Evidence map›Paper›PMID 38167247›Full record

ArticleNature communications2024

AI-driven projection tomography with multicore fibre-optic cell rotation.

Jiawei Sun, Bin Yang, Nektarios Koukourakis, Jochen Guck, Juergen W Czarske

Open access · goldAbstract 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 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
7.7field-weighted citation impact, top 2% of its field
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

14 citing papers in PubMed, 42 citations in OpenAlex.

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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

5 authors at 4 institutions in 2 countries.

Jiawei SunShanghai Artificial Intelligence Laboratory, Longwen Road 129, Xuhui District, 200232, Shanghai, China. sunjiawei1@pjlab.org.cn.ORCID 0000-0001-8663-1556
Bin YangLaboratory of Measurement and Sensor System Technique (MST), TU Dresden, Dresden, Germany.
Nektarios KoukourakisCompetence Center for Biomedical Computational Laser Systems (BIOLAS), TU Dresden, Helmholtzstrasse 18, 01069, Dresden, Germany.
Jochen GuckMax Planck Institute for the Science of Light & Max Planck-Zentrum für Physik und Medizin, 91058, Erlangen, Germany.ORCID 0000-0002-1453-6119
Juergen W CzarskeCompetence Center for Biomedical Computational Laser Systems (BIOLAS), TU Dresden, Helmholtzstrasse 18, 01069, Dresden, Germany. juergen.czarske@tu-dresden.de.ORCID 0000-0001-7280-0523
Center for Systems Biology Dresden · DEBeijing Academy of Artificial Intelligence · CNMax Planck Institute for the Science of Light · DETechnische Universität Dresden · DE

Funding

Deutsche Forschungsgemeinschaft (German Research Foundation) CZ55/40-1
6 · The paper itself

Abstract

Optical tomography has emerged as a non-invasive imaging method, providing three-dimensional insights into subcellular structures and thereby enabling a deeper understanding of cellular functions, interactions, and processes. Conventional optical tomography methods are constrained by a limited illumination scanning range, leading to anisotropic resolution and incomplete imaging of cellular structures. To overcome this problem, we employ a compact multi-core fibre-optic cell rotator system that facilitates precise optical manipulation of cells within a microfluidic chip, achieving full-angle projection tomography with isotropic resolution. Moreover, we demonstrate an AI-driven tomographic reconstruction workflow, which can be a paradigm shift from conventional computational methods, often demanding manual processing, to a fully autonomous process. The performance of the proposed cell rotation tomography approach is validated through the three-dimensional reconstruction of cell phantoms and HL60 human cancer cells. The versatility of this learning-based tomographic reconstruction workflow paves the way for its broad application across diverse tomographic imaging modalities, including but not limited to flow cytometry tomography and acoustic rotation tomography. Therefore, this AI-driven approach can propel advancements in cell biology, aiding in the inception of pioneering therapeutics, and augmenting early-stage cancer diagnostics.

Indexed as

TomographyTomography, OpticalAlgorithmsArtificial IntelligenceFiber Optic TechnologyHumansImage Processing, Computer-AssistedPhantoms, ImagingRotation

Identifiers

PMID38167247
PMCPMC10762230
OpenAlexW4390510206

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.