Huge plastic bezoar: a rare cause of gastrointestinal obstruction

Bezoars are rare causes of gastrointestinal obstruction. Basically, they are of four types: trichobezoars, phytobezoars, pharmacobezoars, and lactobezoars. Some rare types of bezoars are also known. In this article a unique case of plastic bezoars is presented. We describe a girl aged 14 years who ingested large amounts of plastic material used for knitting chairs and charpoys. The conglomerate of plastic threads, entrapped food material and other debris, formed a huge mass occupying the whole stomach and extended into small bowel

Applications of Sequential Learning for Medical Image Classification

Purpose: The aim of this work is to develop a neural network training framework for continual training of small amounts of medical imaging data and create heuristics to assess training in the absence of a hold-out validation or test set. Materials and Methods: We formulated a retrospective sequential learning approach that would train and consistently update a model on mini-batches of medical images over time. We address problems that impede sequential learning such as overfitting, catastrophic forgetting, and concept drift through PyTorch convolutional neural networks (CNN) and publicly available Medical MNIST and NIH Chest X-Ray imaging datasets. We begin by comparing two methods for a sequentially trained CNN with and without base pre-training. We then transition to two methods of unique training and validation data recruitment to estimate full information extraction without overfitting. Lastly, we consider an example of real-life data that shows how our approach would see mainstream research implementation. Results: For the first experiment, both approaches successfully reach a ~95% accuracy threshold, although the short pre-training step enables sequential accuracy to plateau in fewer steps. The second experiment comparing two methods showed better performance with the second method which crosses the ~90% accuracy threshold much sooner. The final experiment showed a slight advantage with a pre-training step that allows the CNN to cross ~60% threshold much sooner than without pre-training. Conclusion: We have displayed sequential learning as a serviceable multi-classification technique statistically comparable to traditional CNNs that can acquire data in small increments feasible for clinically realistic scenarios

Tumeur stromale du mésentère: une cause inhabituelle d’une masse abdominale

Les tumeurs stromales gastro-intestinales (GIST) sont les tumeurs mésenchymateuses les plus fréquentes du tractus digestif. Elles représentent une entité nosologique individualisée depuis la découverte de l'expression quasi-constante de la protéine c-Kit détectée par la coloration immunohistochimique de l'antigène CD117. Des tumeurs avec les mêmes caractéristiques morphologiques et immuno-phénotypiques peuvent rarement apparaître en dehors du tractus gastro-intestinal. Nous rapportons le cas d'une jeune patiente de 34 ans présentant une masse tumorale mésentérique se révélant être de nature stromale sans aucun contact avec la paroi intestinale. Il s'agit d'une localisation très rare des tumeurs stromales à laquelle il faut penser en préopératoire afin d'avoir une conduite thérapeutique adaptée et efficace

Kyste hydatique pelvien primitif. une localisation inhabituelle

Nous rapportons le cas exceptionnel d’un kyste hydatique pelvien primitif chez une jeune fille de 17 ans. Le diagnostic était suspecté en pré-opératoire devant l’origine de la patiente (zone rural endémique), l’aspect échographique et tomodensitométrique puis il a était confirmé en per-opératoire devant le contenu kystique typique .une kysto-périkystectomie partielle était réalisée par voie de pfannenstiel avec drainage de la cavité résiduelle. Les suites étaient simples et la patiente n’a pas présentée de récidives sur un recul de 3ans. Le but de notre travail est de mettre le point une localisation peu commune de la maladie hydatique d’en rapporter les particularités cliniques et radiologiques et surtout d’en discuter les différentes théories éthiopathogéniques et les modalités du traitement qui reste avant tout chirurgical

Flow-to-fracture transition and pattern formation in a discontinuous shear thickening fluid

Recent theoretical and experimental work suggests a frictionless-frictional transition with increasing inter-particle pressure explains the extreme solid-like response of discontinuous shear thickening suspensions. However, analysis of macroscopic discontinuous shear thickening flow in geometries other than the standard rheometry tools remain scarce. Here we use a Hele-Shaw cell geometry to visualise gas-driven invasion patterns in discontinuous shear thickening cornstarch suspensions. We plot quantitative results from pattern analysis in a volume fraction-pressure phase diagram and explain them in context of rheological measurements. We observe three distinct pattern morphologies: viscous fingering, dendritic fracturing, and system-wide fracturing, which correspond to the same packing fraction ranges as weak shear thickening, discontinuous shear thickening, and shear-jammed regimes

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14