Evidence map›Paper›PMID 35334659›Full record

ArticleMicromachines2022

Single Red Blood Cell Hydrodynamic Traps via the Generative Design.

Georgii V Grigorev, Nikolay O Nikitin, Alexander Hvatov, Anna V Kalyuzhnaya, Alexander V Lebedev, Xiaohao Wang, Xiang Qian, Georgii V Maksimov, Liwei Lin

Abstract read
In one paragraph

Article in Micromachines, 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. 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.

Georgii V GrigorevData Science and Information Technology Research Center, Tsinghua Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518055, China.
Nikolay O NikitinNSS Lab, ITMO University, 197101 Saint-Petersburg, Russia.ORCID 0000-0002-6839-9957
Alexander HvatovNSS Lab, ITMO University, 197101 Saint-Petersburg, Russia.ORCID 0000-0002-5222-583X
Anna V KalyuzhnayaNSS Lab, ITMO University, 197101 Saint-Petersburg, Russia.
Alexander V LebedevMachine Building Department, Bauman Moscow State Technical University, 105005 Moscow, Russia.
Xiaohao WangShenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.
Xiang QianShenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.ORCID 0000-0003-2487-8785
Georgii V MaksimovBiophysics Lab, Biology Department, Moscow State University, 119192 Moscow, Russia.ORCID 0000-0002-7377-0773
Liwei LinMechanical Department, University of California in Berkeley, Berkeley, CA 94703, USA.

Funding

Ministry of Science and Higher Education of Russian Federation FSER-2021-0012
6 · The paper itself

Abstract

This paper describes a generative design methodology for a micro hydrodynamic single-RBC (red blood cell) trap for applications in microfluidics-based single-cell analysis. One key challenge in single-cell microfluidic traps is to achieve desired through-slit flowrates to trap cells under implicit constraints. In this work, the cell-trapping design with validation from experimental data has been developed by the generative design methodology with an evolutionary algorithm. L-shaped trapping slits have been generated iteratively for the optimal geometries to trap living-cells suspended in flow channels. Without using the generative design, the slits have low flow velocities incapable of trapping single cells. After a search with 30,000 solutions, the optimized geometry was found to increase the through-slit velocities by 49%. Fabricated and experimentally tested prototypes have achieved 4 out of 4 trapping efficiency of RBCs. This evolutionary algorithm and trapping design can be applied to cells of various sizes.

Indexed as

artificial intelligencecell trapevolutionary algorithmgenerative designmicrofluidicsRBC

Identifiers

PMID35334659
PMCPMC8952688

What OpenQuestion holds

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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.