Evidence map›Paper›PMID 35702773›Full record

ArticleCurrent diabetes reviews2024

An

Bhuvnesh Rai, Pramod Kumar Maurya, Medha Srivastava, Prabhakar Mishra, Mehar Hasan Asif, Swasti Tiwari

Abstract read
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In one paragraph

Article in Current diabetes reviews, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.3field-weighted citation impact, top 42% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Bhuvnesh RaiDepartment of Molecular Medicine and Biotechnology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India.ORCID 0000-0002-9090-186X
Pramod Kumar MauryaDepartment of Molecular Medicine and Biotechnology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India.
Medha SrivastavaDepartment of Molecular Medicine and Biotechnology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India.
Prabhakar MishraDepartment of Biostatistics and Health, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India.
Mehar Hasan AsifGenetics and Biotechnology Division, CSIR-National Botanical Research Institute, Lucknow, India.
Swasti TiwariDepartment of Molecular Medicine and Biotechnology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India.
Sanjay Gandhi Post Graduate Institute of Medical Sciences · INNational Botanical Research Institute · IN

Funding

Indian Council of Medical Research, ICMR CARE- No Coord/7(1)/CARE-KD/18-NCD-II, 5/4/7-12/2013-NCD-II
6 · The paper itself

Abstract

backgroundRenal disease in T2DM could arise independently of hyperglycemia, aka non diabetic kidney disease. Its prevalence ranges from 33% to 72.5% among T2DM patients. Specific molecular signatures that distinguish Diabetic Nephropathy from NDKD (FSGS) in T2DM might provide new targets for CKD management.

methodsFive original GEO microarray DN and FSGS datasets were evaluated (GSE111154, GSE96804, GSE125779, GSE129973 and GSE121233). Each of the three groups (DN, FSGS, and Controls) had equal renal transcriptome data (n=32) included in the analysis to eliminate bias. The DEGs were identified using TAC4.0. Pathway analysis was performed on the discovered genes aligned to official gene symbols using Reactome, followed by functional gene enrichment analysis using Funrich, Enrichr..STRING and Network analyst investigated PPI, followed by Webgestalt's pathway erichment. Finally, using the Targetscan 7.0 and DIANA tools, filtered differential microRNAs downregulated in DN were evaluated for target identification.

resultBetween the three groups, DN, FSGS, and Control, a total of 194 DEGs with foldchange, >2 & <-2 and P-value 0.01 were found in the renal transcriptome. In comparison to control, 45 genes were elevated, particularly in DN, whereas 43 were upregulated specifically in FSGS. DN datasets were compared to FSGS in a separate analysis. FABP4, EBF1, ADIRF, and ART4 were shown to be among the substantially up-regulated genes unique to DN in both analyses. The transcriptional regulation of white adipocytes was discovered by pathway analysis.

conclusionThe molecular markers revealed might be employed as specific targets in the aetiology of DN, as well as in T2DM patients' therapeutic care.

Indexed as

Diabetic NephropathiesBiomarkersComputational BiologyComputer SimulationDiabetes Mellitus, Type 2Gene Expression ProfilingGene Regulatory NetworksGlomerulosclerosis, Focal SegmentalHumansMicroRNAsProtein Interaction MapsTranscriptomeBiomarkersMicroRNAscytokines.diagnosiskidney disease in diabetesnephropathyrenal biopsyRenal transcriptome

Identifiers

PMID35702773
OpenAlexW4282976696

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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 Socratic graph.