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Copeptin is a reliable biomarker of vasopressin and is associated with urine osmolality in patients on peritoneal dialysis
No full textInternational audienceAbstract Background Vasopressin, a hormone regulating water metabolism, has been poorly studied in patients on peritoneal dialysis (PD). Vasopressin measurement is challenging and not routinely available in clinical practice. This study aimed to evaluate whether copeptin, a stable surrogate marker of vasopressin, could be used to assess vasopressin levels in patients on PD and to determine if vasopressin maintains its antidiuretic effect in this population. Methods We included 34 PD patients from three French nephrology centers. Plasma vasopressin was measured using radioimmunoassay, while copeptin was quantified with a non-competitive immunofluorescence assay. Urine osmolality and 24-hour urine output were assessed, and peritoneal adequacy tests were performed. Associations between copeptin, vasopressin, and clinical parameters were analyzed using Spearman correlations and mixed-effect models. Healthy controls were included for comparison. Results Copeptin levels were strongly correlated with vasopressin levels (Spearman's rho = 0.62, P < 0.001), confirming its reliability as a biomarker of vasopreassin. Higher copeptin levels were associated with increased urine osmolality (β = 3.63, P = 0.008) and decreased 24-hour urine output (β = −0.53, P = 0.008), indicating that vasopressin retains its antidiuretic activity in PD patients. Compared to healthy controls, PD patients had lower urine osmolality and required higher copeptin levels to achieve similar urine concentration, suggesting vasopressin resistance. Copeptin levels were also associated with lower residual kidney function and higher brain natriuretic peptide levels but were not influenced by blood pressure, plasma sodium, or PD characteristics. Conclusions This study provides evidence that vasopressin maintains an antidiuretic effect in PD patients, and supports the use of copeptin as a robust biomarker for vasopressin in this population
Contribution of medical hypnosis via virtual reality in managing pain and anxiety in patients undergoing invasive sampling techniques for prenatal diagnosis: a prospective study
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Benefit and risk associated with interleukin-6 receptor inhibitor administration during severe COVID-19: a retrospective multicentric study
No full textInternational audienceDuring severe and critical COVID-19, therapeutic options remain scarce. Among interventions, the use of interleukin-6 receptor inhibitor (IL-6Ri) is especially controversial due to persistent uncertainty about their efficacy and safety. To compare the occurrence of secondary infections, digestive and hematological complication function of the administration of IL-6Ri we conducted a multicentric retrospective French observational study. All severe or critical COVID-19 requiring hospital admission were included. Among 2587 patients requiring hospital admission, 1603 had a severe COVID-19 and 984 a critical one requiring ICU admission. 224 received at least one dose of tocilizumab or sarilumab. Incidence of secondary infection was 29.5% in the IL-6Ri group vs. 19.5% without IL-6Ri (p = 0.0004) in the whole population. This result remained consistent after adjustment, without multiple imputation (MI) and after MI (adjusted OR: 1.47 [1.25; 1.72]; p < 0.0001)). Incidence of hematological or digestive complication were similar between groups. Mortality of patients admitted in ward was higher in the IL-6Ri group (18.7% vs 10.5%, p = 0.0155). No difference in 28 days, ICU, hospital of 90 days mortality was noticed among ICU patients.Clinical trial registration: This study was registred on ClinicalTrial.gov: NCT05017441
Santé mentale en tension : enjeux épistémologiques, politiques et cliniques d'un espace fragmenté
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From non-specific biomarker to targeted action: transdiagnostic and sex-specific drivers of high-CRP status in severe mental illness across the FondaMental Advanced Centers of Expertise (FACE) cohorts
No full textInternational audienceBackground and objectives: Low-grade systemic inflammation contributes to the pathophysiology of severe mental illness (SMI) in a substantial subset of patients, who often experience greater disease burden and poorer treatment response. Elevated C-reactive protein (CRP), defined as CRP ≥ 3 mg/L, has been proposed to identify this group, but its non-specificity limits the biomarker's ability to guide targeted intervention. We aimed to determine the most consistent drivers of high CRP across bipolar disorder (BD), schizophrenia (SZ) and major depressive disorder (MDD), and to translate these into clinically actionable intervention targets using robust data-driven methods.Methods: We pooled and harmonised data from three large French national SMI cohorts (n = 7149: 4797 bipolar disorder, 1958 schizophrenia and 394 resistant major depression) and classified participants by CRP ≥ 3 mg/L, as well as an alternative cut-off of 5 mg/L. We applied penalised logistic regression (PLR), random forests (RF) and unsupervised clustering, using 28 biopsychosocial variables to identify robust drivers of high-CRP status. We then grouped these into actionable targets and assessed relative dominance.Results: In total, 30.16% of participants had CRP ≥ 3 mg/L. PLR identified female sex (OR [95% CI]: 1.60 [1.27, 1.93]), higher BMI (OR: 1.09 [1.07, 1.13]), current nicotine dependence (OR: 1.05 [1.02, 1.09]), lower HDL cholesterol (OR: 0.57 [0.44, 0.73]) and smoking (ex-smoker status OR: 0.84 [0.66, 0.98]) as consistent drivers. RF highlighted a similar set of key drivers, also including waist circumference, triglycerides and cardiovascular comorbidities. Clustering of the high-CRP group was almost entirely driven by smoking status and nicotine dependence. When grouped into actionable targets, the identified drivers accounted for 16% of variance in CRP status, with obesity emerging as most dominant contributor. This pattern was most pronounced in females; in males it was more diffuse, with a more prominent role for smoking.Conclusions: We propose a decision tree framework where CRP can serve as a first-line screening marker for inflammation in SMI, with subsequent steps focusing on the main contributing factors to guide targeted interventions. Priority should be given to targeting obesity and metabolic dysregulation. Among females, hyperuricemia represents the next most appropriate target, whereas in males, smoking warrants greater attention. This stepwise approach provides a route from a nonspecific biomarker to targeted treatment strategies and should be validated in prospective studies
Sanchez, C., Garnier, P. & Jacob, E. (2026) Penser les cheminements des enfants en maternelle en France et au Québec. Entre textes institutionnels et pratiques quotidiennes. Dans Conus, X., Pirard, F., Garnier, P. (dir.). Transition en petite enfance. Edition Alphil, p. 63-85.
No full textInternational audienceThis chapter examines early childhood transitions as children's "pathways" over the course of a school year in France and Quebec. The objective is to understand how teachers strive to produce traces and define markers of these mostly invisible pathways, through ethnographic classroom observation and interviews with teachers. We are particularly interested in how teachers' daily practices with children-pupils take account of institutional prescriptions. Our analysis reveals that, while teaching approaches are largely similar in both countries, a key difference emerges in the scope of "competency" domains documented: tightly focused on language and mathematical learning in France, the affective and social domains are given greater consideration in Quebec
From haemodynamics to kidney risk: AI-based early prediction validated in general and burn ICU populations
No full textInternational audienceAbstract Aims Acute kidney injury (AKI) is a frequent and severe complication in critically ill patients with cardiovascular instability. Current risk scores rely on delayed renal biomarkers such as serum creatinine (sCr) and blood urea nitrogen (BUN). We aimed to develop and validate machine learning (ML) models predicting AKI and major adverse kidney events (MAKE) exclusively from systemic physiological and haemodynamic data. Methods and results Two ML models were trained on the MIMIC-IV database: one including (sCr+/BUN+) and one excluding (sCr−/BUN−) renal parameters. External validation was performed in the eICU database and in a cohort of burn ICU patients from AP-HP. Model performance was assessed for early AKI and MAKE prediction up to 100 h before diagnosis. Systemic haemodynamic and physiological variables were the strongest predictors of AKI. In MIMIC-IV, the sCr−/BUN− model achieved auROC 0.78 at 72 h, approaching the sCr+/BUN+ model. In eICU, it outperformed the biomarker-based model at later time points (auROC 0.73). In the burn ICU cohort—representing a high-stress systemic environment—it maintained robust accuracy (auROC 0.75 at 24 h, 0.77 at 72 h). For MAKE prediction, the sCr−/BUN− model achieved auROC 0.87 (burn cohort), 0.67 (eICU), and 0.77 (MIMIC-IV). Median lead time for AKI prediction exceeded 70 h. Conclusion AI models based solely on non-renal parameters can accurately predict AKI and MAKE, even under extreme systemic stress such as severe burns. Haemodynamic signatures carry sufficient information to anticipate kidney dysfunction well in advance, opening the way to real-time, proactive cardio-renal risk stratification in ICU patients with acute heart failure, cardiogenic shock, and after cardiac surgery
Long-term air pollution exposure and mental health in French adults of the CONSTANCES cohort: Role of black carbon independently of PM2.5
No full textInternational audienceAmbient air pollution could be associated with poor mental health. Black carbon (BC) has been highlighted as a crucial component of particulate matter; however, its isolated role independent from the total particulate matter mass has been poorly studied. Our study aimed to examine the associations between long-term exposure to particulate matter with a diameter <2.5 μm (PM2.5), BC and nitrogen dioxide (NO2) and psychological distress in the French CONSTANCES cohort and to assess the role of BC independently of PM2.5. This cross-sectional study included 104,146 adults. Psychological distress was assessed in 2019 using the General Health Questionnaire-12 (GHQ-12). Annual concentrations of PM2.5, BC, and NO2 estimated from land-use regression models at each participant's residential address. Negative binomial models with different covariate adjustments were used. A residuals method was used to assess the independent role of BC. Incident rate ratios (IRR) per an interquartile range (IQR) increase in exposure to each pollutant were calculated. Stratified analyses by age, sex, education and season were also conducted. Mean exposures were 9.38 μg/m3 for PM2.5 (IQR = 2.6), 1.15 × 10−5/m for BC (IQR = 0.5) and 19.1 μg/m3 for NO2 (IQR = 11.5). Exposure to each pollutant was significantly associated with higher psychological distress (IRR (95 % CI): 1.052 (1.014–1.092) for PM2.5, 1.078 (1.055–1.101) for BC, and 1.082(1.057–1.109) for NO2). Stronger associations were found for men, elderly, lower-educated, and during warm season. BC residuals were significantly associated with higher psychological distress when regressed on PM2.5 (1.055 (1.039–1.071)) and when regressed on NO2 (1.067(1.041–1.093)). Exposure to ambient air pollution was associated with psychological distress, with BC showing a deleterious role independently of PM2.5 and NO2
MObyGaze: a film dataset of multimodal objectification densely annotated by experts
No full textCharacterizing and quantifying gender representation disparities in audiovisual storytelling contents is necessary to grasp how stereotypes may perpetuate on screen. In this article, we consider the high-level construct of objectification and introduce a new AI task to the ML community: characterize and quantify complex multimodal (visual, speech, audio) temporal patterns producing objectification in films. Building on film studies and psychology, we define the construct of objectification in a structured thesaurus involving 5 sub-constructs manifesting through 11 concepts spanning 3 modalities. We introduce the Multimodal Objectifying Gaze (MObyGaze) dataset, made of 20 movies annotated densely by experts for objectification levels and concepts over freely delimited segments: it amounts to 6072 segments over 43 hours of video with fine-grained localization and categorization. We formulate different learning tasks, propose and investigate best ways to learn from the diversity of labels among a low number of annotators, and benchmark recent vision, text and audio models, showing the feasibility of the task. We make our code and our dataset available to the community and described in the Croissant format: https://anonymous.4open.science/r/MObyGaze-F600/ Related worksWe position our contributions with respect to works on: analyses of biases in film datasets, annotation of audiovisual and mulimodal contents, and dataset creation for interpretive tasks.</div
