Evidence map›Paper›PMID 36289886›Full record

ReviewBiomedicines2022

Glycation-Associated Diabetic Nephropathy and the Role of Long Noncoding RNAs.

Ankita Durge, Isha Sharma, Rashmi Santosh Tupe

Open access · goldAbstract readReview
In one paragraph

Review in Biomedicines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 54% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Review
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

3 authors at 2 institutions in 2 countries.

Ankita DurgeSymbiosis School of Biological Sciences (SSBS), Symbiosis International (Deemed University), Lavale, Mulshi, Pune 412115, Maharashtra, India.ORCID 0000-0002-2383-8776
Isha SharmaDepartment of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.ORCID 0000-0003-3936-914X
Rashmi Santosh TupeSymbiosis School of Biological Sciences (SSBS), Symbiosis International (Deemed University), Lavale, Mulshi, Pune 412115, Maharashtra, India.ORCID 0000-0002-6726-3601
Symbiosis International University · INNorthwestern University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The glycation of various biomolecules is the root cause of many pathological conditions associated with diabetic nephropathy and end-stage kidney disease. Glycation imbalances metabolism and increases renal cell injury. Numerous therapeutic measures have narrowed down the adverse effects of endogenous glycation, but efficient and potent measures are miles away. Recent advances in the identification and characterization of noncoding RNAs, especially the long noncoding RNAs (lncRNAs), have opened a mammon of new biology to explore the mitigations for glycation-associated diabetic nephropathy. Furthermore, tissue-specific distribution and condition-specific expression make lncRNA a promising key for second-generation therapeutic interventions. Though the techniques to identify and exemplify noncoding RNAs are rapidly evolving, the lncRNA study encounters multiple methodological constraints. This review will discuss lncRNAs and their possible involvement in glycation and advanced glycation end products (AGEs) signaling pathways. We further highlight the possible approaches for lncRNA-based therapeutics and their working mechanism for perturbing glycation and conclude our review with lncRNAs biology-related future opportunities.

Indexed as

biomarkersdiabetesdiabetic nephropathyglycationlong noncoding RNAtherapeutics

Identifiers

PMID36289886
PMCPMC9599575
OpenAlexW4306837104

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.