ORCID Profile
0000-0003-0655-1041
Current Organisations
University of Information Technology and Communications
,
University of California, San Diego
,
University College Dublin
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Publisher: Springer Science and Business Media LLC
Date: 27-06-2019
Publisher: American Diabetes Association
Date: 17-07-2012
DOI: 10.2337/DB11-0751
Abstract: We formed the GEnetics of Nephropathy–an International Effort (GENIE) consortium to examine previously reported genetic associations with diabetic nephropathy (DN) in type 1 diabetes. GENIE consists of 6,366 similarly ascertained participants of European ancestry with type 1 diabetes, with and without DN, from the All Ireland-Warren 3-Genetics of Kidneys in Diabetes U.K. and Republic of Ireland (U.K.-R.O.I.) collection and the Finnish Diabetic Nephropathy Study (FinnDiane), combined with reanalyzed data from the Genetics of Kidneys in Diabetes U.S. Study (U.S. GoKinD). We found little evidence for the association of the EPO promoter polymorphism, rs161740, with the combined phenotype of proliferative retinopathy and end-stage renal disease in U.K.-R.O.I. (odds ratio [OR] 1.14, P = 0.19) or FinnDiane (OR 1.06, P = 0.60). However, a fixed-effects meta-analysis that included the previously reported cohorts retained a genome-wide significant association with that phenotype (OR 1.31, P = 2 × 10−9). An expanded investigation of the ELMO1 locus and genetic regions reported to be associated with DN in the U.S. GoKinD yielded only nominal statistical significance for these loci. Finally, top candidates identified in a recent meta-analysis failed to reach genome-wide significance. In conclusion, we were unable to replicate most of the previously reported genetic associations for DN, and significance for the EPO promoter association was attenuated.
Publisher: American Chemical Society (ACS)
Date: 17-01-2020
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 04-2013
Publisher: Frontiers Media SA
Date: 23-01-2017
Publisher: Elsevier BV
Date: 12-2017
DOI: 10.1016/J.MAM.2017.05.001
Abstract: Inflammation and its timely resolution play a critical role in effective host defence and wound healing. Unresolved inflammatory responses underlie the pathology of many prevalent diseases resulting in tissue fibrosis and eventual organ failure as typified by kidney, lung and liver fibrosis. The role of autocrine and paracrine mediators including cytokines, prostaglandins and leukotrienes in initiating and sustaining inflammation is well established. More recently a physiological role for specialized pro-resolving lipid mediators [SPMs] in modulating inflammatory responses and promoting the resolution of inflammation has been appreciated. As will be discussed in this review, SPMs not only attenuate the development of fibrosis through promoting the resolution of inflammation but may also directly suppress fibrotic responses. These findings suggest novel therapeutic paradigms to treat intractable life-limiting diseases such as renal fibrosis.
Publisher: Wiley
Date: 04-2021
DOI: 10.1002/JEV2.12084
Abstract: Extracellular vesicles (EVs) are emerging as key players in different stages of atherosclerosis. Here we provide evidence that EVs released by mixed aggregates of monocytes and platelets in response to TNF‐α display pro‐inflammatory actions on endothelial cells and atherosclerotic plaques. Tempering platelet activation with Iloprost, Aspirin or a P2Y 12 inhibitor impacted quantity and phenotype of EV produced. Proteomics of EVs from cells activated with TNF‐α alone or in the presence of Iloprost revealed a distinct composition, with interesting hits like annexin‐A1 and gelsolin. When added to human atherosclerotic plaque explants, EVs from TNF‐α stimulated monocytes augmented release of cytokines. In contrast, EVs generated by TNF‐α together with Iloprost produced minimal plaque activation. Notably, patients with coronary artery disease that required percutaneous coronary intervention had elevated plasma numbers of monocyte, platelet as well as double positive EV subsets. In conclusion, EVs released following monocyte latelet activation may play a potential role in the development and progression of atherosclerosis. Whereas attenuating platelet activation modifies EV composition released from monocyte latelet aggregates, curbing their pro‐inflammatory actions may offer therapeutic avenues for the treatment of atherosclerosis.
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 19-09-2019
Abstract: Although studies show that diabetic kidney disease has a heritable component, searches for the genetic determinants of this complication of diabetes have had limited success. In this study, a new international genomics consortium, the JDRF funded Diabetic Nephropathy Collaborative Research Initiative, assembled nearly 20,000 s les from participants with type 1 diabetes, with and without kidney disease. The authors found 16 new diabetic kidney disease–associated loci at genome-wide significance. The strongest signal centers on a protective missense coding variant at COL4A3 , a gene that encodes a component of the glomerular basement membrane that, when mutated, causes the progressive inherited nephropathy Alport syndrome. These GWAS-identified risk loci may provide insights into the pathogenesis of diabetic kidney disease and help identify potential biologic targets for prevention and treatment. Although diabetic kidney disease demonstrates both familial clustering and single nucleotide polymorphism heritability, the specific genetic factors influencing risk remain largely unknown. To identify genetic variants predisposing to diabetic kidney disease, we performed genome-wide association study (GWAS) analyses. Through collaboration with the Diabetes Nephropathy Collaborative Research Initiative, we assembled a large collection of type 1 diabetes cohorts with harmonized diabetic kidney disease phenotypes. We used a spectrum of ten diabetic kidney disease definitions based on albuminuria and renal function. Our GWAS meta-analysis included association results for up to 19,406 in iduals of European descent with type 1 diabetes. We identified 16 genome-wide significant risk loci. The variant with the strongest association (rs55703767) is a common missense mutation in the collagen type IV alpha 3 chain ( COL4A3) gene, which encodes a major structural component of the glomerular basement membrane (GBM). Mutations in COL4A3 are implicated in heritable nephropathies, including the progressive inherited nephropathy Alport syndrome. The rs55703767 minor allele (Asp326Tyr) is protective against several definitions of diabetic kidney disease, including albuminuria and ESKD, and demonstrated a significant association with GBM width protective allele carriers had thinner GBM before any signs of kidney disease, and its effect was dependent on glycemia. Three other loci are in or near genes with known or suggestive involvement in this condition ( BMP7) or renal biology ( COLEC11 and DDR1 ). The 16 diabetic kidney disease–associated loci may provide novel insights into the pathogenesis of this condition and help identify potential biologic targets for prevention and treatment.
Publisher: American Diabetes Association
Date: 27-04-2018
DOI: 10.2337/DB17-0914
Abstract: Identification of sequence variants robustly associated with predisposition to diabetic kidney disease (DKD) has the potential to provide insights into the pathophysiological mechanisms responsible. We conducted a genome-wide association study (GWAS) of DKD in type 2 diabetes (T2D) using eight complementary dichotomous and quantitative DKD phenotypes: the principal dichotomous analysis involved 5,717 T2D subjects, 3,345 with DKD. Promising association signals were evaluated in up to 26,827 subjects with T2D (12,710 with DKD). A combined T1D+T2D GWAS was performed using complementary data available for subjects with T1D, which, with replication s les, involved up to 40,340 subjects with diabetes (18,582 with DKD). Analysis of specific DKD phenotypes identified a novel signal near GABRR1 (rs9942471, P = 4.5 × 10−8) associated with microalbuminuria in European T2D case subjects. However, no replication of this signal was observed in Asian subjects with T2D or in the equivalent T1D analysis. There was only limited support, in this substantially enlarged analysis, for association at previously reported DKD signals, except for those at UMOD and PRKAG2, both associated with estimated glomerular filtration rate. We conclude that, despite challenges in addressing phenotypic heterogeneity, access to increased s le sizes will continue to provide more robust inference regarding risk variant discovery for DKD.
Publisher: Springer Science and Business Media LLC
Date: 10-05-2021
Publisher: Springer Science and Business Media LLC
Date: 31-03-2021
DOI: 10.1186/S40537-021-00444-8
Abstract: In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.
Publisher: MDPI AG
Date: 04-03-2020
DOI: 10.3390/ELECTRONICS9030427
Abstract: Sickle cell anemia, which is also called sickle cell disease (SCD), is a hematological disorder that causes occlusion in blood vessels, leading to hurtful episodes and even death. The key function of red blood cells (erythrocytes) is to supply all the parts of the human body with oxygen. Red blood cells (RBCs) form a crescent or sickle shape when sickle cell anemia affects them. This abnormal shape makes it difficult for sickle cells to move through the bloodstream, hence decreasing the oxygen flow. The precise classification of RBCs is the first step toward accurate diagnosis, which aids in evaluating the danger level of sickle cell anemia. The manual classification methods of erythrocytes require immense time, and it is possible that errors may be made throughout the classification stage. Traditional computer-aided techniques, which have been employed for erythrocyte classification, are based on handcrafted features techniques, and their performance relies on the selected features. They also are very sensitive to different sizes, colors, and complex shapes. However, microscopy images of erythrocytes are very complex in shape with different sizes. To this end, this research proposes lightweight deep learning models that classify the erythrocytes into three classes: circular (normal), elongated (sickle cells), and other blood content. These models are different in the number of layers and learnable filters. The available datasets of red blood cells with sickle cell disease are very small for training deep learning models. Therefore, addressing the lack of training data is the main aim of this paper. To tackle this issue and optimize the performance, the transfer learning technique is utilized. Transfer learning does not significantly affect performance on medical image tasks when the source domain is completely different from the target domain. In some cases, it can degrade the performance. Hence, we have applied the same domain transfer learning, unlike other methods that used the ImageNet dataset for transfer learning. To minimize the overfitting effect, we have utilized several data augmentation techniques. Our model obtained state-of-the-art performance and outperformed the latest methods by achieving an accuracy of 99.54% with our model and 99.98% with our model plus a multiclass SVM classifier on the erythrocytesIDB dataset and 98.87% on the collected dataset.
Publisher: Elsevier BV
Date: 2015
DOI: 10.1016/J.YEXCR.2014.07.034
Abstract: Epithelial injury and tubulointerstitial fibrosis (TIF) within a hypoxic microenvironment are associated with progressive loss of renal function in chronic kidney disease [CKD]. Transforming growth factor beta-1 (TGF-β1) is an important mediator of renal fibrosis. Growing evidence suggests that Vitamin D [1,25-(OH)2D] and its analogues may have a renoprotective effect in CKD. Here we examined the protective effect of the vitamin D analogue paricalcitol [PC 19-nor-1α,3β,25-trihydroxy-9,10-secoergosta-5(Z),7(E) 22(E)-triene] on the responses of human renal epithelial cells to TGF-β1. PC attenuated TGF-β1-induced Smad 2 phosphorylation and upregulation of the Notch ligand Jagged-1, α-smooth muscle actin and thrombospondin-1 and prevented the TGF-β1-mediated loss of E-Cadherin. To mimic the hypoxic milieu of CKD we cultured renal epithelial cells in hypoxia [1% O2] and observed similar attenuation by PC of TGF-β1-induced fibrotic responses. Furthermore, in cells cultured in normoxia [21% O2], PC induced an accumulation of hypoxia-inducible transcription factors (HIF) 1α and HIF-2α in a time and concentration [1 µM-2 µM] dependent manner. Here, PC-induced HIF stabilisation was dependent on activation of the PI-3Kinase pathway. This is the first study to demonstrate regulation of the HIF pathway by PC which may have importance in the mechanism underlying renoprotection by PC.
Publisher: Springer Science and Business Media LLC
Date: 28-06-2022
DOI: 10.1007/S00125-022-05735-0
Abstract: Diabetic kidney disease (DKD) is the leading cause of kidney failure and has a substantial genetic component. Our aim was to identify novel genetic factors and genes contributing to DKD by performing meta-analysis of previous genome-wide association studies (GWAS) on DKD and by integrating the results with renal transcriptomics datasets. We performed GWAS meta-analyses using ten phenotypic definitions of DKD, including nearly 27,000 in iduals with diabetes. Meta-analysis results were integrated with estimated quantitative trait locus data from human glomerular ( N =119) and tubular ( N =121) s les to perform transcriptome-wide association study. We also performed gene aggregate tests to jointly test all available common genetic markers within a gene, and combined the results with various kidney omics datasets. The meta-analysis identified a novel intronic variant (rs72831309) in the TENM2 gene associated with a lower risk of the combined chronic kidney disease (eGFR ml/min per 1.73 m 2 ) and DKD (microalbuminuria or worse) phenotype ( p =9.8×10 −9 although not withstanding correction for multiple testing, p .3×10 −9 ). Gene-level analysis identified ten genes associated with DKD ( COL20A1 , DCLK1 , EIF4E , PTPRN–RESP18 , GPR158 , INIP–SNX30 , LSM14A and MFF p .7×10 −6 ). Integration of GWAS with human glomerular and tubular expression data demonstrated higher tubular AKIRIN2 gene expression in in iduals with vs without DKD ( p =1.1×10 −6 ). The lead SNPs within six loci significantly altered DNA methylation of a nearby CpG site in kidneys ( p .5×10 −11 ). Expression of lead genes in kidney tubules or glomeruli correlated with relevant pathological phenotypes (e.g. TENM2 expression correlated positively with eGFR [ p =1.6×10 −8 ] and negatively with tubulointerstitial fibrosis [ p =2.0×10 −9 ], tubular DCLK1 expression correlated positively with fibrosis [ p =7.4×10 −16 ], and SNX30 expression correlated positively with eGFR [ p =5.8×10 −14 ] and negatively with fibrosis [ p .0×10 −16 ]). Altogether, the results point to novel genes contributing to the pathogenesis of DKD. The GWAS meta-analysis results can be accessed via the type 1 and type 2 diabetes (T1D and T2D, respectively) and Common Metabolic Diseases (CMD) Knowledge Portals, and downloaded on their respective download pages ( ownloads.html ownloads.html ownloads.html ).
Publisher: American Diabetes Association
Date: 13-09-2018
DOI: 10.2337/DB17-1317
Abstract: Increasing evidence points to the fact that defects in the resolution of inflammatory pathways predisposes in iduals to the development of chronic inflammatory diseases, including diabetic complications such as accelerated atherosclerosis. The resolution of inflammation is dynamically regulated by the production of endogenous modulators of inflammation, including lipoxin A4 (LXA
Publisher: Elsevier BV
Date: 07-2016
DOI: 10.1038/MT.2016.90
Publisher: PeerJ
Date: 28-09-2021
DOI: 10.7717/PEERJ-CS.715
Abstract: Transfer learning (TL) has been widely utilized to address the lack of training data for deep learning models. Specifically, one of the most popular uses of TL has been for the pre-trained models of the ImageNet dataset. Nevertheless, although these pre-trained models have shown an effective performance in several domains of application, those models may not offer significant benefits in all instances when dealing with medical imaging scenarios. Such models were designed to classify a thousand classes of natural images. There are fundamental differences between these models and those dealing with medical imaging tasks regarding learned features. Most medical imaging applications range from two to ten different classes, where we suspect that it would not be necessary to employ deeper learning models. This paper investigates such a hypothesis and develops an experimental study to examine the corresponding conclusions about this issue. The lightweight convolutional neural network (CNN) model and the pre-trained models have been evaluated using three different medical imaging datasets. We have trained the lightweight CNN model and the pre-trained models with two scenarios which are with a small number of images once and a large number of images once again. Surprisingly, it has been found that the lightweight model trained from scratch achieved a more competitive performance when compared to the pre-trained model. More importantly, the lightweight CNN model can be successfully trained and tested using basic computational tools and provide high-quality results, specifically when using medical imaging datasets.
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 28-02-2018
Abstract: Background The failure of spontaneous resolution underlies chronic inflammatory conditions, including microvascular complications of diabetes such as diabetic kidney disease. The identification of endogenously generated molecules that promote the physiologic resolution of inflammation suggests that these bioactions may have therapeutic potential in the context of chronic inflammation. Lipoxins (LXs) are lipid mediators that promote the resolution of inflammation. Methods We investigated the potential of LXA 4 and a synthetic LX analog (Benzo-LXA 4 ) as therapeutics in a murine model of diabetic kidney disease, ApoE −/− mice treated with streptozotocin. Results Intraperitoneal injection of LXs attenuated the development of diabetes-induced albuminuria, mesangial expansion, and collagen deposition. Notably, LXs administered 10 weeks after disease onset also attenuated established kidney disease, with evidence of preserved kidney function. Kidney transcriptome profiling defined a diabetic signature (725 genes false discovery rate P ≤0.05). Comparison of this murine gene signature with that of human diabetic kidney disease identified shared renal proinflammatory rofibrotic signals (TNF- α , IL-1 β , NF- κ B). In diabetic mice, we identified 20 and 51 transcripts regulated by LXA 4 and Benzo-LXA 4 , respectively, and pathway analysis identified established (TGF- β 1, PDGF, TNF- α , NF- κ B) and novel (early growth response–1 [EGR-1]) networks activated in diabetes and regulated by LXs. In cultured human renal epithelial cells, treatment with LXs attenuated TNF- α –driven Egr-1 activation, and Egr-1 depletion prevented cellular responses to TGF- β 1 and TNF- α . Conclusions These data demonstrate that LXs can reverse established diabetic complications and support a therapeutic paradigm to promote the resolution of inflammation.
Publisher: Elsevier BV
Date: 03-2021
Publisher: Springer Science and Business Media LLC
Date: 05-2021
DOI: 10.1186/S13148-021-01081-X
Abstract: A subset of in iduals with type 1 diabetes mellitus (T1DM) are predisposed to developing diabetic kidney disease (DKD), the most common cause globally of end-stage kidney disease (ESKD). Emerging evidence suggests epigenetic changes in DNA methylation may have a causal role in both T1DM and DKD. The aim of this exploratory investigation was to assess differences in blood-derived DNA methylation patterns between in iduals with T1DM-ESKD and in iduals with long-duration T1DM but no evidence of kidney disease upon repeated testing to identify potential blood-based biomarkers. Blood-derived DNA from in iduals (107 cases, 253 controls and 14 experimental controls) were bisulphite treated before DNA methylation patterns from both groups were generated and analysed using Illumina’s Infinium MethylationEPIC BeadChip arrays ( n = 862,927 sites). Differentially methylated CpG sites (dmCpGs) were identified (false discovery rate adjusted p ≤ × 10 –8 and fold change ± 2) by comparing methylation levels between ESKD cases and T1DM controls at single site resolution. Gene annotation and functionality was investigated to enrich and rank methylated regions associated with ESKD in T1DM. Top-ranked genes within which several dmCpGs were located and supported by functional data with methylation look-ups in other cohorts include: AFF3, ARID5B, CUX1, ELMO1 , FKBP5 , HDAC4, ITGAL, LY9 , PIM1, RUNX3, SEPTIN9 and UPF3A . Top-ranked enrichment pathways included pathways in cancer, TGF-β signalling and Th17 cell differentiation. Epigenetic alterations provide a dynamic link between an in idual’s genetic background and their environmental exposures. This robust evaluation of DNA methylation in carefully phenotyped in iduals has identified biomarkers associated with ESKD, revealing several genes and implicated key pathways associated with ESKD in in iduals with T1DM.
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 10-2013
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 2020
Publisher: MDPI AG
Date: 02-03-2022
DOI: 10.3390/APP12052605
Abstract: In the last decade, there has been a surge of interest in addressing complex Computer Vision (CV) problems in the field of face recognition (FR). In particular, one of the most difficult ones is based on the accurate determination of the ethnicity of mankind. In this regard, a new classification method using Machine Learning (ML) tools is proposed in this paper. Specifically, a new Deep Learning (DL) approach based on a Deep Convolutional Neural Network (DCNN) model is developed, which outperforms a reliable determination of the ethnicity of people based on their facial features. However, it is necessary to make use of specialized high-performance computing (HPC) hardware to build a workable DCNN-based FR system due to the low computation power given by the current central processing units (CPUs). Recently, the latter approach has increased the efficiency of the network in terms of power usage and execution time. Then, the usage of field-programmable gate arrays (FPGAs) was considered in this work. The performance of the new DCNN-based FR method using FPGA was compared against that using graphics processing units (GPUs). The experimental results considered an image dataset composed of 3141 photographs of citizens from three distinct countries. To our knowledge, this is the first image collection gathered specifically to address the ethnicity identification problem. Additionally, the ethnicity dataset was made publicly available as a novel contribution to this work. Finally, the experimental results proved the high performance provided by the proposed DCNN model using FPGAs, achieving an accuracy level of 96.9 percent and an F1 score of 94.6 percent while using a reasonable amount of energy and hardware resources.
Publisher: Springer International Publishing
Date: 12-04-2019
Publisher: Springer International Publishing
Date: 12-04-2019
Publisher: Frontiers Media SA
Date: 19-12-2018
Publisher: Elsevier BV
Date: 04-2012
Publisher: Institute of Advanced Engineering and Science
Date: 05-2020
Publisher: ACM
Date: 15-04-2019
Publisher: Elsevier BV
Date: 2019
DOI: 10.1016/J.EJMECH.2018.10.049
Abstract: Lipoxins (LXs) are endogenously generated eicosanoids with potent bio-actions consistent with attenuation of inflammation. The costly synthesis and metabolic instability of LXs may limit their therapeutic potential. Here we report the synthesis and characterization of novel imidazole-/oxazole-containing synthetic-LX-mimetics (sLXms). The key steps of asymmetric synthesis of putative sLXms include a Suzuki reaction and an asymmetric ketone reduction. The effect of the novel compounds on inflammatory responses was assessed using a human monocyte cell line stably expressing a Nuclear Factor Kappa B (NFkB) reporter gene, by investigating downstream cytokine secretion. The potential interaction of the imidazoles/oxazoles with the molecular target of LXs, i.e. G-protein coupled receptor (GPCR) Formyl Peptide Receptor 2 (ALX/FPR2) was investigated using a cell system where ALX/FPR2 is coupled to the Gα
Publisher: Cold Spring Harbor Laboratory
Date: 31-07-2020
DOI: 10.1101/2020.07.30.228734
Abstract: A subset of in iduals with type 1 diabetes mellitus (T1DM) are predisposed to developing diabetic kidney disease (DKD), which is the most common cause globally of end-stage kidney disease (ESKD). Emerging evidence suggests epigenetic changes in DNA methylation may have a causal role in both T1DM and DKD. The aim of this investigation was to assess differences in blood-derived DNA methylation patterns between in iduals with T1DM-ESKD and in iduals with long-duration T1DM but no evidence of kidney disease upon repeated testing. Blood-derived DNA from in iduals (107 cases, 253 controls and 14 experimental controls) were bisulphite treated before DNA methylation patterns from both groups were generated and analysed using Illumina's Infinium MethylationEPIC BeadChip arrays (n=862,927 sites). Differentially methylated CpG sites (dmCpGs) were identified (false discovery rate adjusted p≤x10-8 and fold change ±2) by comparing methylation levels between ESKD cases and T1DM controls at single site resolution. Gene annotation and functionality was investigated to enrich and rank methylated regions associated with ESKD in T1DM. Top-ranked genes within which several dmCpGs were located and supported by in silico functional data, and replication where possible, include AFF3, ARID5B, CUX1, ELMO1, FKBP5, HDAC4, ITGAL, LY9, PIM1, RUNX3, SEPTIN9, and UPF3A. Top-ranked enrichment pathways included pathways in cancer, TGF-β signalling and Th17 cell differentiation. Epigenetic alterations provide a dynamic link between an in idual's genetic background and their environmental exposures. This robust evaluation of DNA methylation in carefully phenotyped in iduals, has identified biomarkers associated with ESKD, revealing several genes and implicated key pathways associated with ESKD in in iduals with T1DM.
Publisher: Wiley
Date: 09-2014
DOI: 10.1113/EXPPHYSIOL.2014.078774
Abstract: Diabetic kidney disease occurs in >30% of patients with type 2 diabetes mellitus and is characterized at source by a maladaptive response in the renal parenchyma to exposure to a glucotoxic-lipotoxic diabetic milieu that courses coincident with hypertension. The consequence of these maladaptive responses is progressive renal injury, which is exacerbated by the development of a chronic inflammatory infiltrate associated with the development of tubulointerstitial fibrosis. The evolution of tubulointerstitial fibrosis is correlated with the loss of functional renal mass and descent towards renal failure. Transforming growth factor-β1 (TGF-β1) is a recognized mediator of the profibrotic response of mesangial cells and renal tubular epithelial cells to hyperglycaemia. While euglycaemia remains the goal in the treatment of type 2 diabetes mellitus, the prevention, arrest and reversal of microvascular complications, such as diabetic kidney disease, may be assisted by pharmacological modulation of the effectors of glucotoxicity, such as TGF-β1. This review focuses on describing how, through reductionist in vitro experimentation focusing on TGF-β1-related responses to hyperglycaemia, we have identified induced in high glucose-1 (IHG-1), induced in high glucose-2 (IHG-2/Grem1) and the lipoxin-inducible microRNA let-7c as potential targets for harnessing new therapeutic approaches to limit the bioactivity of TGF-β1 in diabetic kidney disease.
Publisher: Springer International Publishing
Date: 15-08-2020
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer International Publishing
Date: 12-04-2019
Publisher: Springer International Publishing
Date: 12-04-2019
Publisher: IEEE
Date: 10-2019
Publisher: American Chemical Society (ACS)
Date: 17-06-2021
Publisher: Springer International Publishing
Date: 15-08-2020
Publisher: MDPI AG
Date: 30-03-2021
Abstract: Deep learning requires a large amount of data to perform well. However, the field of medical image analysis suffers from a lack of sufficient data for training deep learning models. Moreover, medical images require manual labeling, usually provided by human annotators coming from various backgrounds. More importantly, the annotation process is time-consuming, expensive, and prone to errors. Transfer learning was introduced to reduce the need for the annotation process by transferring the deep learning models with knowledge from a previous task and then by fine-tuning them on a relatively small dataset of the current task. Most of the methods of medical image classification employ transfer learning from pretrained models, e.g., ImageNet, which has been proven to be ineffective. This is due to the mismatch in learned features between the natural image, e.g., ImageNet, and medical images. Additionally, it results in the utilization of deeply elaborated models. In this paper, we propose a novel transfer learning approach to overcome the previous drawbacks by means of training the deep learning model on large unlabeled medical image datasets and by next transferring the knowledge to train the deep learning model on the small amount of labeled medical images. Additionally, we propose a new deep convolutional neural network (DCNN) model that combines recent advancements in the field. We conducted several experiments on two challenging medical imaging scenarios dealing with skin and breast cancer classification tasks. According to the reported results, it has been empirically proven that the proposed approach can significantly improve the performance of both classification scenarios. In terms of skin cancer, the proposed model achieved an F1-score value of 89.09% when trained from scratch and 98.53% with the proposed approach. Secondly, it achieved an accuracy value of 85.29% and 97.51%, respectively, when trained from scratch and using the proposed approach in the case of the breast cancer scenario. Finally, we concluded that our method can possibly be applied to many medical imaging problems in which a substantial amount of unlabeled image data is available and the labeled image data is limited. Moreover, it can be utilized to improve the performance of medical imaging tasks in the same domain. To do so, we used the pretrained skin cancer model to train on feet skin to classify them into two classes—either normal or abnormal (diabetic foot ulcer (DFU)). It achieved an F1-score value of 86.0% when trained from scratch, 96.25% using transfer learning, and 99.25% using double-transfer learning.
Publisher: MDPI AG
Date: 29-06-2020
DOI: 10.3390/APP10134523
Abstract: One of the main challenges of employing deep learning models in the field of medicine is a lack of training data due to difficulty in collecting and labeling data, which needs to be performed by experts. To overcome this drawback, transfer learning (TL) has been utilized to solve several medical imaging tasks using pre-trained state-of-the-art models from the ImageNet dataset. However, there are primary ergences in data features, sizes, and task characteristics between the natural image classification and the targeted medical imaging tasks. Therefore, TL can slightly improve performance if the source domain is completely different from the target domain. In this paper, we explore the benefit of TL from the same and different domains of the target tasks. To do so, we designed a deep convolutional neural network (DCNN) model that integrates three ideas including traditional and parallel convolutional layers and residual connections along with global average pooling. We trained the proposed model against several scenarios. We utilized the same and different domain TL with the diabetic foot ulcer (DFU) classification task and with the animal classification task. We have empirically shown that the source of TL from the same domain can significantly improve the performance considering a reduced number of images in the same domain of the target dataset. The proposed model with the DFU dataset achieved F1-score value of 86.6% when trained from scratch, 89.4% with TL from a different domain of the targeted dataset, and 97.6% with TL from the same domain of the targeted dataset.
Publisher: Elsevier BV
Date: 03-2021
DOI: 10.1016/J.PHRS.2021.105445
Abstract: The resolution of inflammation is a dynamic process, characterized by the biosynthesis of pro-resolving mediators, including the lipid Lipoxin A4 (LXA
Publisher: Cold Spring Harbor Laboratory
Date: 28-07-2020
DOI: 10.1101/MCS.A005462
Abstract: High-throughput DNA testing is becoming established as a standard diagnostic test in the renal clinic. Previously published studies on cohorts of patients with unexplained chronic kidney disease of a suspected genetic aetiology have suggested a diagnostic yield for genomic sequencing of up to 18%. Here we determine the yield of targeted gene panel in a clinically unscreened cohort of patients referred for percutaneous native renal biopsy. Patients who underwent renal biopsy for investigation of chronic kidney disease were sequenced using a genomic sequencing panel covering 227 genes in which variation is known to be associated with monogenic chronic kidney disease (CKD). Candidate disease-causing variants were assessed for pathogenicity using guidelines from the American College for Medical Genetics and Genomics. Fifty CKD patients were recruited and sequenced. A molecular diagnosis was obtained for two patients (4%). A molecular diagnosis is possible using genomic testing in ∼4% of clinically unscreened patients undergoing renal biopsy. Genetic screening may be useful for diagnosis in a subset of CKD patients but is most valuable when applied to patients with suspected heritable forms of kidney disease.
Publisher: MDPI AG
Date: 06-03-2020
DOI: 10.3390/ELECTRONICS9030445
Abstract: Breast cancer is a significant factor in female mortality. An early cancer diagnosis leads to a reduction in the breast cancer death rate. With the help of a computer-aided diagnosis system, the efficiency increased, and the cost was reduced for the cancer diagnosis. Traditional breast cancer classification techniques are based on handcrafted features techniques, and their performance relies upon the chosen features. They also are very sensitive to different sizes and complex shapes. However, histopathological breast cancer images are very complex in shape. Currently, deep learning models have become an alternative solution for diagnosis, and have overcome the drawbacks of classical classification techniques. Although deep learning has performed well in various tasks of computer vision and pattern recognition, it still has some challenges. One of the main challenges is the lack of training data. To address this challenge and optimize the performance, we have utilized a transfer learning technique which is where the deep learning models train on a task, and then fine-tune the models for another task. We have employed transfer learning in two ways: Training our proposed model first on the same domain dataset, then on the target dataset, and training our model on a different domain dataset, then on the target dataset. We have empirically proven that the same domain transfer learning optimized the performance. Our hybrid model of parallel convolutional layers and residual links is utilized to classify hematoxylin–eosin-stained breast biopsy images into four classes: invasive carcinoma, in-situ carcinoma, benign tumor and normal tissue. To reduce the effect of overfitting, we have augmented the images with different image processing techniques. The proposed model achieved state-of-the-art performance, and it outperformed the latest methods by achieving a patch-wise classification accuracy of 90.5%, and an image-wise classification accuracy of 97.4% on the validation set. Moreover, we have achieved an image-wise classification accuracy of 96.1% on the test set of the microscopy ICIAR-2018 dataset.
Publisher: MDPI AG
Date: 05-11-2013
DOI: 10.3390/GENES4040596
Publisher: Elsevier BV
Date: 08-2020
Publisher: Springer International Publishing
Date: 15-08-2020
Publisher: Cold Spring Harbor Laboratory
Date: 06-2020
DOI: 10.1101/2020.06.01.120980
Abstract: Roux-en-Y Gastric Bypass Surgery (RYGB) reduces albuminuria and the long-term incidence of end-stage renal disease in patients with obesity and diabetes. Preclinical modelling in experimental diabetic kidney disease (DKD) demonstrates that improvements in glomerular structure likely underpin these findings. In adult male Zucker Diabetic Fatty (ZDF) rats, we profiled the effect of RYGB on weight and metabolic control as well biochemical, structural and ultrastructural indices of diabetic renal injury. Furthermore, we sequenced the renal cortical transcriptome in these rats and used bioinformatic pathway analyses to characterise the transcriptional alterations governing the renal reparative response to RYGB In parallel with improvements in weight and metabolic control, RYGB reduced albuminuria, glomerulomegaly, podocyte stress, and podocyte foot process effacement. Pathway analysis of RYGB-induced transcriptomic changes in the renal cortex highlighted correction of disease-associated alterations in fibrosis, inflammation and biological oxidation pathways. RYGB reversed disease-associated changes in the expression of TGF-β superfamily genes that strongly correlated with improvements in structural measures of glomerulopathy. Improved glomerular structure in ZDF rats following RYGB is underpinned by pathway level changes, including interruption of the TGF-β driven pro-fibrotic programme. Our data provide an important layer of experimental support for clinical evidence demonstrating that RYGB arrests renal damage in patients with obesity and Type 2 Diabetes. What is already known about this subject? RYGB is an effective treatment for obesity and type 2 diabetes and longitudinal cohort studies have demonstrated it’s albuminuria lowering effect and evidence of longer term reno-protection. Studies in pre-clinical models of diabetic kidney disease have described favourable changes in measures of renal structure and ultrastructure following RYGB. What are the new findings? The present study directly correlates structural and ultrastructural improvements in the ZDF rat kidney following RYGB with corrective shifts in the global renal transcriptome. Chronic renal remodelling responses in experimental DKD that are governed by TGF-β signalling are interrupted and reversed by RYGB. How might these results change the focus of research or clinical practice? These data will support further interrogation of RYGB specific shifts in the renal transcriptome with a view to identifying tractable targets for treatment response biomarkers and bariatric mimetic based diet and pharmacotherapy based interventions.
Publisher: American Diabetes Association
Date: 09-05-2017
DOI: 10.2337/DB16-1405
Abstract: The let-7 miRNA family plays a key role in modulating inflammatory responses. Vascular smooth muscle cell (SMC) proliferation and endothelial cell (EC) dysfunction are critical in the pathogenesis of atherosclerosis, including in the setting of diabetes. Here we report that let-7 levels are decreased in diabetic human carotid plaques and in a model of diabetes-associated atherosclerosis, the diabetic ApoE−/− mouse. In vitro platelet-derived growth factor (PDGF)– and tumor necrosis factor-α (TNF-α)–induced vascular SMC and EC activation was associated with reduced let-7 miRNA expression via Lin28b, a negative regulator of let-7 biogenesis. Ectopic overexpression of let-7 in SMCs inhibited inflammatory responses including proliferation, migration, monocyte adhesion, and nuclear factor-κB activation. The therapeutic potential of restoring let-7 levels using a let-7 mimic was tested: in vitro in SMCs using an endogenous anti-inflammatory lipid (lipoxin A4), ex vivo in murine aortas, and in vivo via tail vein injection in a 24-h murine model. Furthermore, we delivered let-7 mimic to human carotid plaque ex vivo and observed significant changes to the secretome in response to let-7 therapy. Restoration of let-7 expression could provide a new target for an anti-inflammatory approach in diabetic vascular disease.
Publisher: Public Library of Science (PLoS)
Date: 20-09-2012
Publisher: IEEE
Date: 10-2019
Publisher: Institute of Advanced Engineering and Science
Date: 06-2023
DOI: 10.11591/IJEECS.V30.I3.PP1624-1633
Abstract: The development in the field of computer technology, and the increase in the growth rate of database, alongside the extraction of certain data from a huge pool of database involve intricate and complex processes. The processes comprise text mining, pattern recognition, retrieval of information and text processing. Thus, the need for enhancing the performance of string matching algorithms is required, which is considered as one of the challenges to the researchers. Consequently, one of the resolution to address this problem is the parallelization for exact string matching algorithms. In this study, we implemented the parallel exact string matching algorithm termed as E-Atheer with multi-core processing utilizing Pthread (POSIX) for the reduction of time consumption. The Pitch, XML, Protein, and DNA database types are utilized to test the impact of the proposed parallel algorithm. The parallelization algorithm obtained positive results in the parallel execution time, and a more superior expediting capabilities, in comparison to the sequential result. The Pitch database indicated optimal results in parallel execution time, and when utilizing long and short pattern lengths. The DNA database indicated optimal speedup performance when utilizing short and long pattern length, meanwhile the XML and Protein on the other hand indicated the worst results.
Publisher: American Diabetes Association
Date: 25-08-2015
DOI: 10.2337/DB15-0254
Abstract: Obesity has been posited as an independent risk factor for diabetic kidney disease (DKD), but establishing causality from observational data is problematic. We aimed to test whether obesity is causally related to DKD using Mendelian randomization, which exploits the random assortment of genes during meiosis. In 6,049 subjects with type 1 diabetes, we used a weighted genetic risk score (GRS) comprised of 32 validated BMI loci as an instrument to test the relationship of BMI with macroalbuminuria, end-stage renal disease (ESRD), or DKD defined as presence of macroalbuminuria or ESRD. We compared these results with cross-sectional and longitudinal observational associations. Longitudinal analysis demonstrated a U-shaped relationship of BMI with development of macroalbuminuria, ESRD, or DKD over time. Cross-sectional observational analysis showed no association with overall DKD, higher odds of macroalbuminuria (for every 1 kg/m2 higher BMI, odds ratio [OR] 1.05, 95% CI 1.03–1.07, P & 0.001), and lower odds of ESRD (OR 0.95, 95% CI 0.93–0.97, P & 0.001). Mendelian randomization analysis showed a 1 kg/m2 higher BMI conferring an increased risk in macroalbuminuria (OR 1.28, 95% CI 1.11–1.45, P = 0.001), ESRD (OR 1.43, 95% CI 1.20–1.72, P & 0.001), and DKD (OR 1.33, 95% CI 1.17–1.51, P & 0.001). Our results provide genetic evidence for a causal link between obesity and DKD in type 1 diabetes. As obesity prevalence rises, this finding predicts an increase in DKD prevalence unless intervention should occur.
Publisher: Springer Science and Business Media LLC
Date: 11-02-2015
DOI: 10.1038/NATURE14177
Publisher: Springer Science and Business Media LLC
Date: 11-02-2015
DOI: 10.1038/NATURE14132
Publisher: Springer Science and Business Media LLC
Date: 19-07-2021
Location: United Kingdom of Great Britain and Northern Ireland
Location: Iraq
Location: United States of America
No related grants have been discovered for Catherine Godson.