ArticleFrontiers in endocrinology2021
qPCR Analysis Reveals Association of Differential Expression of
Article in Frontiers in endocrinology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 23 citations in OpenAlex.
- Diabetes affects the composition of the respiratory tract microbiome and transcriptome in patients with viral pneumonia.Microbiology spectrum · 2026Article
- Machine learning-based stratification of prediabetes and type 2 diabetes progression.Diabetology & metabolic syndrome · 2025Article
- Hawthorn leaf and its extract alleviate high-fat diet-induced obesity and modulate gut microbiome in mice.Current research in food science · 2025Article
- Exploring the ceRNA network involving AGAP2-AS1 as a novel biomarker for preeclampsia.Scientific reports · 2024Article
- Article
- Study on Potential Differentially Expressed Genes in Idiopathic Pulmonary Fibrosis by Bioinformatics and Next-Generation Sequencing Data Analysis.Biomedicines · 2023Article
- Association of Protein Tyrosine Phosphatase Receptor Type D and Serine Racemase Genetic Variants with Type 2 Diabetes in Malaysian Indians.Indian journal of endocrinology and metabolismArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Type 2 diabetes mellitus (T2DM) is a heterogeneous, metabolic, and chronic condition affecting vast numbers of the world's population. The related variables and T2DM associations have not been fully understood due to their diverse nature. However, functional genomics can facilitate understanding of the disease. This information will be useful in drug design, advanced diagnostic, and prognostic markers. Aim: To understand the genetic causes of T2DM, this study was designed to identify the differentially expressed genes (DEGs) of the disease. Methods: We investigated 20 publicly available disease-specific cDNA datasets from Gene Expression Omnibus (GEO) containing several attributes including gene symbols and clone identifiers, GenBank accession numbers, and phenotypic feature coordinates. We analyzed an integrated system-level framework involving Gene Ontology (GO), protein motifs and co-expression analysis, pathway enrichment, and transcriptional factors to reveal the biological information of genes. A co-expression network was studied to highlight the genes that showed a coordinated expression pattern across a group of samples. The DEGs were validated by quantitative PCR (qPCR) to analyze the expression levels of case and control samples (50 each) using glyceraldehyde 3-phosphate dehydrogenase ( Results: From the list of 50 DEGs, we ranked three T2DM-related genes ( Conclusion: This study would help to modulate the therapeutic strategies for T2DM and could speed up drug discovery outcomes.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.