Hepatotoxicity of cantharidin is associated with the altered bile acid metabolism

Cantharidin (CTD) is an effective antitumor agent. However, it exhibits significant hepatotoxicity, the mechanism of which remains unclear. In this study, biochemical and histopathological analyses complemented with ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS)-based targeted metabolomic analysis of bile acids (BAs) were employed to investigate CTD-induced hepatotoxicity in rats. Sixteen male and female Sprague–Dawley rats were randomly divided into two groups: control and CTD (1.0 mg/kg) groups. Serum and liver samples were collected after 28 days of intervention. Biochemical, histopathological, and BA metabolomic analyses were performed for all samples. Further, the key biomarkers of CTD-induced hepatotoxicity were identified via multivariate and metabolic pathway analyses. In addition, metabolite–gene–enzyme network and Kyoto Encyclopedia of Genes and Genomes pathway analyses were used to identify the signaling pathways related to CTD-induced hepatotoxicity. The results revealed significantly increased levels of biochemical indices (alanine aminotransferase, aspartate aminotransferase, and total bile acid). Histopathological analysis revealed that the hepatocytes were damaged. Further, 20 endogenous BAs were quantitated via UHPLC-MS/MS, and multivariate and metabolic pathway analyses of BAs revealed that hyocholic acid, cholic acid, and chenodeoxycholic acid were the key biomarkers of CTD-induced hepatotoxicity. Meanwhile, primary and secondary BA biosynthesis and taurine and hypotaurine metabolism were found to be associated with the mechanism by which CTD induced hepatotoxicity in rats. This study provides useful insights for research on the mechanism of CTD-induced hepatotoxicity.     

Epilepsy in patients with insulin-dependent diabetes and relation to glutamic acid decarboxylase 65

Objectives:To report if the association of epilepsy in pediatric patients (below the age of 15 years) with Insulin-dependent Diabetes (IDDM) at King Fahad Medical City (KFMC) is higher than the prevalence of epilepsy in the same age group (who have no IDDM) in our community. Consequently, we would determine if there is a relationship between the presence of epilepsy in diabetic children and the presence of positive antiGAD65 antibodies.Methods:This cohort study included 305 pediatric patients below the age of 15 years with Insulin-dependent Diabetes Mellitus (IDDM). They were randomly recruited at the Pediatric Endocrinology Clinic in KFMC. The patients’ caregivers were given a questionnaire between December 2015 till March 2019 to determine the seizure disorder history. There was also a retrospective review of 214 patients’ files for anti-GAD 65 positivity.Results:Our study found a significant relation between the presence of epilepsy in children with IDDM. Therefore, we could confirm the relationship between the existence of epilepsy in children with IDDM and having positive GAD65 antibodies.Conclusion:Our study supports the presence of consistent relation between having IDDM and having epilepsy in children and between the latter and the presence of positive GAD65 antibodies.

Insulin dependent diabetes Mellitus (IDDM) is a common condition in children and adolescents worldwide and so is epilepsy.1,2 Recently, there were increasing reports suggesting a potential association between having IDDM and the occurrence of epilepsy.3 Their association might represent simply a chance to relate their underlying mechanisms. However, the cause-effect relationship is not fully well defined. Literature from other countries have shown the increased prevalence of seizure disorders in this group of patients.4,5 There are scarce studies in the literature investigating IDDM characteristics contributing to having epilepsy, including positive GAD 65 antibodies. In this study that ran in King Fahad Medical City, Riyadh, Kingdom of Saudi Arabia (KSA) we are aiming to determine if the prevalence of epilepsy among 1DDM children under the age of 15 years (in our center) is higher than controls (same age without IDDM), and to check the positivity of anti-GAD 65 amongst those patients in order to find if there is a relationship between epilepsy in children with diabetes and the presence of positive GAD65 Antibodies.     

A Stacked Generalization of 3D Orthogonal Deep Learning Convolutional Neural Networks for Improved Detection of White Matter Hyperintensities in 3D FLAIR Images

BACKGROUND AND PURPOSE:Accurate and reliable detection of white matter hyperintensities and their volume quantification can provide valuable clinical information to assess neurologic disease progression. In this work, a stacked generalization ensemble of orthogonal 3D convolutional neural networks, StackGen-Net, is explored for improving automated detection of white matter hyperintensities in 3D T2-FLAIR images.MATERIALS AND METHODS:Individual convolutional neural networks in StackGen-Net were trained on 2.5D patches from orthogonal reformatting of 3D-FLAIR (n = 21) to yield white matter hyperintensity posteriors. A meta convolutional neural network was trained to learn the functional mapping from orthogonal white matter hyperintensity posteriors to the final white matter hyperintensity prediction. The impact of training data and architecture choices on white matter hyperintensity segmentation performance was systematically evaluated on a test cohort (n = 9). The segmentation performance of StackGen-Net was compared with state-of-the-art convolutional neural network techniques on an independent test cohort from the Alzheimer’s Disease Neuroimaging Initiative-3 (n = 20).RESULTS:StackGen-Net outperformed individual convolutional neural networks in the ensemble and their combination using averaging or majority voting. In a comparison with state-of-the-art white matter hyperintensity segmentation techniques, StackGen-Net achieved a significantly higher Dice score (0.76 [SD, 0.08], F1-lesion (0.74 [SD, 0.13]), and area under precision-recall curve (0.84 [SD, 0.09]), and the lowest absolute volume difference (13.3% [SD, 9.1%]). StackGen-Net performance in Dice scores (median = 0.74) did not significantly differ (P = .22) from interobserver (median = 0.73) variability between 2 experienced neuroradiologists. We found no significant difference (P = .15) in white matter hyperintensity lesion volumes from StackGen-Net predictions and ground truth annotations.CONCLUSIONS:A stacked generalization of convolutional neural networks, utilizing multiplanar lesion information using 2.5D spatial context, greatly improved the segmentation performance of StackGen-Net compared with traditional ensemble techniques and some state-of-the-art deep learning models for 3D-FLAIR.

White matter hyperintensities (WMHs) correspond to pathologic features of axonal degeneration, demyelination, and gliosis observed within cerebral white matter.¹ Clinically, the extent of WMHs in the brain has been associated with cognitive impairment, Alzheimer’s disease and vascular dementia, and increased risk of stroke.^2,3 The detection and quantification of WMH volumes to monitor lesion burden evolution and its correlation with clinical outcomes have been of interest in clinical research.^4,5 Although the extent of WMHs can be visually scored,⁶ the categoric nature of such scoring systems makes quantitative evaluation of disease progression difficult. Manually segmenting WMHs is tedious, prone to inter- and intraobserver variability, and is, in most cases, impractical. Thus, there is an increased interest in developing fast, accurate, and reliable computer-aided automated techniques for WMH segmentation.Convolutional neural network (CNN)-based approaches have been successful in several semantic segmentation tasks in medical imaging.⁷ Recent works have proposed using deep learning–based methods for segmenting WMHs using 2D-FLAIR images.^8-11 More recently, a WMH segmentation challenge¹² was also organized (http://wmh.isi.uu.nl/) to facilitate comparison of automated segmentation of WMHs of presumed vascular origin in 2D multislice T2-FLAIR images. Architectures that used an ensemble of separately trained CNNs showed promising results in this challenge, with 3 of the top 5 winners using ensemble-based techniques.¹²Conventional 2D-FLAIR images are typically acquired with thick slices (3–4 mm) and possible slice gaps. Partial volume effects from a thick slice are likely to affect the detection of smaller lesions, both in-plane and out-of-plane. 3D-FLAIR images, with isotropic resolution, have been shown to achieve higher resolution and contrast-to-noise ratio¹³ and have shown promising results in MS lesion detection using 3D CNNs.¹⁴ Additionally, the isotropic resolution enables viewing and evaluation of the images in multiple planes. This multiplanar reformatting of 3D-FLAIR without the use of interpolating kernels is only possible due to the isotropic nature of the acquisition. Network architectures that use information from the 3 orthogonal views have been explored in recent works for CNN-based segmentation of 3D MR imaging data.¹⁵ The use of data from multiple planes allows more spatial context during training without the computational burden associated with full 3D training.¹⁶ The use of 3 orthogonal views simultaneously mirrors how humans approach this segmentation task.Ensembles of CNNs have been shown to average away the variances in the solution and the choice of model- and configuration-specific behaviors of CNNs.¹⁷ Traditionally, the solutions from these separately trained CNNs are combined by averaging or using a majority consensus. In this work, we propose the use of a stacked generalization framework (StackGen-Net) for combining multiplanar lesion information from 3D CNN ensembles to improve the detection of WMH lesions in 3D-FLAIR. A stacked generalization¹⁸ framework learns to combine solutions from individual CNNs in the ensemble. We systematically evaluated the performance of this framework and compared it with traditional ensemble techniques, such as averaging or majority voting, and state-of-the-art deep learning techniques.     

Myeloid-derived suppressor cell (MDSC) key genes analysis in rat anti-CD28-induced immune tolerance kidney transplantation

BackgroundIn the field of transplantation, inducing immune tolerance in recipients is of great importance. Blocking co-stimulatory molecule using anti-CD28 antibody could induce tolerance in a rat kidney transplantation model. Myeloid-derived suppressor cells (MDSCs) reveals strong immune suppressive abilities in kidney transplantation. Here we analyzed key genes of MDSCs leading to transplant tolerance in this model.MethodsMicroarray data of rat gene expression profiles under accession number {"type":"entrez-geo","attrs":{"text":"GSE28545","term_id":"28545"}}GSE28545 in the Gene Expression Omnibus (GEO) database were analyzed. Running the LIMMA package in R language, the differentially expressed genes (DEGs) were found. Enrichment analysis of the DEGs was conducted in the Database for Annotation, Visualization and Integrated Discovery (DAVID) database to explore gene ontology (GO) annotation and their Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Their protein-protein interactions (PPIs) were provided by STRING database and was visualized in Cytoscape. Hub genes were carried out by CytoHubba.ResultsThree hundred and thirty-eight DEGs were exported, including 27 upregulated and 311 downregulated genes. The functions and KEGG pathways of the DEGs were assessed and the PPI network was constructed based on the string interactions of the DEGs. The network was visualized in Cytoscape; the entire PPI network consisted of 192 nodes and 469 edges. Zap70, Cdc42, Stat1, Stat4, Ccl5 and Cxcr3 were among the hub genes.ConclusionsThese key genes, corresponding proteins and their functions may provide valuable background for both basic and clinical research and could be the direction of future studies in immune tolerance, especially those examining immunocyte-induced tolerance.