EEG-fMRI has gained increasing importance in epilepsy pre-surgical diagnosis. However, 40-70% of EEG-fMRI recordings in patients lack interictal epileptiform discharges (IEDs) during the scan, which could be overcome by detecting matching topography maps. We tried to validate this method in clinical settings taking various electroclinical factors into consideration. Eleven patients who had undergone EEG-fMRI during pre-surgical evaluation for drug-resistant epilepsy and who had had clinical long-term video-EEG were studied. Spike-related blood oxygen level-dependent (BOLD) maps were created using IEDs occurring during the EEG-fMRI scan. Separate maps were then generated from IEDs marked on the clinical long-term EEG recordings, which were averaged to produce topographical IED maps and correlated with the EEGs recorded inside the scanner yielding a correlation coefficient time course. Epileptogenic zones were defined by an expert panel during pre-surgical evaluation and validated by an epilepsy surgery resulting in a good outcome. Both techniques’ performance was evaluated according to factors including arousal during IED recording, IED topography and lateralization, lesion type, and localization. Topography-related EEG-fMRI yielded more specific results compared to the spike-related method. Repertaxin Superficial lesion location and ipsilateral IED seem to result in a higher concordance of BOLD maps. The polarity of BOLD responses may be lesion-dependent, and both positive and negative BOLD changes may be associated with the irritative zone. Topography-related EEG-fMRI may show improved specificity especially for superficial lesions producing ipsilateral spikes. This method can be used as an alternative either in the absence of spikes during the simultaneous EEG-fMRI acquisition or to sharpen a diffusely activated BOLD-map.Discovering relations of cross-type biomedical entities is crucial for biology research. A large amount of potential or indirect connected biological relations is hidden in millions of biomedical literatures and biological databases. The previous rules-based and deep learning approaches rely on plenty of manual annotations, which is laborious, time-consuming and unsatisfactory. It is necessary to be able to combine available annotated gene databases, chemical, genomic, clinical and other types of data repositories as domain knowledge to assist the extraction of biological entity relations from numerous literatures. Under this scenario, this paper proposes BioGraphSAGE model, a Siamese graph neural network with structured databases as domain knowledge to extract biological entity relations from literatures. Our model combines both biological semantic features and positional features to improve the recognition of relations between distant entities in the same literature. The experiment results show that BioGraphSAGE achieves the best F1 score among other relation extraction models on smaller annotated samples. Moreover, the proposed model can still maintain a F1 score of 0.526 without using annotated training samples.The novel SARS-CoV-2 has spread to virtually all countries of the world infecting millions of people, the medical burden of this disease obviously being enormous. The gonads of both sexes are among the organs that may be affected by COVID-19 and/or may affect the severity of the disease. The clinical spectrum of SARS-CoV-2 infection clearly differs between genders. The current evidence indicates that the underlying mechanism of such an interaction could be associated with genetic, hormonal, and immunological differences, as well as with gender differences in such habits as smoking and alcohol use. On the other hand, there are controversies as to how and to what extent the gonads could be affected by COVID-19, possibly impacting upon sex steroids, fertility, and other functions. This review underlines the possible mechanisms that could clarify these questions concerning COVID-19 and the gonads. In addition, reference is made to potential new treatment modalities presently under investigation, these supported by accumulating data published in the recent literature.The generation of pluripotent stem cells from adult somatic cells by cell reprogramming has put a whole new perspective on stem cell biology and stem cell-based regenerative medicine. Cell reprogramming acts through the introduction of key genes that regulate and maintain the pluripotent cell state. In this chapter, we describe the optimized protocol for the efficient isolation of fibroblasts from a skin punch biopsy and the subsequent easy and effective generation of integration-free induced pluripotent stem cell (iPSC) colonies forcing the expression of specific factors by non-modified RNAs.Paracellular permeability of the intestinal epithelium is a feature of the intestinal barrier, which plays an important role in the physiology of gut and the whole organism. Intestinal paracellular permeability is controlled by complex processes and is involved in the passage of ions and fluids (called pore pathway) and macromolecules (called leak pathway) through tight junctions, which seal the intercellular space. Impairment of intestinal paracellular permeability is associated with several diseases. The identification of a defect in intestinal paracellular permeability may help to understand the implication of gut barrier as a cause or a consequence in human pathology. Here we describe two complementary methods to evaluate alteration of paracellular permeability in cell culture, using the human intestinal cell line Caco-2 and its clone Caco-2/TC7.

In quantitative computed tomography (CT), manual selection of the intensity calibration phantom’s region of interest is necessary for calculating density (mg/cm

) from the radiodensity values (Hounsfield units HU). However, as this manual process requires effort and time, the purposes of this study were to develop a system that applies a convolutional neural network (CNN) to automatically segment intensity calibration phantom regions in CT images and to test the system in a large cohort to evaluate its robustness.

This cross-sectional, retrospective study included 1040 cases (520 each from two institutions) in which an intensity calibration phantom (B-MAS200, Kyoto Kagaku, Kyoto, Japan) was used. A training dataset was created by manually segmenting the phantom regions for 40 cases (20 cases for each institution). The CNN model’s segmentation accuracy was assessed with the Dice coefficient, and the average symmetric surface distance was assessed through fourfold cross-validation. Further, absolute difference of HU was compared between manually and automatically segmented regions.