Evidence map›Paper›PMID 37845808›Full record

ArticleAging cell2023

Form follows function: Nuclear morphology as a quantifiable predictor of cellular senescence.

Jakub Belhadj, Surina Surina, Markus Hengstschläger, Alexis J Lomakin

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in Aging cell, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
6.0field-weighted citation impact, top 3% 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

29 citing papers in PubMed, 34 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 2 countries.

Jakub BelhadjCenter for Pathobiochemistry & Genetics, Institute of Medical Genetics, Medical University of Vienna, Vienna, Austria.
Surina SurinaCenter for Pathobiochemistry & Genetics, Institute of Medical Genetics, Medical University of Vienna, Vienna, Austria.
Markus HengstschlägerCenter for Pathobiochemistry & Genetics, Institute of Medical Genetics, Medical University of Vienna, Vienna, Austria.
Alexis J LomakinCenter for Pathobiochemistry & Genetics, Institute of Medical Genetics, Medical University of Vienna, Vienna, Austria.ORCID 0000-0002-5346-3426
Medical University of Vienna · ATUniversity of Campania "Luigi Vanvitelli" · IT

Funding

Fellinger Krebsforschung 2023-07-0046
6 · The paper itself

Abstract

Enlarged or irregularly shaped nuclei are frequently observed in tissue cells undergoing senescence. However, it remained unclear whether this peculiar morphology is a cause or a consequence of senescence and how informative it is in distinguishing between proliferative and senescent cells. Recent research reveals that nuclear morphology can act as a predictive biomarker of senescence, suggesting an active role for the nucleus in driving senescence phenotypes. By employing deep learning algorithms to analyze nuclear morphology, accurate classification of cells as proliferative or senescent is achievable across various cell types and species both in vitro and in vivo. This quantitative imaging-based approach can be employed to establish links between senescence burden and clinical data, aiding in the understanding of age-related diseases, as well as assisting in disease prognosis and treatment response.

Indexed as

Cellular SenescenceBiomarkersPhenotypeBiomarkersartificial intelligencecell nucleuscellular biophysicscellular senescencecomputer visionmachine learningmorphogenesisquantitative microscopy

Identifiers

PMID37845808
PMCPMC10726876
OpenAlexW4387692094

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.