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Gastric diverticula are a rare condition; they are divided into true diverticula, which are congenital, and pseudodiverticula, which are acquired. True diverticula are usually located in the posterior fundus wall, below the esophagogastric junction, and appear on abdominal computed tomography as a cystic mass that is commonly taken for an extra digestive mass, especially adrenal mass. We report the case of an asymptomatic 24-year-old female patient with gastric diverticulum who was mistakenly diagnosed in ultrasound with a renal mass.

The brown tumor is a non-neoplastic lesion resulting from an abnormality in bone metabolism in the context of primary, secondary, or tertiary hyperparathyroidism. They can affect any bone structure and be single or multiple. They are usually located on the long bones, pelvis, ribs, and collarbones. Facial localization is rare and is found in only 4.5% of brown tumors dominated by mandibular involvement. The treatment of a brown tumor depends on several elements: etiology, location, and symptomatology. It can regress or disappear after the treatment of hyperparathyroidism, thus avoiding surgical removal. Otherwise, excision of this mass is performed. We illustrate through this case a brown mandibular tumor revealing secondary hyperparathyroidism in a patient with a history of end-stage chronic renal failure on hemodialysis and associated neurofibromatosis type 1.

BackgroundMechanisms contributing to tissue remodeling of the infarcted heart following cell-based therapy remain elusive. While cell-based interventions have the potential to influence the cardiac healing process, there is little direct evidence of preservation of functional myocardium.AimThe aim of the study was to investigate tissue remodeling in the infarcted heart following human embryonic stem cell-derived endothelial cell product (hESC-ECP) therapy.Materials and methodsFollowing coronary artery ligation (CAL) to induce cardiac ischemia, we investigated infarct size at 1 day post-injection in media-injected controls (CALM, n = 11), hESC-ECP-injected mice (CALC, n = 10), and dead hESC-ECP-injected mice (CALD, n = 6); echocardiography-based functional outcomes 14 days post-injection in experimental (CALM, n = 13; CALC, n = 17) and SHAM surgical mice (n = 4); and mature infarct size (CALM and CALC, both n = 6). We investigated ligand–receptor interactions (LRIs) in hESC-ECP cell populations, incorporating a publicly available C57BL/6J mouse cardiomyocyte-free scRNAseq dataset with naive, 1 day, and 3 days post-CAL hearts.ResultsHuman embryonic stem cell-derived endothelial cell product injection reduces the infarct area (CALM: 54.5 ± 5.0%, CALC: 21.3 ± 4.9%), and end-diastolic (CALM: 87.8 ± 8.9 uL, CALC: 63.3 ± 2.7 uL) and end-systolic ventricular volume (CALM: 56.4 ± 9.3 uL, CALC: 33.7 ± 2.6 uL). LRI analyses indicate an alternative immunomodulatory effect mediated via viable hESC-ECP-resident signaling.ConclusionDelivery of the live hESC-ECP following CAL modulates the wound healing response during acute pathological remodeling, reducing infarct area, and preserving functional myocardium in this relatively acute model. Potential intrinsic myocardial cellular/hESC-ECP interactions indicate that discreet immunomodulation could provide novel therapeutic avenues to improve cardiac outcomes following myocardial infarction.

Aim/Purpose: In this paper, we highlight the need to monitor and diagnose adaptive e-learning systems requirements at runtime to develop a better understanding of their behavior during learning activities and improve their design. Our focus is to reveal which learning requirements the adaptive system is satisfying while still evolving and to provide specific recommendations regarding what actions should be taken and which relevant features are needed to help meet the specified learning requirements. Background: Adaptive e-learning systems research has long focused on user modeling and social learning to personalize each learner experience, while fewer instruments are reported to assess the quality of the solutions provided by such adaptive systems and to investigate their design problems. The design problems may emerge due to ever-evolving requirements being statically specified at design stages and to the changing environments that can be difficult to control and observe. The combination of some or all of these factors can lead to a definition of inconsistent or insufficient adaptation rules, which in turn may prevent these systems from providing appropriate resources to learners even if the needed ones have been accounted for within the knowledge space. Methodology: An empirical study has been performed to check and validate the behavior of a real-world adaptive e-learning system under four stated requirements. The study used a novel monitoring and diagnosing tool that reads the collected data from the system and checks its behavior against constraints that are derived automatically from the requirements specification. Contribution: The results provide statistical insights and highlight some issues related to requirements compliance at runtime, which helped us detect unforeseen instructional design issues. Recommendations for Practitioners: The study suggests that diagnosing requirements compliance at runtime can be an essential means to increase the confidence about their adaptive e-learning systems capabilities at runtime. Recommendations for Researchers: The study suggests that further research for developing specific indicators related to requirements compliance is needed in the field of adaptive e-learning systems. Future Research: Future work includes the study of possible improvement of our diagnostic tool using probabilistic reasoning.

In Brazil, the development of gullies constitutes widespread land degradation, especially in the state of South Mato Grosso, where fighting against this degradation has become a priority for policy makers. However, the environmental and anthropogenic factors that promote gully development are multiple, interact, and present a complexity that can vary by locality, making their prediction difficult. In this framework, a database was constructed for the Rio Ivinhema basin in the southern part of the state, including 400 georeferenced gullies and 13 geo-environmental descriptors. Multivariate statistical analysis was performed using principal component analysis (PCA) to identify the processes controlling the variability in gully development. Susceptibility maps were created through four machine learning models: multivariate discriminant analysis (MDA), logistic regression (LR), classification and regression tree (CART), and random forest (RF). The predictive performance of the models was analyzed by five evaluation indices: accuracy (ACC), sensitivity (SST), specificity (SPF), precision (PRC), and Receiver Operating Characteristic curve (ROC curve). The results show the existence of two major processes controlling gully erosion. The first is the surface runoff process, which is related to conditions of slightly higher relief and higher rainfall. The second also reflects high surface runoff conditions, but rather related to high drainage density and downslope, close to the river network. Human activity represented by peri-urban areas, construction of small earthen dams, and extensive rotational farming contribute significantly to gully formation. The four machine learning models yielded fairly similar results and validated susceptibility maps (ROC curve > 0.8). However, we noted a better performance of the random forest (RF) model (86% and 89.8% for training and test, respectively, with an ROC curve value of 0.931). The evaluation of the contribution of the parameters shows that susceptibility to gully erosion is not governed primarily by a single factor, but rather by the interconnection between different factors, mainly elevation, geology, precipitation, and land use.

Cardiovascular disease (CVD) is the leading cause of mortality worldwide claiming almost 17. 9 million deaths annually. A primary cause is atherosclerosis within the coronary arteries, which restricts blood flow to the heart muscle resulting in myocardial infarction (MI) and cardiac cell death. Despite substantial progress in the management of coronary heart disease (CHD), there is still a significant number of patients developing chronic heart failure post-MI. Recent research has been focused on promoting neovascularisation post-MI with the ultimate goal being to reduce the extent of injury and improve function in the failing myocardium. Cardiac cell transplantation studies in pre-clinical models have shown improvement in cardiac function; nonetheless, poor retention of the cells has indicated a paracrine mechanism for the observed improvement. Cell communication in a paracrine manner is controlled by various mechanisms, including extracellular vesicles (EVs). EVs have emerged as novel regulators of intercellular communication, by transferring molecules able to influence molecular pathways in the recipient cell. Several studies have demonstrated the ability of EVs to stimulate angiogenesis by transferring microRNA (miRNA, miR) molecules to endothelial cells (ECs). In this review, we describe the process of neovascularisation and current developments in modulating neovascularisation in the heart using miRNAs and EV-bound miRNAs. Furthermore, we critically evaluate methods used in cell culture, EV isolation and administration.

Urban flooding is a complex natural hazard, driven by the interaction between several parameters related to urban development in a context of climate change, which makes it highly variable in space and time and challenging to predict. In this study, we apply a multivariate analysis method (PCA) and four machine learning algorithms to investigate and map the variability and vulnerability of urban floods in the city of Tangier, northern Morocco. Thirteen parameters that could potentially affect urban flooding were selected and divided into two categories: geo-environmental parameters and socio-economic parameters. PCA processing allowed identifying and classifying six principal components (PCs), totaling 73% of the initial information. The scores of the parameters on the PCs and the spatial distribution of the PCs allow to highlight the interconnection between the topographic properties and urban characteristics (population density and building density) as the main source of variability of flooding, followed by the relationship between the drainage (drainage density and distance to channels) and urban properties. All four machine learning algorithms show excellent performance in predicting urban flood vulnerability (ROC curve > 0.9). The Classifications and Regression Tree and Support Vector Machine models show the best prediction performance (ACC = 91.6%). Urban flood vulnerability maps highlight, on the one hand, low lands with a high drainage density and recent buildings, and on the other, higher, steep-sloping areas with old buildings and a high population density, as areas of high to very-high vulnerability.