Instrumentation mécanique des conduits radiculaires. Une étude à l’aide du M.E.B. et analyse informatisée d’image

The effectiveness of ultrasonic and sonic instrumentation in eliminating the smear layer from instrumented root channels. The results were examined with S.E.M. and the effective areas of dental diffusion were calculated using the computerized image analyzer. The agents used: citric acid at concentrations of 10, 25 and 50% as well as 15% EDTA, proved their efficiency with both types of mechanical instrumentation. However, the area of diffusion found was always greater using the ultrasonic instrumentation technique as opposed to the sonic instrumentation technique. 1, 2.5 and 5.25% sodium hypochlorite, as well as 10 volume hydrogen peroxide were not effective in eliminating the smear layer using both types of instrumentation.Nous étudions l’efficacité de l’instrumentation ultrasonique et sonique pour éliminer la boue dentinaire des conduits radiculaires instrumentés. Les résultats ont été objectivés au moyen du M.E.B. et nous avons calculé les aires effectives de diffusion dentinaire avec l’analyseur d’images. Les agents employés: acide citrique à 10, 25 et 50% de concentration, ainsi que l’EDTA à 15% ont confirmé leur efficacité avec les deux types d’instrumentation mécanique. Cependant l’aire de diffusion vérifiée, est toujours supérieure avec la technique d’instrumentation ultrasonique par rapport à la technique d’instrumentation sonique. L’hypochlorite sodique à 1, 2,5 et 5,25%; ainsi que l’eau oxygénée à 10 volumes, se sont avérés inefficaces dans l’élimination de la boue dentinaire avec les deux types d’instrumentation

Aire de diffusion dentinaire lors de la préparation manuelle des conduits radiculaires

The purpose of this study is to show the existence of a correlation between the premolar and molar clenching forces obtained during a voluntary clenching exercise. The study concerned 32 volunteers aged 21 to 28 with no manducatory problems. The forces were obtained using a device with four Kiowa traducers arranged in a complete Wheatstone bridge. The analysis of the results obtained showed that there was a positive correlation between maximum molar and premolar. The analysis also shows that these results are a direct application of the theory of momentum. This enables us to propose a simple biophysical model to explain the functioning of the mandicatory system.Le but de cette étude est de démontrer l’existence d’une corrélation entre les forces prémolaires et molaires enregistrées pendant un exercice de fermeture volontaire forcée de la mandibule. L’étude porte sur 32 volontaires de 21 à 28 ans, sans pathologie de l’appareil manducateur. Les forces sont enregistrées avec un capteur à quatre jauges Kiowa monté en pont complet. La possibilité d’appliquer le théorème des moments aux valeurs recueillies, montre qu’il existe une corrélation positive entre la force maximale développée au niveau prémolaire et la force maximale développée au niveau molaire. Ceci nous permet de proposer un modèle biophysique simple pour expliquer le fonctionnement de l’appareil manducateur

Delay in diagnosis of influenza A (H1N1)pdm09 virus infection in critically ill patients and impact on clinical outcome

Background: Patients infected with influenza A (H1N1)pdm09 virus requiring admission to the ICU remain an important source of mortality during the influenza season. The objective of the study was to assess the impact of a delay in diagnosis of community-acquired influenza A (H1N1)pdm09 virus infection on clinical outcome in critically ill patients admitted to the ICU. Methods: A prospective multicenter observational cohort study was based on data from the GETGAG/SEMICYUC registry (2009–2015) collected by 148 Spanish ICUs. All patients admitted to the ICU in which diagnosis of influenza A (H1N1)pdm09 virus infection had been established within the first week of hospitalization were included. Patients were classified into two groups according to the time at which the diagnosis was made: early (within the first 2 days of hospital admission) and late (between the 3rd and 7th day of hospital admission). Factors associated with a delay in diagnosis were assessed by logistic regression analysis. Results: In 2059 ICU patients diagnosed with influenza A (H1N1)pdm09 virus infection within the first 7 days of hospitalization, the diagnosis was established early in 1314 (63.8 %) patients and late in the remaining 745 (36.2 %). Independent variables related to a late diagnosis were: age (odds ratio (OR) = 1.02, 95 % confidence interval (CI) 1.01–1.03, P < 0.001); first seasonal period (2009–2012) (OR = 2.08, 95 % CI 1.64–2.63, P < 0.001); days of hospital stay before ICU admission (OR = 1.26, 95 % CI 1.17–1.35, P < 0.001); mechanical ventilation (OR = 1.58, 95 % CI 1.17–2.13, P = 0.002); and continuous venovenous hemofiltration (OR = 1.54, 95 % CI 1.08–2.18, P = 0.016). The intra-ICU mortality was significantly higher among patients with late diagnosis as compared with early diagnosis (26.9 % vs 17.1 %, P < 0.001). Diagnostic delay was one independent risk factor for mortality (OR = 1.36, 95 % CI 1.03–1.81, P < 0.001). Conclusions: Late diagnosis of community-acquired influenza A (H1N1)pdm09 virus infection is associated with a delay in ICU admission, greater possibilities of respiratory and renal failure, and higher mortality rate. Delay in diagnosis of flu is an independent variable related to death

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients. © The Author(s) 2024