Evidence map›Paper›PMID 40699390›Full record

ReviewMolecular biology reports2025

Pulmonary fibrosis and exosomes: pathways to treatment.

R J Nija, T G Nithya

Abstract readReview
PubMed Publisher
In one paragraph

Review in Molecular biology reports, 2025. 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. Review
  2. Review
  3. Article
  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

2 authors.

R J NijaDepartment of Biochemistry, Faculty of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India.
T G NithyaDepartment of Biochemistry, Faculty of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India. nithyag@srmist.edu.in.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Idiopathic pulmonary fibrosis (IPF) is a progressive, life-threatening lung disease characterized by excessive extracellular matrix (ECM) deposition, fibroblast proliferation, and epithelial injury. Disease progression involves key signaling pathways-TGF-β, WNT/β-catenin, PI3K/AKT, and YAP/TAZ-that drive fibroblast activation, epithelial-to-mesenchymal transition (EMT), endothelial-mesenchymal transition (EndMT), and ECM remodeling. Recent studies have highlighted the role of small extracellular vesicles (sEVs), particularly exosomes, as crucial modulators of the fibrotic microenvironment. These vesicles carry microRNAs (miRNAs), proteins, and lipids that influence fibroblast behaviour, immune signaling, and tissue architecture.Growing evidence supports the therapeutic promise of exosomes and engineered nanoparticle-exosome hybrids in targeting dysregulated fibrotic pathways and promoting tissue repair. This review examines the molecular mechanisms underlying pulmonary fibrosis, focusing on the role of exosomal crosstalk and therapeutic potential. We also incorporate emerging insights from single-cell RNA sequencing (scRNA-seq) on fibroblast heterogeneity, the influence of senescence-associated secretory phenotype (SASP) on exosomal cargo, and the limitations of current animal models in recapitulating chronic IPF. Understanding these mechanisms may facilitate the development of precision medicine approaches for managing and potentially reversing IPF progression.

Indexed as

ExosomesIdiopathic Pulmonary FibrosisPulmonary FibrosisAnimalsEpithelial-Mesenchymal TransitionExtracellular MatrixFibroblastsHumansMicroRNAsSignal TransductionMicroRNAsEMTEndMTExosomesPulmonary fibrosis

Identifiers

What OpenQuestion 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 OpenQuestion graph.