Evidence map›Paper›PMID 39962067›Full record

ArticleScientific reports2025

Predicting cell properties with AI from 3D imaging flow cytometer data.

Zunming Zhang, Yuxuan Zhu, Zhaoyu Lai, Minhong Zhou, Xinyu Chen, Rui Tang, William Alaynick, Sung Hwan Cho, Yu-Hwa Lo

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

9 authors.

Zunming ZhangDepartment of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, 92093, USA.
Yuxuan ZhuDepartment of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, 92093, USA.
Zhaoyu LaiDepartment of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, 92093, USA.
Minhong ZhouDepartment of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, 92093, USA.
Xinyu ChenDepartment of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, 92093, USA.
Rui TangNanoCellect Biomedical Inc., San Diego, CA, 92121, USA.
William AlaynickNanoCellect Biomedical Inc., San Diego, CA, 92121, USA.
Sung Hwan ChoNanoCellect Biomedical Inc., San Diego, CA, 92121, USA.
Yu-Hwa LoDepartment of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, 92093, USA. ylo@ucsd.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Predicting the properties of tissues or organisms from the genomics data is widely accepted by the medical community. Here we ask a question: can we predict the properties of each individual cell? Single-cell genomics does not work because the RNA sequencing process destroys the cell, not allowing us to verify our predictions. To test the hypothesis, we investigate the approach of using AI to analyze single-cell images obtained from a 3D imaging flow cytometer. We analyze the cell image at day zero and make the AI-assisted cell property prediction. The prediction is then examined later when the cells continue to live and develop. Our preliminary results are promising, showing 88% accuracy in predicting cells that will have a high protein expression level. The technique can have strong ramifications and impact on preventive medicine, drug development, cell therapy, and fundamental biomedical research.

Indexed as

Artificial IntelligenceFlow CytometryImaging, Three-DimensionalSingle-Cell AnalysisHumans

Identifiers

PMID39962067
PMCPMC11833109

What Socratic holds

Textmetadata
LicenceCC BY
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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.