BackgroundSchizophrenia is a neurodevelopmental disorder with genetic and environmental factors contributing to its pathogenesis, although the mechanism is unknown due to the difficulties in accessing diseased tissue during human neurodevelopment. The aim of this study was to find neuronal differentiation genes disrupted in schizophrenia and to evaluate those genes in post-mortem brain tissues from schizophrenia cases and controls.MethodsWe analyzed differentially expressed genes (DEG), copy number variation (CNV) and differential methylation in human induced pluripotent stem cells (hiPSC) derived from fibroblasts from one control and one schizophrenia patient and further differentiated into neuron (NPC). Expression of the DEG were analyzed with microarrays of post-mortem brain tissue (frontal cortex) cohort of 29 schizophrenia cases and 30 controls. A Weighted Gene Co-expression Network Analysis (WGCNA) using the DEG was used to detect clusters of co-expressed genes that werenon-conserved between adult cases and controls brain samples.ResultsWe identified methylation alterations potentially involved with neuronal differentiation in schizophrenia, which displayed an over-representation of genes related to chromatin remodeling complex (adjP = 0.04). We found 228 DEG associated with neuronal differentiation. These genes were involved with metabolic processes, signal transduction, nervous system development, regulation of neurogenesis and neuronal differentiation. Between adult brain samples from cases and controls there were 233 DEG, with only four genes overlapping with the 228 DEG, probably because we compared single cell to tissue bulks and more importantly, the cells were at different stages of development. The comparison of the co-expressed network of the 228 genes in adult brain samples between cases and controls revealed a less conserved module enriched for genes associated with oxidative stress and negative regulation of cell differentiation.ConclusionThis study supports the relevance of using cellular approaches to dissect molecular aspects of neurogenesis with impact in the schizophrenic brain. We showed that, although generated by different approaches, both sets of DEG associated to schizophrenia were involved with neocortical development. The results add to the hypothesis that critical metabolic changes may be occurring during early neurodevelopment influencing faulty development of the brain and potentially contributing to further vulnerability to the illness.Electronic supplementary materialThe online version of this article (doi:10.1186/s12920-015-0098-9) contains supplementary material, which is available to authorized users.
Objective:Cocaine use disorders (CUDs) represent a major public health problem in many countries. To better understand the interaction between the environmental modulations and phenotype, the aim of the present study was to investigate the DNA methylation pattern of CUD patients, who had concomitant cocaine and crack dependence, and healthy controls.Methods:We studied DNA methylation profiles in the peripheral blood of 23 CUD patients and 24 healthy control subjects using the Illumina Infinium HumanMethylation450 BeadChip arrays.Results:Comparison between CUD patients and controls revealed 186 differentially methylated positions (DMPs; adjusted p-value [adjP] < 10-5) related to 152 genes, with a subset of CpGs confirmed by pyrosequencing. DNA methylation patterns discriminated CUD patients and control groups. A gene network approach showed that the EHMT1, EHMT2, MAPK1, MAPK3, MAP2K1, and HDAC5 genes, which are involved in transcription and chromatin regulation cellular signaling pathways, were also associated with cocaine dependence.Conclusion:The investigation of DNA methylation patterns may contribute to a better understanding of the biological mechanisms involved in CUD.
Melanoma is a highly aggressive cancer, accounting for up to 75% of skin cancer deaths. A small proportion of melanoma cases can be ascribed to the presence of highly penetrant germline mutations, and approximately 40% of hereditary melanoma cases are caused by CDKN2A mutations. The current study sought to investigate whether the presence of germline CDKN2A mutations or the occurrence of cutaneous melanoma would result in constitutive genome-wide DNA methylation changes. The leukocyte methylomes of two groups of melanoma patients (those with germline CDKN2A mutations and those without CDKN2A mutations) were analyzed together with the profile of a control group of individuals. A pattern of DNA hypomethylation was detected in the CDKN2A-negative patients relative to both CDKN2A-mutated patients and controls. Additionally, we delineated a panel of 90 CpG sites that were differentially methylated in CDKN2A-mutated patients relative to controls. Although we identified a possible constitutive epigenetic signature in CDKN2A-mutated patients, the occurrence of reported SNPs at the detected CpG sites complicated the data interpretation. Thus, further studies are required to elucidate the impact of these findings on melanoma predisposition and their possible effect on the penetrance of CDKN2A mutations.
Introduction: Thoracic aortic aneurysm diameter determination is paramount for the decision-making process regarding surgical management. Studies focusing in asymptomatic patients have determined prevalence of 0.16 to 0.36% of TAAs in imaging studies. Several groups have proposed automated aortic measurement tools as propaedeutic and therapeutic instruments. In this study we developed and tested an automatic 3-dimensional (3D) segmentation method for the thoracic aorta, applicable on computed tomography angiography (CTA) acquired using low-dose and standard dose protocol, with and without contrast enhancement; and to accurately calculate the 3D diameter information of the arterial segments. Methods: a retrospective cohort of all CT scans acquired in our service between 2016 and 2021 led to the selection of 587 CT exams including low and standard-dose radiation, with and without contrast enhancement. 527 exams were used for neural network training of an algorithm capable of aptly measuring the aortic diameters, using manual measurements performed by three medical specialists as a baseline. Sixty exams were used for validation. The algorithm was developed both for use with the support of PyRadiomics and for a self-made approach. Results: Aortic measurement using the algorithm supported by PyRadiomics resulted in mean absolute error values under 2mm. For the self-made approach, mean absolute error values were under 5mm. Conclusion: This study presents an effective automated solution for thoracic aortic measurement with good results in sets of standard or low-radiation exams, as well as those acquired with or without contrast enhancement; presenting a possibility for an auxiliary tool for automation of the process of measuring the diameter of the thoracic aorta.
This study aimed to develop an automated 3-dimensional (3D) segmentation method for measuring the diameter of the thoracic aorta using different computed tomography (CT) protocols. A total of 587 CT scans were retrospectively analysed, and a manual slice-by-slice segmentation of the thoracic aorta was performed by three specialists. The segmented images were used to train convolutional neural network (CNN) models for automated segmentation. The models achieved high accuracy, with an average Dice Score Coefficient (DSC) of 0.8708. Four different methods for thoracic aorta diameter measurement were compared: manual measuring, semi-automatic measuring, automatic measuring using PyRadiomics, and automatic measuring using a made-to-measure algorithm. The results showed that the automatic measuring methods had similar accuracy to the manual and semi-automatic methods. The mean thoracic aorta diameter varied between 3.3 cm and 4.95 cm. These findings demonstrate the feasibility and accuracy of using artificial intelligence algorithms for automated thoracic aorta diameter measurement, which can aid in the assessment and management of aortic diseases.
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