Evidence map›Paper›PMID 37112600›Full record

ArticleToxics2023

Effects of Multiwall Carbon Nanotubes on Premature Kidney Aging: Biochemical and Histological Analysis.

Ji-Eun Kim, Myung-Haing Cho

Open access · goldAbstract read
In one paragraph

Article in Toxics, 2023. 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
1.7field-weighted citation impact, top 15% 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, 6 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

2 authors at 1 institution in 2 countries.

Ji-Eun KimLaboratory of Toxicology, College of Veterinary Medicine, Seoul National University, Seoul 151-742, Republic of Korea.ORCID 0000-0002-3643-2177
Myung-Haing ChoLaboratory of Toxicology, College of Veterinary Medicine, Seoul National University, Seoul 151-742, Republic of Korea.
Seoul National University · KR

Funding

Korea National Research Foundation NRF-2012-0006206
6 · The paper itself

Abstract

Carbon nanotubes (CNTs) have gained much attention due to their superb properties, which make them promising options for the reinforcing composite materials with desirable mechanical properties. However, little is known about the linkage between lung exposure to nanomaterials and kidney disease. In this study, we compared the effects on the kidneys and aging for two different types of multiwall carbon nanotubes (MWCNTs): pristine MWCNTs (PMWCNTs) and acid-treated MWCNTs (TMWCNTs), with TMWCNTs being the preferred form for use as a composite material due to its superior dispersion properties. We used tracheal instillation and maximum tolerated dose (MTD) for both types of CNTs. MTD was determined as a 10% weight loss dose in a 3-month subchronic study, and the appropriate dosage for 1-year exposure was 0.1 mg/mouse. Serum and kidney samples were analyzed using ELISA, Western blot, and immunohistochemistry after 6 months and 1 year of treatment. PMWCNT-administered mice showed the activation of pathways for inflammation, apoptosis, and insufficient autophagy, as well as decreased serum Klotho levels and increased serum levels of DKK-1, FGF-23, and sclerostin, while TMWCNTs did not. Our study suggests that lung exposure to PMWCNTs can induce premature kidney aging and highlights a possible toxic effect of using MWCNTs on the kidneys in the industrial field, further highlighting that dispersibility can affect the toxicity of the nanotubes.

Indexed as

kidneymultiwall carbon nanotubesmultiwall carbon nanotubes (MWCNTs)premature agingtoxicity

Identifiers

PMID37112600
PMCPMC10143039
OpenAlexW4365512165

What OpenQuestion holds

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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.