Evidence map›Paper›PMID 41112220›Full record

ArticleThe journal of liquid biopsy2025

Phenotype independent capture of circulating tumor cell using magnetic platelet decoys.

Kenise Morris, Colette Li, Sasmit Sarangi, Anne-Laure Papa

Abstract read
In one paragraph

Article in The journal of liquid biopsy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

4 authors.

Kenise MorrisDepartment of Biomedical Engineering, School of Engineering and Applied Science, The George Washington University, Washington, DC, 20052, USA.
Colette LiDepartment of Biomedical Engineering, School of Engineering and Applied Science, The George Washington University, Washington, DC, 20052, USA.
Sasmit SarangiDepartment of Neurology, The Warren Alpert Medical School of Brown University, Providence, 02903, RI, USA.
Anne-Laure PapaDepartment of Biomedical Engineering, School of Engineering and Applied Science, The George Washington University, Washington, DC, 20052, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Circulating tumor cells (CTCs) are an essential biomarker for metastatic disease as they provide valuable information regarding the primary tumor, metastatic potential, potential prognosis, as well as aid in patient monitoring and guiding personalized therapy. Successful detection, isolation, and enumeration of CTCs remains a challenge due to their rarity in blood and biological heterogeneity. Though traditionally known for their roles in maintaining hemostasis and promoting wound healing, platelets significantly contribute to cancer metastasis by interacting with CTCs in the bloodstream. These interactions protect CTCs from shear stress and immune detection, facilitate their arrest in blood vessels, and ultimately promote metastatic spread to distant tissues. We describe a system that leverages platelet-cancer cell interactions to target and retrieve CTCs from a liquid biopsy sample by engineering magnetic platelet decoys. Conventional techniques typically rely on specific markers on the CTC surface (most commonly EpCAM). Our approach begins with engineering platelet decoys that lose their functional ability but retain some functional surface receptors that enable their ability to interact with other cells, followed by ION (iron oxide nanoparticle) loading which permits CTC capture

Indexed as

Circulating tumor cell captureMetastasisPlatelet-cancer cell interaction

Identifiers

PMID41112220
PMCPMC12528870

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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 OpenQuestion graph.