Evidence map›Paper›PMID 35453505›Full record

ReviewBiomedicines2022

PI3K Signaling in Mechanisms and Treatments of Pulmonary Fibrosis Following Sepsis and Acute Lung Injury.

Jean Piero Margaria, Lucia Moretta, Jose Carlos Alves-Filho, Emilio Hirsch

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 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
8.4field-weighted citation impact, top 2% 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

30 citing papers in PubMed, 59 citations in OpenAlex.

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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

4 authors at 2 institutions in 2 countries.

Jean Piero MargariaDepartment of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Torino, Via Nizza 52, 10126 Torino, Italy.
Lucia MorettaDepartment of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Torino, Via Nizza 52, 10126 Torino, Italy.ORCID 0000-0003-1920-6749
Jose Carlos Alves-FilhoDepartment of Pharmacology, Ribeirão Preto Medical School, University of São Paulo, Avenida Bandeirantes 3900, Ribeirao Preto 14049-900, Brazil.ORCID 0000-0002-9918-8714
Emilio HirschDepartment of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Torino, Via Nizza 52, 10126 Torino, Italy.ORCID 0000-0002-9073-6024
University of Turin · ITUniversidade de São Paulo · BR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pulmonary fibrosis is a pathological fibrotic process affecting the lungs of five million people worldwide. The incidence rate will increase even more in the next years due to the long-COVID-19 syndrome, but a resolving treatment is not available yet and usually prognosis is poor. The emerging role of the phosphatidylinositol 3-kinase (PI3K)/AKT signaling in fibrotic processes has inspired the testing of drugs targeting the PI3K/Akt pathway that are currently under clinical evaluation. This review highlights the progress in understanding the role of PI3K/Akt in the development of lung fibrosis and its causative pathological context, including sepsis as well as acute lung injury (ALI) and its consequent acute respiratory distress syndrome (ARDS). We further summarize current knowledge about PI3K inhibitors for pulmonary fibrosis treatment, including drugs under development as well as in clinical trials. We finally discuss how the design of inhaled compounds targeting the PI3K pathways might potentiate efficacy and improve tolerability.

Indexed as

ARDSinhibitorPI3Kpulmonary fibrosissepsis

Identifiers

PMID35453505
PMCPMC9028704
OpenAlexW4221008539

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.