Evidence map›Paper›PMID 39233442›Full record

ArticleBiophysical journal2024

Deep learning permits imaging of multiple structures with the same fluorophores.

Luhong Jin, Jingfang Liu, Heng Zhang, Yunqi Zhu, Haixu Yang, Jianhang Wang, Luhao Zhang, Cuifang Kuang, Baohua Ji, Ju Zhang and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biophysical journal, 2024. 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. 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

12 authors.

Luhong JinSchool of Information Science and Technology, Hangzhou Normal University, Hangzhou, China; Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China.
Jingfang LiuDepartment of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China.
Heng ZhangDepartment of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China.
Yunqi ZhuDepartment of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China.
Haixu YangDepartment of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China; Binjiang Institute of Zhejiang University, Hangzhou, China.
Jianhang WangDepartment of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China.
Luhao ZhangDepartment of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China; Binjiang Institute of Zhejiang University, Hangzhou, China.
Cuifang KuangState Key Laboratory of Extreme Photonics and Instrumentation, Department of Optical Engineering, Zhejiang University, Hangzhou, China.
Baohua JiInstitute of Biomechanics and Applications, Department of Engineering Mechanics, Zhejiang University, Hangzhou, China.
Ju ZhangSchool of Information Science and Technology, Hangzhou Normal University, Hangzhou, China.
Xu LiuState Key Laboratory of Extreme Photonics and Instrumentation, Department of Optical Engineering, Zhejiang University, Hangzhou, China.
Yingke XuDepartment of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China; Binjiang Institute of Zhejiang University, Hangzhou, China; Department of Endocrinology, Children's Hospital of Zhejiang University School of Medicine, National Clinical Research Center for Children's Health, Hangzhou, China. Electronic address: yingkexu@zju.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fluorescence microscopy, which employs fluorescent tags to label and observe cellular structures and their dynamics, is a powerful tool for life sciences. However, due to the spectral overlap between different dyes, a limited number of structures can be separately labeled and imaged for live-cell applications. In addition, the conventional sequential channel imaging procedure is quite time consuming, as it needs to switch either different lasers or filters. Here, we propose a novel double-structure network (DBSN) that consists of multiple connected models, which can extract six distinct subcellular structures from three raw images with only two separate fluorescent labels. DBSN combines the intensity-balance model to compensate for uneven fluorescent labels for different structures and the structure-separation model to extract multiple different structures with the same fluorescent labels. Therefore, DBSN breaks the bottleneck of the existing technologies and holds immense potential applications in the field of cell biology.

Indexed as

Deep LearningFluorescent DyesMicroscopy, FluorescenceHumansImage Processing, Computer-AssistedFluorescent Dyes

Identifiers

PMID39233442
PMCPMC11494491

What OpenQuestion holds

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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.