Evidence map›Paper›PMID 35513689›Full record

ArticleScientific reports2022

Biophysical studies of cancer cells' traverse-vessel behaviors under different pressures revealed cells' motion state transition.

Xiao Li, Jialin Shi, Ziqing Gao, Jian Xu, Shujing Wang, Xin Li, Qi Ouyang, Chunxiong Luo

Abstract read
In one paragraph

Article in Scientific reports, 2022. 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. Article
  2. 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

8 authors.

Xiao LiThe State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics, Peking University, Beijing, China.
Jialin ShiThe State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics, Peking University, Beijing, China.
Ziqing GaoPeking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.
Jian XuThe State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics, Peking University, Beijing, China.
Shujing WangThe State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics, Peking University, Beijing, China.
Xin LiCenter for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
Qi OuyangThe State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics, Peking University, Beijing, China.
Chunxiong LuoThe State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics, Peking University, Beijing, China. pkuluocx@pku.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Circulating tumor cells (CTCs) survive in the bloodstream and then seed and invade to foster tumor metastasis. The arrest of cancer cells is favored by permissive flow forces and geometrical constraints. Through the use of high-throughput microfluidic devices designed to mimic capillary-sized vessels, we applied pressure differences to cancer cells (MCF-7 cell line) and recorded the cell traverse-vessel behaviors. Our results showed that cancer cells transform from a Newtonian droplet state to an adhesion/migration state when cancer cells traverse artificial vessels. To explain these phenomena, a modified Newtonian droplet model was also proposed. These phenomena and the modified model may reveal how CTCs in the blood seed and invade vessels under suitable conditions.

Indexed as

Neoplastic Cells, CirculatingCell Line, TumorHumansLab-On-A-Chip DevicesMCF-7 Cells

Identifiers

PMID35513689
PMCPMC9072532

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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.