Evidence map›Paper›PMID 30674959›Full record

Trial reportScientific reports2019

Lipid Uptake by Alveolar Macrophages Drives Fibrotic Responses to Silica Dust.

Xiaomin Hou, Ross Summer, Ziying Chen, Ying Tian, Jingjing Ma, Jie Cui, Xiaohui Hao, Lingli Guo, Hong Xu, Hongli Wang and 1 more

Open access · goldAbstract readClinical Trial
In one paragraph

Trial report in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed
4.7field-weighted citation impact, top 4% 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

31 citing papers in PubMed, 59 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Silicosis: from pathogenesis to therapeutics.Frontiers in pharmacology · 2025
    Review
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Editorial: Host immune evasion byFrontiers in immunology · 2022
    Article
  20. 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

11 authors at 2 institutions in 2 countries.

Xiaomin HouSchool of Public Health, North China University of Science and Technology, Tangshan, Hebei, 063210, China.
Ross SummerCenter for Translational Medicine and Jane and Leonard Korman Lung Center, Thomas Jefferson University, Philadelphia, PA, 19107, USA.
Ziying ChenSchool of Public Health, North China University of Science and Technology, Tangshan, Hebei, 063210, China.
Ying TianSchool of Public Health, North China University of Science and Technology, Tangshan, Hebei, 063210, China.
Jingjing MaSchool of Public Health, North China University of Science and Technology, Tangshan, Hebei, 063210, China.
Jie CuiSchool of Public Health, North China University of Science and Technology, Tangshan, Hebei, 063210, China.
Xiaohui HaoMedical Research Center, Health, North China University of Science and Technology, Tangshan, Hebei, 063210, China.
Lingli GuoMedical Research Center, Health, North China University of Science and Technology, Tangshan, Hebei, 063210, China.
Hong XuCenter for Translational Medicine and Jane and Leonard Korman Lung Center, Thomas Jefferson University, Philadelphia, PA, 19107, USA.
Hongli WangSchool of Public Health, North China University of Science and Technology, Tangshan, Hebei, 063210, China. whllhl@163.com.
Heliang LiuSchool of Public Health, North China University of Science and Technology, Tangshan, Hebei, 063210, China. tsruoshui@163.com.
North China University of Science and Technology · CNThomas Jefferson University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Silicosis is a common occupational disease and represents a significant contributor to respiratory morbidity and mortality worldwide. Lipid-laden macrophages, or foam cells, are observed in the lungs of patients with silicosis but the mechanisms mediating their formation remain poorly understood. In this study, we sought to elucidate the mechanisms by which silica promotes foam cell formation in the lung, and to determine whether uptake of lipids alone is sufficient to drive TGF-β production by alveolar macrophages. Consistent with previous reports, we found that foam cells were markedly increased in the lungs of patients with silicosis and that these findings associated with both higher levels of intracellular lipid levels (oxidized LDL, ox-LDL) and elevated transcript levels for the lipid scavenger receptor CD36 and the nuclear receptor PPARγ. Employing a rat alveolar macrophage cell line, we found that exposure to silica dust or ox-LDL alone had a modest effect on the induction of foam cell formation and only silica was capable of inducing the production of TGF-β. In contrast, foam cell formation and TGF-β production were both dramatically increased when cells were exposed to a combination of silica dust and ox-LDL. Moreover, we found that these endpoints were markedly attenuated by either blocking CD36 or inhibiting the activity of PPARγ. Altogether, our findings suggest that foam cell formation and TGF-β production are driven by the simultaneous uptake of silica and lipids in alveolar macrophages and that strategies aimed at blocking lipid uptake by alveolar macrophages might be effective in ameliorating fibrotic responses to silica in the lung.

Indexed as

DustFoam CellsMacrophages, AlveolarSilicosisAdultAnimalsCD36 AntigensCell LineFemaleHumansLipoproteins, LDLMaleMiddle AgedPulmonary FibrosisRatsSilicon DioxideCD36 AntigensCD36 protein, humanDustLipoproteins, LDLoxidized low density lipoproteinSilicon Dioxide

Identifiers

PMID30674959
PMCPMC6344530
OpenAlexW2911502385

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.