Une tumeur du vagin à ne pas méconnaitre, l’adénocarcinome mésonephrique: à propos d’un cas et revue de la literature

L'adénocarcinome mésonéphrique du vagin est une tumeur maligne extrêmement rare avec uniquement trois cas publiés dans la littérature jusqu'à maintenant. Il dérive des reliquats embryonnaires des canaux mésonéphriques au niveau du vagin. Nous rapportons un cas d'adénocarcinome mésonéphrique du vagin survenant chez une femme de 50 ans, et révélé par une masse polyploïde du vagin. L'IRM a montré un envahissement du périnée et de la branche inférieure du pubis. L'étude anatomo-pathologique était en faveur d'un adénocarcinome mésonéphrique dont les cellules tumorales expriment la pancytokératine et le CD10. Elles ne sont pas marquées par les anticorps anti récepteurs ostrogéniques et progestatifs. La patiente a été adressée pour radiothérapie avant la prise en charge chirurgicale. Les auteurs soulignent à travers cette observation les aspects étiopathogéniques, histologiques et thérapeutiques de cette tumeur rare

Comparison of a fluorometric assay kit with high-performance liquid chromatography for the assessment of serum retinol concentration.

Background: Although high-performance liquid chromatography (HPLC) is the commonly used method for the analysis of retinol in biological samples, simple and rapid test kits are available.Objectives: This study compared a rapid test kit (ICHECK Fluoro®) to HPLC for the assessment of serum retinol concentrations.Methods: For the analysis by HPLC, sample preparation included standard deproteinization and extraction phases. The analysis by ICHECK was performed by injecting serum into IEX reagent vials (n=89) and mixing manually for separation. After precipitation of the proteins, the vial was introduced into the chamber of the ICHECK Fluoro and analysed at 0 min (ICHECK0min) and 15 min later (ICHECK15min). Bland and Altman approach was applied to test the agreement between HPLC and ICHECK.Results: Mean HPLC, ICHECK0min and ICHECK15min values were 421.2±106.0 μg/L, 423.1±118.3 μg/L and 413.2±107.6 μg/L, respectively. Retinol concentrations significantly decreased in the IEX solution over time (p<0.001). No significant proportional bias was observed between HPLC and ICHECK0min (r-0.038, p=0.73) and ICHECK15min (r=- 0.024, p=0.82). Fixed biases (HPLC minus ICHECK) for ICHECK0min and ICHECK15min were respectively -1.9±23.1 μg/l (p=0.45) and 8.0±22.7 μg/l (p=0.002).Conclusion: ICHECK Fluoro may offer a reliable mean for assessing serum retinol for measurements performed with no significant time delay.Keywords: HPLC, ICHECK Fluoro, serum retinol, test kit, vitamin A status

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Ensembles of Convolutional Neural Networks for Survival Time Estimation of High-Grade Glioma Patients from Multimodal MRI

Glioma is the most common type of primary malignant brain tumor. Accurate survival time prediction for glioma patients may positively impact treatment planning. In this paper, we develop an automatic survival time prediction tool for glioblastoma patients along with an effective solution to the limited availability of annotated medical imaging datasets. Ensembles of snapshots of three dimensional (3D) deep convolutional neural networks (CNN) are applied to Magnetic Resonance Image (MRI) data to predict survival time of high-grade glioma patients. Additionally, multi-sequence MRI images were used to enhance survival prediction performance. A novel way to leverage the potential of ensembles to overcome the limitation of labeled medical image availability is shown. This new classification method separates glioblastoma patients into long- and short-term survivors. The BraTS (Brain Tumor Image Segmentation) 2019 training dataset was used in this work. Each patient case consisted of three MRI sequences (T1CE, T2, and FLAIR). Our training set contained 163 cases while the test set included 46 cases. The best known prediction accuracy of 74% for this type of problem was achieved on the unseen test set

Discovery of a Generalization Gap of Convolutional Neural Networks on COVID-19 X-Rays Classification

A number of recent papers have shown experimental evidence that suggests it is possible to build highly accurate deep neural network models to detect COVID-19 from chest X-ray images. In this paper, we show that good generalization to unseen sources has not been achieved. Experiments with richer data sets than have previously been used show models have high accuracy on seen sources, but poor accuracy on unseen sources. The reason for the disparity is that the convolutional neural network model, which learns features, can focus on differences in X-ray machines or in positioning within the machines, for example. Any feature that a person would clearly rule out is called a confounding feature. Some of the models were trained on COVID-19 image data taken from publications, which may be different than raw images. Some data sets were of pediatric cases with pneumonia where COVID-19 chest X-rays are almost exclusively from adults, so lung size becomes a spurious feature that can be exploited. In this work, we have eliminated many confounding features by working with as close to raw data as possible. Still, deep learned models may leverage source specific confounders to differentiate COVID-19 from pneumonia preventing generalizing to new data sources (i.e. external sites). Our models have achieved an AUC of 1.00 on seen data sources but in the worst case only scored an AUC of 0.38 on unseen ones. This indicates that such models need further assessment/development before they can be broadly clinically deployed. An example of fine-tuning to improve performance at a new site is given

Optimal use of procalcitonin to rule out bacteremia in patients with possible viral infections

Objective: During the winter, many patients present with suspected infection that could be a viral or a bacterial (co)infection. The aim of this study is to investigate whether the optimal use of procalcitonin (PCT) is different in patients with and without proven viral infections for the purpose of excluding bacteremia. We hypothesize that when a viral infection is confirmed, this lowers the probability of bacteremia and, therefore, influences the appropriate cutoff of procalcitonin. Methods: This study was conducted in the emergency department of an academic medical center in The Netherlands in the winter seasons of 2019 and 2020. Adults (>18 years) with suspected infection, in whom a blood culture and a rapid polymerase chain reaction test for influenza was performed were included. Results: A total of 546 patients were included of whom 47 (8.6%) had a positive blood culture. PCT had an area under the curve of 0.85, 95% confidence interval (95% CI) 0.80–0.91, for prediction of bacteremia. In patients with a proven viral infection (N = 212) PCT < 0.5 μg/L had a sensitivity of 100% (95% CI 63.1–100) and specificity of 81.2% (95% CI 75.1–86.3) to exclude bacteremia. In patients without a viral infection, the procalcitonin cutoff point of < 0.25 μg/L showed a sensitivity of 87.2% (95% CI 72.6–95.7) and specificity of 64.1 % (95% CI 58.3–69.6). Conclusion: In patients with a viral infection, our findings suggest that a PCT concentration of <0.50 μg/L makes bacteremia unlikely. However, this finding needs to be confirmed in a larger population of patients with viral infections, especially because the rate of coinfection in our cohort was low

Figure historique et personnage romanesque

Le général d’artillerie Jean-Pierre Lacombe Saint-Michel (1753-1812) fut une figure de la Révolution et de l’Empire: député du Tarn à la Convention, membre du Comité de salut public, commissaire aux armées, ambassadeur à Naples, gouverneur de Barcelone… Colosse impétueux, il fut aussi un homme des Lumières, lecteur de Rousseau, auteur de réflexions, de souvenirs, de poèmes, et l’époux inconsolable d’une jeune protestante hollandaise morte à 33 ans. Son descendant en ligne directe, Claude Simon (1913-2005), prix Nobel de littérature en 1985, avait été fasciné enfant par le buste de marbre du général trônant dans le salon familial. Il a fait de lui, sous les initiales L. S. M., un personnage central de son roman Les Géorgiques (Les Éditions de Minuit, 1981). Réunissant deux grands thèmes de l’oeuvre, la Terre et la Guerre, il prend pour matériau d’écriture la correspondance retrouvée de son ancêtre, qui concerne ses fonctions militaires et politiques, mais aussi la conduite à distance de son domaine du Tarn, d’où le titre emprunté au célèbre poème de Virgile. Interrogeant à travers ce personnage, comme en un miroir, son propre rapport à l’histoire, à la nature, à l’écriture, Claude Simon nous fait aussi réfléchir au devenir du roman en un moment – le début des années 1980 – où il se tourne à nouveau vers l’histoire, vers la mémoire individuelle et collective, vers les archives et les traces du passé, tout en prenant acte du «soupçon» qu’a porté le Nouveau Roman sur l’idée même de «restitution». Claude Simon apparaît ainsi comme un précurseur, et pour beaucoup comme un modèle