Phytochemical analysis, antiproliferative against k562 humam chronic myelogenus leukemia, antiviral and hypoglycaemic activities of cedrus species and medicinal plants native from Libanon

There are four kinds of Cedar, three of them naturally present in the Mediterranean Sea region: Cedrus libani, in Lebanon, Syria and Turky, Cedrus atlantica in Algeria and Morocco, Cedrus brevifolia in Cyprus Island While Cedrus deodara in Himalaya Mountains . The wood essential oils of C. libani, C .atalantica and C. deodara inhibited the proliferation of k 562 cell line with IC50 Value of 23.38, 59.37 and 37.09 µg.ml-1 respectively. Meanwhile Cedrus libani wood oils showed a percentage of erythroide differentiation of 15% at the concentration of 5µg.ml-1another side Cedrus deodara wood oil was found a percentage of erythroide differentiation of 20% at the concentration 25µg.ml-1 and Cedrus atlantica wood oils indicated a percentage of erythroide differentiation of 12% at concentration 10µg.ml-1 The essential oils obtained from different officinal plants of Lebanon, belonging to the Magnoliophyta division, have been tested for their antiproliferative activity on humanerythroleukemic K562 cells. Satureja montana showed the most interesting biological activity in inhibiting the cell growth and inducing erythroid differentiation of K562 cells The essential oil of Satureja montana was therefore analyzed using a GC/MS (gas chromatography/mass spectrometry) system in order to identify the major constituents and compare them with analysis performed on Satureja hortensis. The major constituent of Satureja hortensis being carvacrol (50.61%) and that of Satureja montana being α-terpineol (12.66%). Satureja Montana essential oil displayed different natural derivatives characterized by higher activity than those present in Satureja hortensis. The common active principles are α-pinene, γ-terpinene, 4-terpineol, α-terpineol, τ-cadinene, τ-cadinol and caryophyllene. Both caryophyllene and α-terpineol showed important antiproliferative effects on K562 cells

Performance Evaluation of Redundant Disk Array Support for Transaction Recovery

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryNational Aeronautics and Space Administration / NAG 1-613Department of the Navy / N00014-91-J-128

The Use of Medical Imaging Request Forms as Trigger Tools to Detect Intra-Hospital Adverse Events: A Pilot Study

Aim: To evaluate the contribution of medical imaging request forms as trigger tools to detect patient adverse event (AE) occurring during hospitalization. Material and Methods: This is a retrospective study in a single institution. Between January and June 2019, the hospital information system (HIS) was fetched for request forms of radiological examinations performed for inpatients >48 hours after the admission date. The investigated request forms were: Doppler ultrasound of the upper limbs, Doppler ultrasound of the lower limbs, and the repetition of three consecutive requests of chest radiographs within 24 hrs, to detect upper or lower limb venous thrombosis, or AEs related to the respiratory system, respectively. Patients’ medical charts and radiological examinations were evaluated to document the presence or absence of an AE. The frequencies of AEs in the three groups of trigger tools were compared to corresponding control groups, matched according to age, sex and length of stay. Results: Among a total of 2798 hospital admissions during the study period, there were 74 files triggered by the three types of radiological request forms. There were 6/24 AE (25%) related to upper limb venous thrombosis, 4/33 (12.1%) AE related to lower limb venous thrombosis, and 6/17 (35.3%) AE related to the respiratory system. For all the trigger tools, the frequency of AE in the study groups was significantly higher than that in the control groups. Conclusion: Medical imaging requests could be used as potential trigger tools to detect adverse events related to hospital stay

Structural study of thin films prepared from tungstate glass matrix by Raman and X-ray absortion spectroscopy

Thin films were prepared using glass precursors obtained in the ternary system NaPO3-BaF2-WO3 and the binary system NaPO3-WO3 with high concentrations of WO3 (above 40% molar). Vitreous samples have been used as a target to prepare thin films. Such films were deposited using the electron beam evaporation method onto soda-lime glass substrates. Several structural characterizations were performed by Raman spectroscopy and X-ray Absorption Near Edge Spectroscopy (XANES) at the tungsten LI and LIII absorption edges. XANES investigations showed that tungsten atoms are only sixfold coordinated (octahedral WO6) and that these films are free of tungstate tetrahedral units (WO4). In addition, Raman spectroscopy allowed identifying a break in the linear phosphate chains as the amount of WO3 increases and the formation of P-O-W bonds in the films network indicating the intermediary behavior of WO6 octahedra in the film network. Based on XANES data, we suggested a new attribution of several Raman absorption bands which allowed identifying the presence of W-O- and W=O terminal bonds and a progressive apparition of W-O-W bridging bonds for the most WO3 concentrated samples (above 40% molar) attributed to the formation of WO6 clusters.FAPESPCNPqCAPE

Detection, prediction and prevention of healthcare acquired adverse events with artificial intelligence, associating rules-based approach and machine learning

La sécurité des patients est considérée comme une grande priorité en santé publique. Les systèmes de santé au niveau mondial tentent de mobiliser des ressources pour la prévention des évènements indésirables cliniques chez les patients. Selon l'Organisation Mondiale de la Santé, des études ont montré qu'en moyenne un patient sur dix est sujet à un événement indésirable (EI) lors de son hospitalisation dans les pays à revenu élevé. L'estimation pour les pays à revenu faible et intermédiaire suggère même que jusqu'à un patient sur quatre sont concernés. Malgré une dynamique positive au cours des vingt dernières années, d'importantes opportunités d'amélioration subsistent, découlant souvent d'une mise en œuvre inefficace ou inadéquate des actions d'amélioration de la sécurité. Ainsi, les EI restent l'une des principales causes de morbidité et de mortalité en santé, entraînant des pressions économiques et sociales supplémentaires pour les systèmes de santé. L’analyse approfondie des raisons pour lesquelles l'incidence élevée des EI persiste semble identifier les facteurs suivants :- Le manque d'outils fiables et efficaces pour la mesure et la surveillance des EI cliniques. - L'incohérence dans l'utilisation de techniques de prévention recommandées - Le manque d'accessibilité aux techniques de prévention recommandées - Le manque d'utilisabilité de certaines solutions technologiques - L'existence de domaines cliniques pour lesquels il n'y a pas de solutions de prévention prouvées, dénommés les "angles morts" de la sécurité patient. L'intelligence artificielle (IA) détient un potentiel important qui peut être appliqué à l'identification, la prédiction et la prévention des EI pour les patients. En fait, elle peut être utilisée pour détecter les événements liés à la sécurité des patients, améliorer les performances des alarmes cliniques, prédire le risque de survenue d’EI et améliorer l’adhérence aux bonnes pratiques. D’autre part, quand bien même l’IA commence à générer des applications dans certains domaines cliniques, peu ont été développés pour le domaine de l’amélioration de la sécurité des patient et la grande majorité de ces applications sont encore au stade expérimental ou insuffisamment validées pour des scénarios d'utilisation en contexte réel.En fait, le développement d'applications basées sur l'IA pose de nombreux défis liés à la conception, à la validation et à l'acceptation par les utilisateurs qui doivent être relevés avant que ces applications puissent être transformées en systèmes d’aide à la décision clinique (SADC) ou d’amélioration. Dans le cadre de cette thèse, notre objectif est d'explorer le potentiel de l'IA pour le domaine d’application des événements indésirables cliniques. Après une présentation de l’état de l’art, notre point de départ sera un système d'outil de détection non basé sur l'IA pour la mesure automatisée des événements indésirables hospitaliers, conçu avant la thèse.Après avoir identifié et analysé les avantages et les inconvénients de ce genre de système, nous concevrons deux modèles basés sur l'apprentissage automatique, consacrés respectivement à la prédiction des réadmissions à l'hôpital et à la détérioration clinique des patients. Nous présenterons également un SADC basé sur l'IA qui associe le deuxième modèle de prédiction à des règles cliniques élaborées par des experts médicaux afin de gérer le risque de détérioration clinique.Finalement, nous analyserons les spécificités de l'utilisation de l'apprentissage automatique par rapport aux algorithmes basés sur des règles dans de telles applications, et comment ces deux techniques peuvent être utilisées en synergie.Patient safety is considered as one of the biggest public health priorities, with healthcare systems around the globe attempting to mobilize resources for the prevention of patient harms. According to the World Health Organization, research studies show that an average of one in ten patients is subject to an adverse event (AE) while receiving hospital care in high- income countries. The estimate for low- and middle- income countries suggests that up to one in four patients is harmed. Despite a positive momentum in the last twenty years, substantial opportunities for improvement remain, stemming from inefficient or inadequate implementation of safety actions, patient harm remaining one of the leading causes of morbidity and mortality in healthcare, causing further economic and social burden on healthcare systems. There are a number of reasons why the problem of the high incidence of patient harm is still prevailing despite more than two decades of awareness and research in the domain of patient safety :- Lack of reliable and efficient tools for the measure and surveillance of clinical AEs.- Inconsistency in use of proven prevention techniques- Lack of accessibility to proven prevention techniques- Lack of usability of some technological solutions- Areas with insufficient/no proven prevention techniques : the safety “blind spots”Artificial Intelligence (AI) promises to hold an important potential in identifying, predicting and preventing patient harm. In fact, it can be applied to detect patient safety events and improve performance of clinical alarms, provide prediction of various patient safety events, and improve adherence to best practices.In fact, the systematic detection of clinical adverse events (AE) is still not available in healthcare institutions and systems. Without valid measurements of this type, it is almost impossible to assess the true impact of safety improvement actions on clinical outcomes. Furthermore, if such events can be predicted in due time, a significant potential in terms of prevention can be unleashed for the best interest of the patients and the whole healthcare system.Moreover, while certain clinical applications of AI getting gradually mature, the patient safety domain has still not witnessed widely recognized AI-based applications. As for experimental applications that are published, the vast majority have still not been sufficiently validated for real-world use scenarios. In fact, the development of AI-based applications holds many challenges related to design, validation and user acceptance that need to be tackled before any such application can be transformed into a valuable tool for the improvement of patient safety.In this thesis, our objective is to explore the potential of AI when applied to the domain of Clinical Adverse Events.Following a presentation of the state of the art, our starting point will be a non-AI based trigger-tool system for the automated measurement of hospital acquired adverse events, designed before the thesis.After identifying and analyzing the advantages and shortcomings of such a system, we will design two AI-based machine learning models, devoted to the prediction of hospital readmissions and patient clinical deterioration, respectively. We will also design an AI-based Clinical Decision Support application that integrates the second machine learning model with clinical rules written by medical experts. We will highlight specifically the added value that such systems could potentially produce both to improve current tools performances and to contribute to the patient safety efforts. In addition to that we will analyze the specificities of using machine learning versus rules-based algorithms in such applications, and how these two techniques can be used in synergy

Détection, prédiction et prévention des évènements indésirables liés aux soins via l’intelligence artificielle, en associant approche basée sur les règles de décision et l’apprentissage automatique

Patient safety is considered as one of the biggest public health priorities, with healthcare systems around the globe attempting to mobilize resources for the prevention of patient harms. According to the World Health Organization, research studies show that an average of one in ten patients is subject to an adverse event (AE) while receiving hospital care in high- income countries. The estimate for low- and middle- income countries suggests that up to one in four patients is harmed. Despite a positive momentum in the last twenty years, substantial opportunities for improvement remain, stemming from inefficient or inadequate implementation of safety actions, patient harm remaining one of the leading causes of morbidity and mortality in healthcare, causing further economic and social burden on healthcare systems. There are a number of reasons why the problem of the high incidence of patient harm is still prevailing despite more than two decades of awareness and research in the domain of patient safety :- Lack of reliable and efficient tools for the measure and surveillance of clinical AEs.- Inconsistency in use of proven prevention techniques- Lack of accessibility to proven prevention techniques- Lack of usability of some technological solutions- Areas with insufficient/no proven prevention techniques : the safety “blind spots”Artificial Intelligence (AI) promises to hold an important potential in identifying, predicting and preventing patient harm. In fact, it can be applied to detect patient safety events and improve performance of clinical alarms, provide prediction of various patient safety events, and improve adherence to best practices.In fact, the systematic detection of clinical adverse events (AE) is still not available in healthcare institutions and systems. Without valid measurements of this type, it is almost impossible to assess the true impact of safety improvement actions on clinical outcomes. Furthermore, if such events can be predicted in due time, a significant potential in terms of prevention can be unleashed for the best interest of the patients and the whole healthcare system.Moreover, while certain clinical applications of AI getting gradually mature, the patient safety domain has still not witnessed widely recognized AI-based applications. As for experimental applications that are published, the vast majority have still not been sufficiently validated for real-world use scenarios. In fact, the development of AI-based applications holds many challenges related to design, validation and user acceptance that need to be tackled before any such application can be transformed into a valuable tool for the improvement of patient safety.In this thesis, our objective is to explore the potential of AI when applied to the domain of Clinical Adverse Events.Following a presentation of the state of the art, our starting point will be a non-AI based trigger-tool system for the automated measurement of hospital acquired adverse events, designed before the thesis.After identifying and analyzing the advantages and shortcomings of such a system, we will design two AI-based machine learning models, devoted to the prediction of hospital readmissions and patient clinical deterioration, respectively. We will also design an AI-based Clinical Decision Support application that integrates the second machine learning model with clinical rules written by medical experts. We will highlight specifically the added value that such systems could potentially produce both to improve current tools performances and to contribute to the patient safety efforts. In addition to that we will analyze the specificities of using machine learning versus rules-based algorithms in such applications, and how these two techniques can be used in synergy.La sécurité des patients est considérée comme une grande priorité en santé publique. Les systèmes de santé au niveau mondial tentent de mobiliser des ressources pour la prévention des évènements indésirables cliniques chez les patients. Selon l'Organisation Mondiale de la Santé, des études ont montré qu'en moyenne un patient sur dix est sujet à un événement indésirable (EI) lors de son hospitalisation dans les pays à revenu élevé. L'estimation pour les pays à revenu faible et intermédiaire suggère même que jusqu'à un patient sur quatre sont concernés. Malgré une dynamique positive au cours des vingt dernières années, d'importantes opportunités d'amélioration subsistent, découlant souvent d'une mise en œuvre inefficace ou inadéquate des actions d'amélioration de la sécurité. Ainsi, les EI restent l'une des principales causes de morbidité et de mortalité en santé, entraînant des pressions économiques et sociales supplémentaires pour les systèmes de santé. L’analyse approfondie des raisons pour lesquelles l'incidence élevée des EI persiste semble identifier les facteurs suivants :- Le manque d'outils fiables et efficaces pour la mesure et la surveillance des EI cliniques. - L'incohérence dans l'utilisation de techniques de prévention recommandées - Le manque d'accessibilité aux techniques de prévention recommandées - Le manque d'utilisabilité de certaines solutions technologiques - L'existence de domaines cliniques pour lesquels il n'y a pas de solutions de prévention prouvées, dénommés les "angles morts" de la sécurité patient. L'intelligence artificielle (IA) détient un potentiel important qui peut être appliqué à l'identification, la prédiction et la prévention des EI pour les patients. En fait, elle peut être utilisée pour détecter les événements liés à la sécurité des patients, améliorer les performances des alarmes cliniques, prédire le risque de survenue d’EI et améliorer l’adhérence aux bonnes pratiques. D’autre part, quand bien même l’IA commence à générer des applications dans certains domaines cliniques, peu ont été développés pour le domaine de l’amélioration de la sécurité des patient et la grande majorité de ces applications sont encore au stade expérimental ou insuffisamment validées pour des scénarios d'utilisation en contexte réel.En fait, le développement d'applications basées sur l'IA pose de nombreux défis liés à la conception, à la validation et à l'acceptation par les utilisateurs qui doivent être relevés avant que ces applications puissent être transformées en systèmes d’aide à la décision clinique (SADC) ou d’amélioration. Dans le cadre de cette thèse, notre objectif est d'explorer le potentiel de l'IA pour le domaine d’application des événements indésirables cliniques. Après une présentation de l’état de l’art, notre point de départ sera un système d'outil de détection non basé sur l'IA pour la mesure automatisée des événements indésirables hospitaliers, conçu avant la thèse.Après avoir identifié et analysé les avantages et les inconvénients de ce genre de système, nous concevrons deux modèles basés sur l'apprentissage automatique, consacrés respectivement à la prédiction des réadmissions à l'hôpital et à la détérioration clinique des patients. Nous présenterons également un SADC basé sur l'IA qui associe le deuxième modèle de prédiction à des règles cliniques élaborées par des experts médicaux afin de gérer le risque de détérioration clinique.Finalement, nous analyserons les spécificités de l'utilisation de l'apprentissage automatique par rapport aux algorithmes basés sur des règles dans de telles applications, et comment ces deux techniques peuvent être utilisées en synergie

Diabète après transplantation hépatique, facteurs prédictifs de survenue et responsabilité du traitement immunosuppresseur

PARIS-BIUM (751062103) / SudocCentre Technique Livre Ens. Sup. (774682301) / SudocSudocFranceF

Comparaison des performances catalytiques des systèmes oxydes de Ce, Al et Mn dans l'oxydation totale des particules carbonées et le vaporeformage du méthanol

DUNKERQUE-BU Lettres Sci.Hum. (591832101) / SudocSudocFranceF

Comparison of Machine Learning Algorithms for Classifying Adverse-Event Related 30-ay Hospital Readmissions: Potential Implications for Patient Safety

International audienceStudies in the last decade have focused on identifying patients at risk of readmission using predictive models, in an objective to decrease costs to the healthcare system. However, real-time models specifically identifying readmissions related to hospital adverse-events are still to be elaborated. A supervised learning approach was adopted using different machine learning algorithms based on features available directly from the hospital information system and on a validated dataset elaborated by a multidisciplinary expert consensus panel. Accuracy results upon testing were in line with comparable studies, and variable across algorithms, with the highest prediction given by Artificial Neuron Networks. Features importances relative to the prediction were identified, in order to provide better representation and interpretation of results. Such a model can pave the way to predictive models for readmissions related to patient harm, the establishment of a learning platform for clinical quality measurement and improvement, and in some cases for an improved clinical management of readmitted patients