Evidence map›Paper›PMID 38766184›Full record

ArticleResearch square2024

Beyond the Granuloma: New Insights into Cardiac Sarcoidosis Using Spatial Proteomics.

Eliot Peyster, David Smith, Therese Bittermann, Paco Bravo, Kenneth Margulies

Abstract readPreprint
In one paragraph

Article in Research square, 2024. 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

5 · Who and what money

Authors and funding

5 authors.

Eliot PeysterUniversity of Pennsylvania.ORCID 0000-0002-3350-3136
David SmithChildren's Hospital of Philadelphia.ORCID 0000-0001-7858-3785
Therese BittermannUniversity of Pennsylvania.
Paco BravoUniversity of Pennsylvania.
Kenneth MarguliesUniversity of Pennsylvania.ORCID 0000-0002-8093-4465

Funding

Personalized Rejection Risk Assessment in Cardiac TransplantationK08HL159344 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI PEYSTER, ELIOT · 2021 to 2024
$667k
NHLBI NIH HHS K08 HL159344
6 · The paper itself

Abstract

Cardiac sarcoidosis is poorly understood, challenging to diagnose, and portends a poor prognosis. A lack of animal models necessitates the use of residual human samples to study sarcoidosis, which in turn necessitates the use of analytical tools compatible with archival, fixed tissue. We employed high-plex spatial protein analysis within a large cohort of archival human cardiac sarcoidosis and control tissue samples, studying the immunologic, fibrotic, and metabolic landscape of sarcoidosis at different stages of disease, in different cardiac tissue compartments, and in tissue regions with and without overt inflammation. Utilizing a small set of differentially expressed protein biomarkers, we also report the development of a predictive model capable of accurately discriminating between control cardiac tissue and sarcoidosis tissue, even when no histologic evidence of sarcoidosis is present. This finding has major translational implications, with the potential to markedly improve the diagnostic yield of clinical biopsies obtained from suspected sarcoidosis patients.

Identifiers

PMID38766184
PMCPMC11100892

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.