Evidence map›Paper›PMID 38045568›Full record

ArticleCurrent research in biotechnology2023

Phenotyping senescent mesenchymal stromal cells using AI image translation.

Leya Weber, Brandon S Lee, Sara Imboden, Cho-Jui Hsieh, Neil Y C Lin

Abstract read
In one paragraph

Article in Current research in biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Review
  2. Article
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  4. Article
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  7. Article
  8. Review
  9. Measurable Metrics of Mesenchymal Stem Cell Aging.Sovremennye tekhnologii v meditsine · 2025
    Article
  10. Review
  11. Review
  12. Article
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

5 authors.

Leya WeberDepartment of Mechanical and Aerospace Engineering, University of California, Los Angeles 90095, CA, United States.
Brandon S LeeDepartment of Bioengineering, University of California, Los Angeles 90095, CA, United States.
Sara ImbodenDepartment of Mechanical and Aerospace Engineering, University of California, Los Angeles 90095, CA, United States.
Cho-Jui HsiehDepartment of Computer Science, University of California, Los Angeles 90095, CA, United States.
Neil Y C LinDepartment of Mechanical and Aerospace Engineering, University of California, Los Angeles 90095, CA, United States.

Funding

AI-Informed Signaling Factor Design for In Vitro Rejuvenating Mesenchymal Stromal CellsR35GM146735 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Neil Lin · 2022 to 2026
$2.3M
NIGMS NIH HHS R35 GM146735
6 · The paper itself

Abstract

Mesenchymal stromal cells (MSCs) offer promising potential in biomedical research, clinical therapeutics, and immunomodulatory therapies due to their ease of isolation and multipotent, immunoprivileged, and immunosuppersive properties. Extensive efforts have focused on optimizing the cell isolation and culture methods to generate scalable, therapeutically-relevant MSCs for clinical applications. However, MSC-based therapies are often hindered by cell heterogeneity and inconsistency of therapeutic function caused, in part, by MSC senescence. As such, noninvasive and molecular-based MSC characterizations play an essential role in assuring the consistency of MSC functions. Here, we demonstrated that AI image translation algorithms can effectively predict immunofluorescence images of MSC senescence markers from phase contrast images. We showed that the expression level of senescence markers including senescence-associated beta-galactosidase (SABG), p16, p21, and p38 are accurately predicted by deep-learning models for Doxorubicin-induced MSC senescence, irradiation-induced MSC senescence, and replicative MSC senescence. Our AI model distinguished the non-senescent and senescent MSC populations and simultaneously captured the cell-to-cell variability within a population. Our microscopy-based phenotyping platform can be integrated with cell culture routines making it an easily accessible tool for MSC engineering and manufacturing.

Indexed as

AI image translationCell manufacturingMSC phenotypingSenescence

Identifiers

PMID38045568
PMCPMC10691861

What Socratic 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 Socratic graph.