Evidence map›Paper›PMID 39737963›Full record

ArticleNature communications2024

Neuromorphic-enabled video-activated cell sorting.

Weihua He, Junwen Zhu, Yongxiang Feng, Fei Liang, Kaichao You, Huichao Chai, Zhipeng Sui, Haiqing Hao, Guoqi Li, Jingjing Zhao and 3 more

Abstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing 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

27 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Virtual cell: Current perspectives and future prospects.The Journal of international medical research · 2026
    Review
  5. Beyond the infusion: nursing at the vanguard of cytokine release syndrome rescue in CAR-T cell therapy.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Review
  6. Review
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Review
  13. Review
  14. Ultrafast Dynamic Defect Inspection With Computational Neuromorphic Imaging.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Review
  20. 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

13 authors.

Weihua He *State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, China.
Junwen Zhu *State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, China.
Yongxiang FengState Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, China.
Fei LiangState Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, China.ORCID 0000-0002-9351-9375
Kaichao YouSoftware School, Tsinghua University, Beijing, China.
Huichao ChaiState Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, China.ORCID 0000-0001-8683-6455
Zhipeng SuiState Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, China.
Haiqing HaoState Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, China.
Guoqi LiInstitute of Automation, Chinese Academy of Sciences, Beijing, China.ORCID 0000-0002-0904-8524
Jingjing ZhaoInstitute of Medical Equipment Science and Engineering, Huazhong University of Science and Technology, Wuhan, China.
Lei DengCenter for Brain-Inspired Computing Research (CBICR), Beijing Advanced Innovation Center for Integrated Circuits, Optical Memory National Engineering Research Center, & Department of Precision Instrument, Tsinghua University, Beijing, China.ORCID 0000-0002-5172-9411
Rong ZhaoCenter for Brain-Inspired Computing Research (CBICR), Beijing Advanced Innovation Center for Integrated Circuits, Optical Memory National Engineering Research Center, & Department of Precision Instrument, Tsinghua University, Beijing, China.ORCID 0000-0002-2320-0326
Wenhui WangState Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, China. wwh@mail.tsinghua.edu.cn.ORCID 0000-0002-5884-6098

Funding

National Natural Science Foundation of China (National Science Foundation of China) 62174096
6 · The paper itself

Abstract

Imaging flow cytometry allows image-activated cell sorting (IACS) with enhanced feature dimensions in cellular morphology, structure, and composition. However, existing IACS frameworks suffer from the challenges of 3D information loss and processing latency dilemma in real-time sorting operation. Herein, we establish a neuromorphic-enabled video-activated cell sorter (NEVACS) framework, designed to achieve high-dimensional spatiotemporal characterization content alongside high-throughput sorting of particles in wide field of view. NEVACS adopts event camera, CPU, spiking neural networks deployed on a neuromorphic chip, and achieves sorting throughput of 1000 cells/s with relatively economic hybrid hardware solution (~$10 K for control) and simple-to-make-and-use microfluidic infrastructures. Particularly, the application of NEVACS in classifying regular red blood cells and blood-disease-relevant spherocytes highlights the accuracy of using video over a single frame (i.e., average error of 0.99% vs 19.93%), indicating NEVACS' potential in cell morphology screening and disease diagnosis.

Indexed as

Flow CytometryNeural Networks, ComputerCell SeparationErythrocytesHumansVideo Recording

Identifiers

PMID39737963
PMCPMC11685671

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.