Evidence map›Paper›PMID 42034710›Full record

ArticleCommunications medicine2026

Metabolic profiling of steatotic liver disease by fluorescence lifetime imaging microscopy.

Kaitlyn Purdie, Narain Karedla, Thea Guy, Anna V Schepers, Ana Isabel Espirito Santo, Huw Colin-York, Kseniya Korobchevskaya, Helena Coker, Carl Lee, Alex Gordon-Weeks and 2 more

Abstract read
In one paragraph

Article in Communications medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Kaitlyn PurdieNuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK.ORCID http://orcid.org/0009-0007-0348-5938
Narain KaredlaNuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-7891-3825
Thea GuyNuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK.
Anna V SchepersNuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0003-0066-1784
Ana Isabel Espirito SantoNuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK.
Huw Colin-YorkNuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-6585-3237
Kseniya KorobchevskayaNuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-0411-7447
Helena CokerNuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0001-7012-8139
Carl LeeNuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-6776-2793
Alex Gordon-WeeksNuffield Department of Surgical Sciences, University of Oxford, Oxford, UK.
Jagdeep NanchahalNuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-9579-9411
Marco FritzscheNuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK. marco.fritzsche@kennedy.ox.ac.uk.ORCID http://orcid.org/0000-0002-8712-7471

Funding

Deutsche Forschungsgemeinschaft (German Research Foundation) 533765530RCUK | Medical Research Council (MRC) APP23835
6 · The paper itself

Abstract

backgroundMetabolic dysfunction-associated steatotic liver disease is defined by hepatic lipid overload resulting in a metabolic shift and subsequent mitochondrial impairment. Diagnosis currently relies on tissue biopsy and non-invasive tests. However, these have drawbacks, including subjective histology scoring and relatively low sensitivity, highlighting the need for more robust and reproducible methodologies.

methodsFluorescence lifetime imaging microscopy visualises the metabolic state of cells by measuring the autofluorescence lifetime of metabolites, effectively avoiding the need for exogenous labelling. This technique was applied to a broad range of models, spanning from a hepatocyte cell line to a human tissue slice model, to investigate metabolic changes across disease conditions.

resultsHere, by utilising the metabolic dysfunction associated with steatotic liver disease, we propose a time-efficient method and introduce an index as a quantitative output to assess the metabolic state of human liver biopsies. The index encapsulates features of metabolic dysfunction that directly report on the disease state. These findings using lifetime imaging are substantiated by extensive analysis of structural and functional mitochondrial dysfunction.

conclusionsMeasuring fluorescence lifetime can capture features of metabolic change that standard histological methods do not. Correlating the results to established techniques of histological evaluation highlights the potential of this method to enhance characterisation and speed of biopsy results in metabolically implicated diseases.

Identifiers

PMID42034710
PMCPMC13324169

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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.