Les effets hémodynamiques de la dexmédétomidine chez le nouveau-né admis aux soins intensifs pédiatriques

co-diplomation avec l'Université de Lille (France)Introduction : La dexmédétomidine, un agoniste α-2 adrénergique, est de plus en plus utilisée pour la sédation et l’analgésie des nouveau-nés nécessitant un support ventilatoire grâce à son absence d’effet dépressif respiratoire. Cependant, elle impacte le système cardio-vasculaire via le système nerveux autonome et pourrait modifier la balance des systèmes sympathiques et parasympathiques (S/PS) et ainsi augmenter la survenue de bradycardie et d’hypotension. Objectifs : Cette étude a pour objectif de décrire l’utilisation de la dexmédétomidine et d’analyser l’impact de la perfusion continue sur le système cardio-vasculaire et sur la balance du système S/PS des nouveau-nés admis aux soins intensifs pédiatriques. Méthodes : Nous avons inclus les nouveau-nés (< 28 jours) qui ont reçu une perfusion de dexmédétomidine. Les données ont été collectées à partir d’une base de données haute résolution qui collecte prospectivement, outre les données du dossier clinique informatisé, toutes les données issues des moniteurs et des pousses-seringues. La balance S/PS a été estimée via l’analyse de la variabilité du rythme cardiaque (VRC) sur les électrocardiogrammes, extraits de la base de données, en calculant le rapport basses fréquences / hautes fréquences (LH/HF). Les concentrations plasmatiques (CP) de dexmédétomidine ont été simulées en utilisant un modèle pharmacocinétique de population développé dans une population similaire à la nôtre. Les variables ont été comparées sur différentes périodes au cours des 12 premières heures de perfusion à l'aide d'analyses ANOVA et la relation avec les variables pharmacocinétiques a été calculée par régression linéaire. Résultats : Vingt-trois nouveau-nés ont été inclus dans l’étude. Par rapport aux valeurs témoins, la fréquence cardiaque (FC) a diminué de 19±4 bpm (p=0,002) et la pression artérielle moyenne (PAM) a diminué de 8,6±2,5 mmHg (p=0,023) pendant les 12 premières heures de perfusion. La FC diminuait avec l’augmentation de la CP simulée (coefficient de régression de -8,6 [95% IC : -1,9 ; -6,3]). Le rapport LF/HF a montré une tendance à la diminution sans retrouver de différence statistiquement significative (p=0,077). Conclusion : La FC et la PAM semblent diminuées sous dexmédétomidine tout en restant dans des valeurs normales pour le nouveau-né. L’ampleur de la diminution de la FC semble reliée à l’augmentation des CP. De plus, la tendance de diminution de la balance S/PS suggère une réduction du contrôle sympathique chez le nouveau-né recevant la dexmédétomidine. Cependant, l’ensemble de ces résultats doit encore être confirmé par des études plus larges et prospectives.Introduction: Dexmedetomidine, an adrenergic α-2 agonist, has been increasingly used for sedation and analgesia in neonates due to its lack of respiratory depressant effect. However, it impacts the cardiovascular system via the autonomic nervous system. It could modify the sympathetic/parasympathetic balance (S/PS balance) and result in an increased risk of bradycardia and hypotension. Objectives: This study aimed to describe the utilization of intravenous dexmedetomidine infusion and characterize its impact on the hemodynamics, including S/PS balance, in critically ill neonates. Methods: We retrospectively included neonates (<28 days) who received a dexmedetomidine infusion. Data were collected from a high-fidelity database that prospectively collects all data from the monitors, infusion pumps, and electronic medical records. The S/PS balance was assessed by analyzing heart rate variability from database electrocardiograms. This was done using the low/high frequency (LF/HF) ratio with frequency band adapted for neonatal population. Dexmedetomidine plasma concentrations (PC) were simulated using a published population pharmacokinetic model. The variables were compared at different times during the first 12 hours of infusion using ANOVA analyses and their association with pharmacokinetics variables was evaluated using linear regression. Results: A total of 23 neonates were included. When compared to baseline values, heart rate (HR) values decreased by 19±4 bpm (p=0.002), and mean arterial pressure (MAP) decreased by 8.6±2.5 mmHg (p=0.023) 12 hours following the initiation of infusion. HR decreased proportionally with increased simulated PC over 12 hours (regression coefficient: -8.6 [95% IC: -10.9 - 6.3]). The LF/HF ratio tended to decrease over the same period, although this was not statistically significant (p=0.077). Conclusion: In critically ill neonates, HR and MAP decreased on dexmedetomidine infusion but remained within normal neonatal values. The decrease in HR was associated with higher simulated PC. Moreover, the decreasing trend of S/PS balance suggest a reduction in sympathetic control in the neonate receiving dexmedetomidine. However, these results need to be confirmed with larger prospective studies

Detecting of a Patient's Condition From Clinical Narratives Using Natural Language Representation

The rapid progress in clinical data management systems and artificial intelligence approaches enable the era of personalized medicine. Intensive care units (ICUs) are the ideal clinical research environment for such development because they collect many clinical data and are highly computerized environments. We designed a retrospective clinical study on a prospective ICU database using clinical natural language to help in the early diagnosis of heart failure in critically ill children. The methodology consisted of empirical experiments of a learning algorithm to learn the hidden interpretation and presentation of the French clinical note data. This study included 1386 patients' clinical notes with 5444 single lines of notes. There were 1941 positive cases (36 % of total) and 3503 negative cases classified by two independent physicians using a standardized approach. The multilayer perceptron neural network outperforms other discriminative and generative classifiers. Consequently, the proposed framework yields an overall classification performance with 89 % accuracy, 88 % recall, and 89 % precision. Furthermore, a generative autoencoder learning algorithm was proposed to leverage the sparsity reduction that achieved 91% accuracy, 91% recall, and 91% precision. This study successfully applied learning representation and machine learning algorithms to detect heart failure from clinical natural language in a single French institution. Further work is needed to use the same methodology in other institutions and other languages.Comment: Submitting to IEEE Transactions on Biomedical Engineering. arXiv admin note: text overlap with arXiv:2104.0393

Probiotic Sonicates Selectively Induce Mucosal Immune Cells Apoptosis through Ceramide Generation via Neutral Sphingomyelinase

This is an open-access article distributed under the terms of the Creative Commons Attribution License.-- et al.[Background]: Probiotics appear to be beneficial in inflammatory bowel disease, but their mechanism of action is incompletely understood. We investigated whether probiotic-derived sphingomyelinase mediates this beneficial effect. [Methodology/Principal Findings]: Neutral sphingomyelinase (NSMase) activity was measured in sonicates of the probiotic L. brevis (LB) and S. thermophilus (ST) and the non-probiotic E. coli (EC) and E. faecalis (EF). Lamina propria mononuclear cells (LPMC) were obtained from patients with Crohn's disease (CD) and Ulcerative Colitis (UC), and peripheral blood mononuclear cells (PBMC) from healthy volunteers, analysing LPMC and PBMC apoptosis susceptibility, reactive oxygen species (ROS) generation and JNK activation. In some experiments, sonicates were preincubated with GSH or GW4869, a specific NSMase inhibitor. NSMase activity of LB and ST was 10-fold that of EC and EF sonicates. LB and ST sonicates induced significantly more apoptosis of CD and UC than control LPMC, whereas EC and EF sonicates failed to induce apoptosis. Pre-stimulation with anti-CD3/CD28 induced a significant and time-dependent increase in LB-induced apoptosis of LPMC and PBMC. Exposure to LB sonicates resulted in JNK activation and ROS production by LPMC. NSMase activity of LB sonicates was completely abrogated by GW4869, causing a dose-dependent reduction of LB-induced apoptosis. LB and ST selectively induced immune cell apoptosis, an effect dependent on the degree of cell activation and mediated by bacterial NSMase. [Conclusions]: These results suggest that induction of immune cell apoptosis is a mechanism of action of some probiotics, and that NSMase-mediated ceramide generation contributes to the therapeutic effects of probiotics.The funding sources included grants from Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Ministerio de Ciencia e Innovación (SAF2005-00280 and SAF2008-03676 to MS, FIS2009-00056 to AM, SAF2009-11417 to JCF), Fundación Ramón Areces (to MS), the National Institutes of Health (DK30399 and DK50984 to CF) and the Research Center for Liver and Pancreatic Diseases funded by the United States National Institute for Alcohol Abuse and Alcoholism (P50 AA 11999 to JCF).Peer reviewe

Lettre de Gvillavme sans-pevr, aux trouppes de Mazarin.

Imitation of "Lettre de Guillaume Sans Peur aux debandés de la cour," 1615.Harangue to Mazarin's troops to turn their arms against the cardinal.Signatures: A-B².Title page vignette.Dated at end: Au Chesne Vert, aux Marais, ce 20. feurier.Signed at end: Guillaume sans-peur.MoreauMode of access: Internet.Part of the Mazarinade Collection

Pour une prescription et une délivrance raisonnées du médicament vétérinaire (l exemple de la mise en place du "Guide de bonnes pratiques du médicament vétérinaire")

Le marché du médicament vétérinaire, structuré en différents étages, atteint un chiffre d affaires de 896 millions d euros en France en 2003. Les animaux de rente sont les utilisateurs majoritaires de médicament. Les étages structurant le marché correspondent aux étapes de la vie du médicament : fabrication, distribution en gros, distribution au détail par les trois ayant droit). La Loi du 29 mai 1975 pose les bases réglementaires de la fabrication, de la prescription et de la distribution du médicament vétérinaire. Son application semble toutefois ne pas être totalement effective, notamment en ce qui concerne la distribution. La situation actuelle de la distribution du médicament vétérinaire en France pourrait être à l origine de risques de santé publique. Toutefois, la réglementation connaît depuis quelques années de profondes réformes ayant pour but de sécuriser l utilisation du médicamenrt vétérinaire, mais engendre des problèmes de disponibilité. Le vétérinaire praticien rural doit développer des outils pour répondre à la fois aux attentes réglementaires et professionnelles.TOULOUSE3-BU Santé-Centrale (315552105) / SudocTOULOUSE-EN Vétérinaire (315552301) / SudocSudocFranceF

Perspective socio-économique des politiques publiques encourageant l’usage raisonné des antibiotiques en élevage.

International audienceAntimicrobial use (AMU) in animal agriculture unavoidably leads to the selection of resistant bacteria,potentially transmitted to humans. To address this public health threat, international and national publicpolicies are implemented, but very few have been assessed neither ex ante nor ex post. In thisperspective, we first discuss economic approaches regarding AMU. We then discuss the potentialeconomic and social effects of regulatory policy instruments as well as voluntary approachesimplemented to achieve a reduction of AMU.L’usage des antibiotiques (UAB) en santé animale comme en santé humaine entraine la sélection debactéries résistantes. Leur transmission à l’homme par contact direct ou via la chaine alimentaireconstitue un risque de santé public, qui a conduit à la mise en place de politiques publiques visant àréduire l’UAB. Toutefois, les évaluations socio-économiques de ces politiques sont quasi absentes.Dans cet article, nous proposons de discuter en premier lieu comment l’UAB peut être envisagé sous unangle économique, puis nous abordons comment les politiques publiques, réglementaires ouvolontaires, peuvent contribuer à la réduction de l’UAB en productions animales, en passant en revueleurs performances sociales et économiques

The economics of antimicrobial resistance in veterinary medicine: Optimizing societal benefits through mesoeconomic approaches from public and private perspectives

International audienceAntimicrobial resistance (AMR) is a global public health threat driven by a combination of factors, including antimicrobial use (AMU) and interactions among microorganisms, people, animals and the environment. The emergence and spread of AMR in veterinary medicine (AMR-V) arising from AMU in veterinary medicine (AMU-V) can be linked to individuals' economic behaviour and institutional context. We highlight the limitations of current microeconomic approaches and propose a mesoeconomic conceptual model of AMR-V that integrates actors' strategic and routine behaviours in their context from a dynamic perspective using the concepts of externality, globality and futurity. The global solution to AMR-V management relies on a trade-off between i) the global externality assessment of AMU-V with respect to AMR-V (public perspective) and ii) farm- or value chain-level marginal abatement cost evaluation (private perspective). The improvements realized by the proposed mesoeconomic conceptual model include i) the simultaneous fight against the emergence and spread of AMR-V and ii) a local decrease in AMU-V without any loss of competitiveness for private actors due to the development of adequate production standards. A set of generic equations describing the stepwise change in the scale of analysis is finally proposed. This original contribution to the global challenge of AMR through a mesoeconomic approach bring substantial improvement for better AMU. This model can be considered a way to smoothly promote institutional change and a call for public policies that support public private partnership in the development of adequate incentives. The model requires further development prior to its application in a given value-chain or territory

Adaptation of Autoencoder for Sparsity Reduction From Clinical Notes Representation Learning

When dealing with clinical text classification on a small dataset, recent studies have confirmed that a well-tuned multilayer perceptron outperforms other generative classifiers, including deep learning ones. To increase the performance of the neural network classifier, feature selection for the learning representation can effectively be used. However, most feature selection methods only estimate the degree of linear dependency between variables and select the best features based on univariate statistical tests. Furthermore, the sparsity of the feature space involved in the learning representation is ignored. Goal: Our aim is, therefore, to access an alternative approach to tackle the sparsity by compressing the clinical representation feature space, where limited French clinical notes can also be dealt with effectively. Methods: This study proposed an autoencoder learning algorithm to take advantage of sparsity reduction in clinical note representation. The motivation was to determine how to compress sparse, high-dimensional data by reducing the dimension of the clinical note representation feature space. The classification performance of the classifiers was then evaluated in the trained and compressed feature space. Results: The proposed approach provided overall performance gains of up to 3&#x0025; for each test set evaluation. Finally, the classifier achieved 92&#x0025; accuracy, 91&#x0025; recall, 91&#x0025; precision, and 91&#x0025; f1-score in detecting the patient&#x2019;s condition. Furthermore, the compression working mechanism and the autoencoder prediction process were demonstrated by applying the theoretic information bottleneck framework. Clinical and Translational Impact Statement&#x2014; An autoencoder learning algorithm effectively tackles the problem of sparsity in the representation feature space from a small clinical narrative dataset. Significantly, it can learn the best representation of the training data because of its lossless compression capacity compared to other approaches. Consequently, its downstream classification ability can be significantly improved, which cannot be done using deep learning models