Evidence map›Paper›PMID 39349489›Full record

ArticleNPJ Regenerative medicine2024

Utilising an in silico model to predict outcomes in senescence-driven acute liver injury.

Candice Ashmore-Harris, Evangelia Antonopoulou, Rhona E Aird, Tak Yung Man, Simon M Finney, Annelijn M Speel, Wei-Yu Lu, Stuart J Forbes, Victoria L Gadd, Sarah L Waters

Abstract read
In one paragraph

Article in NPJ Regenerative medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

10 authors.

Candice Ashmore-Harris *Centre for Regenerative Medicine, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh BioQuarter, Edinburgh, UK.ORCID http://orcid.org/0000-0001-9270-8599
Evangelia Antonopoulou *Mathematical Institute, University of Oxford, Oxford, UK.
Rhona E AirdCentre for Regenerative Medicine, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh BioQuarter, Edinburgh, UK.ORCID http://orcid.org/0000-0002-1014-8063
Tak Yung ManCentre for Regenerative Medicine, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh BioQuarter, Edinburgh, UK.
Simon M FinneyMathematical Institute, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0003-4150-0784
Annelijn M SpeelCentre for Regenerative Medicine, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh BioQuarter, Edinburgh, UK.
Wei-Yu LuCentre for Inflammation Research, Institute for Regeneration & Repair, University of Edinburgh, Edinburgh BioQuarter, Edinburgh, UK.
Stuart J ForbesCentre for Regenerative Medicine, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh BioQuarter, Edinburgh, UK.ORCID http://orcid.org/0000-0003-3715-2561
Victoria L GaddCentre for Regenerative Medicine, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh BioQuarter, Edinburgh, UK. victoria.gadd@ed.ac.uk.ORCID http://orcid.org/0000-0003-1819-8660
Sarah L WatersMathematical Institute, University of Oxford, Oxford, UK. waters@maths.ox.ac.uk.ORCID http://orcid.org/0000-0001-5285-0523

Funding

RCUK | Medical Research Council (MRC) MR/T015489/1
6 · The paper itself

Abstract

Currently liver transplantation is the only treatment option for liver disease, but organ availability cannot meet patient demand. Alternative regenerative therapies, including cell transplantation, aim to modulate the injured microenvironment from inflammation and scarring towards regeneration. The complexity of the liver injury response makes it challenging to identify suitable therapeutic targets when relying on experimental approaches alone. Therefore, we adopted a combined in vivo-in silico approach and developed an ordinary differential equation model of acute liver disease able to predict the host response to injury and potential interventions. The Mdm2

Identifiers

PMID39349489
PMCPMC11442582

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.