Evidence map›Paper›PMID 37744845›Full record

ArticleACS omega2023

Sinensetin Attenuated Macrophagic NLRP3 Inflammasomes Formation

Lin Lin, Kuimiao Deng, Zongrong Gong, Huifeng Fan, Dongwei Zhang, Gen Lu

Abstract read
In one paragraph

Article in ACS omega, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. 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

6 authors.

Lin LinDepartment of Respiration, Guangzhou Women and Children's Medical Centre, Guangzhou Medical University, Guangzhou 510120, Guangdong, China.
Kuimiao DengDepartment of Respiration, Guangzhou Women and Children's Medical Centre, Guangzhou Medical University, Guangzhou 510120, Guangdong, China.
Zongrong GongDepartment of Respiration, Guangzhou Women and Children's Medical Centre, Guangzhou Medical University, Guangzhou 510120, Guangdong, China.
Huifeng FanDepartment of Respiration, Guangzhou Women and Children's Medical Centre, Guangzhou Medical University, Guangzhou 510120, Guangdong, China.
Dongwei ZhangDepartment of Respiration, Guangzhou Women and Children's Medical Centre, Guangzhou Medical University, Guangzhou 510120, Guangdong, China.
Gen LuDepartment of Respiration, Guangzhou Women and Children's Medical Centre, Guangzhou Medical University, Guangzhou 510120, Guangdong, China.ORCID https://orcid.org/0009-0006-0161-5850

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Macrophage-mediated inflammation plays essential roles in multiple-organ injury. Sinensetin (SNS) at least exhibits anti-inflammation, antioxidant, and antitumor properties. However, the underlying mechanism of SNS-targeted macrophage-mediated inflammation remains elusive. In the present study, our results showed that SNS suppressed lipopolysaccharide (LPS)-induced inflammation to ameliorate lung and liver injuries. Mechanistically, SNS significantly inhibited M1-type macrophage polarization and its NLRP3 inflammasome formation to significantly decrease tumor necrosis factor α (TNFα) and IL-6 expression, while increasing IL-10 expression. Moreover, SNS interacted and activated SIRT1 to promote NRF2 and its target gene SOD2 transcription, which subsequently decreased LPS-induced inflammation. SIRT1 knockdown impaired the effects of SNS on the inhibition of macrophage polarization, NLRP3 inflammasome formation, and NRF2/SOD2 signaling. Taken together, our results showed that SNS is a potential and promising natural active ingredient to ameliorate inflammatory injury

Identifiers

PMID37744845
PMCPMC10515189

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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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.