans une direction conforme aux objectifs de leur groupe professionnel
Experimental analysis of bone marrow adipose tissue and bone marrow adipocytes : An update from the bone marrow adiposity society (BMAS)
No full textInternational audienceBone marrow adipose tissue (BMAT) is physiologically linked to bone and energy metabolism, endocrine regulation, hematopoiesis and cancer-related processes. A key challenge in the field is that methods for isolating BMAT or bone marrow adipocytes (BMAds) are variable because there are no widely adopted standardized protocols. To generate awareness of this challenge and to establish uniformity in experimental approaches requiring isolation, storage and characterization of BMAT and BMAds, the Biobanking Working Group of the international Bone Marrow Adiposity Society (BMAS) has previously recommended experimental standards. This paper provides an update on this effort and presents current state-of-the-art methods and technical considerations for isolation and characterization of BMAT and BMAds, including currently available high-throughput omics approaches. This review provides a reference point based on the consensus view of BMAS investigators to support studies on biomedical, biological, biochemical and biophysical questions associated with bone marrow adiposity
Utilisation de l'intelligence artificielle pour l'analyse des signaux potentiels de pharmacovigilance à partir de données de vie réelle structurées
No full textThis thesis explores how artificial intelligence (AI), particularly causal machine learning (CML), can improve pharmacovigilance by leveraging structured real-world data (RWD), such as electronic health records. While traditional methods relying on spontaneous reporting of adverse drug reactions are limited by underreporting and bias, RWD offer a more comprehensive view of the patient experience but face challenges related to data complexity and quality. An exploratory study revealed the main obstacles to current AI applications, including inconsistency in data preprocessing and a lack of explainability. To address this, our thesis work implemented a CML framework using the MIMIC-IV database to detect acute drug-induced kidney injury, demonstrating that CML can provide interpretable, personalized, and clinically relevant insights. The results position causal AI as a promising avenue for improving the accuracy, transparency, and regulatory acceptance of pharmacovigilance systems.Cette thèse explore comment l'intelligence artificielle (IA), en particulier l'apprentissage automatique causal (CML), peut améliorer la pharmacovigilance en exploitant des données structurées issues du monde réel (RWD), telles que les dossiers médicaux électroniques. Alors que les méthodes traditionnelles reposant sur la déclaration spontanée d'effets indésirables médicamenteux sont limitées par la sous-déclaration et les biais, les RWD offrent une vision plus complète de l'expérience des patients, mais se heurtent à des problèmes de complexité et de qualité des données. Une étude exploratoire a révélé les principaux obstacles aux applications actuelles de l'IA, notamment le manque de cohérence dans le prétraitement des données et le manque d'explicabilité. Pour y remédier, notre travail de thèse a mis en œuvre un cadre CML utilisant la base de données MIMIC-IV afin de détecter les lésions rénales aiguës d'origine médicamenteuse, démontrant ainsi que le CML peut fournir des informations interprétables, personnalisées et cliniquement pertinentes. Les résultats positionnent l'IA causale comme une voie prometteuse pour améliorer la précision, la transparence et l'acceptation réglementaire des systèmes de pharmacovigilance