Membranous glomerulonephritis (MGN) is one of the most frequent causes of nephrotic syndrome in adults. It is characterized by the thickening of the glomerular basement membrane in the renal tissue. The current diagnosis of MGN is based on renal biopsy and the detection of antibodies to the few podocyte antigens. Due to the limitations of the current diagnostic methods, including invasiveness and the lack of sensitivity of the current biomarkers, there is a requirement to identify more applicable biomarkers. The present study aimed to identify diagnostic metabolites that are involved in the development of the disease using topological features in the component‑reaction‑enzyme‑gene (CREG) network for MGN. Significant differential metabolites in MGN compared with healthy controls were identified using proton nuclear magnetic resonance and gas chromatography‑mass spectrometry techniques, and multivariate analysis. The CREG network for MGN was constructed, and metabolites with a high centrality and a striking fold‑change in patients, compared with healthy controls, were introduced as putative diagnostic biomarkers. In addition, a protein‑protein interaction (PPI) network, which was based on proteins associated with MGN, was built and analyzed using PPI analysis methods, including molecular complex detection and ClueGene Ontology. A total of 26 metabolites were identified as hub nodes in the CREG network, 13 of which had salient centrality and fold‑changes: Dopamine, carnosine, fumarate, nicotinamide D‑ribonucleotide, adenosine monophosphate, pyridoxal, deoxyguanosine triphosphate, L‑citrulline, nicotinamide, phenylalanine, deoxyuridine, tryptamine and succinate. A total of 13 subnetworks were identified using PPI analysis. In total, two of the clusters contained seed proteins (phenylalanine‑4‑hydroxlylase and cystathionine γ‑lyase) that were associated with MGN based on the CREG network. The following biological processes associated with MGN were identified using gene ontology analysis: 'Pyrimidine‑containing compound biosynthetic process', 'purine ribonucleoside metabolic process', 'nucleoside catabolic process', 'ribonucleoside metabolic process' and 'aromatic amino acid family metabolic process'. The results of the present study may be helpful in the diagnostic and therapeutic procedures of MGN. However, validation is required in the future.
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http://dx.doi.org/10.3892/mmr.2018.9477 | DOI Listing |
Sci Rep
November 2024
School of Medicine, Anhui University of Science and Technology, Huainan, 232001, Anhui, China.
Cardiac differentiation of human pluripotent stem cells is not only a new strategy of regenerative therapy for cardiovascular disease treatment but also provides unique opportunities for the study of in vitro disease models and human heart development. To elucidate the dynamic gene regulatory networks and pivotal regulators involved in the cardiomyocyte differentiation process, we conducted an analysis of single-cell RNA sequencing data obtained from the reprogramming of two human induced pluripotent stem cell (iPSC) lines into cardiomyocytes. The data were collected from 32,365 cells at 4 stages of this process.
View Article and Find Full Text PDFFront Immunol
February 2024
Department of Breast Surgery, The First Affiliated Hospital, Jinan University, Guangzhou, China.
Objectives: Perform a bibliometric analysis on the role of LAG-3 in the domain of cancer, elucidate the prevailing areas of research, and visually depict the evolutionary trajectory and prospective directions of LAG-3 research over the past twenty-three decades.
Materials And Methods: Between 2000 and 2023, a comprehensive review of scholarly articles pertaining to LAG-3 research in the context of cancer was carried out using the Web of Science Core Collection (WoSCC) database. Bibliometric analysis can be conducted by taking advantage of VOSviewer (version 1.
Med Image Anal
October 2023
Electronic & Information Engineering School, Harbin Institute of Technology (Shenzhen), Shenzhen, China; Peng Cheng Laboratory, Shenzhen, China; Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China; International Research Institute for Artificial Intelligence, Harbin Institute of Technology (Shenzhen), Shenzhen, China. Electronic address:
One of the core challenges of deep learning in medical image analysis is data insufficiency, especially for 3D brain imaging, which may lead to model over-fitting and poor generalization. Regularization strategies such as knowledge distillation are powerful tools to mitigate the issue by penalizing predictive distributions and introducing additional knowledge to reinforce the training process. In this paper, we revisit knowledge distillation as a regularization paradigm by penalizing attentive output distributions and intermediate representations.
View Article and Find Full Text PDFPLoS Genet
March 2021
Department of Ecology and Evolutionary Biology, Brown University, Providence, Rhode Island, United States of America.
The winged insects of the order Diptera are colloquially named for their most recognizable phenotype: flight. These insects rely on flight for a number of important life history traits, such as dispersal, foraging, and courtship. Despite the importance of flight, relatively little is known about the genetic architecture of flight performance.
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January 2019
CREG, CONICET-Universidad Nacional de La Plata, La Plata, CP 1900, Argentina.
Infectious diseases are of great relevance for global health, but needed drugs and vaccines have not been developed yet or are not effective in many cases. In fact, traditional scientific approaches with intense focus on individual genes or proteins have not been successful in providing new treatments. Hence, innovations in technology and computational methods provide new tools to further understand complex biological systems such as pathogen biology.
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