Evidence map›Paper›PMID 32483455›Full record

ArticleTheranostics2020

Faster, sharper, more precise: Automated Cluster-FLIM in preclinical testing directly identifies the intracellular fate of theranostics in live cells and tissue.

Robert Brodwolf, Pierre Volz-Rakebrand, Johannes Stellmacher, Christopher Wolff, Michael Unbehauen, Rainer Haag, Monika Schäfer-Korting, Christian Zoschke, Ulrike Alexiev

Open access · goldAbstract read
In one paragraph

Article in Theranostics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
5.4field-weighted citation impact, top 4% 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

20 citing papers in PubMed, 33 citations in OpenAlex.

  1. Trial
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  7. Visualization of Nanocarriers and Drugs in Cells and Tissue.Handbook of experimental pharmacology · 2024
    Article
  8. Article
  9. In Vitro Models of Biological Barriers for Nanomedical Research.International journal of molecular sciences · 2022
    Review
  10. Article
  11. Article
  12. Label-free sensing of cells with fluorescence lifetime imaging: The quest for metabolic heterogeneity.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  13. Deep learning in macroscopic diffuse optical imaging.Journal of biomedical optics · 2022
    Review
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. 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 at 1 institution in 1 country.

Robert BrodwolfInstitute of Experimental Physics, Freie Universität Berlin, Arnimallee 14, 14195 Berlin, Germany.
Pierre Volz-RakebrandInstitute of Experimental Physics, Freie Universität Berlin, Arnimallee 14, 14195 Berlin, Germany.
Johannes StellmacherInstitute of Experimental Physics, Freie Universität Berlin, Arnimallee 14, 14195 Berlin, Germany.
Christopher WolffInstitute of Pharmacy (Pharmacology and Toxicology), Freie Universität Berlin, Königin-Luise-Str. 2+4, 14195 Berlin, Germany.
Michael UnbehauenInstitute of Chemistry and Biochemistry, Freie Universität Berlin, Takustr. 3, 14195 Berlin, Germany.
Rainer HaagInstitute of Chemistry and Biochemistry, Freie Universität Berlin, Takustr. 3, 14195 Berlin, Germany.
Monika Schäfer-KortingInstitute of Pharmacy (Pharmacology and Toxicology), Freie Universität Berlin, Königin-Luise-Str. 2+4, 14195 Berlin, Germany.
Christian ZoschkeInstitute of Pharmacy (Pharmacology and Toxicology), Freie Universität Berlin, Königin-Luise-Str. 2+4, 14195 Berlin, Germany.
Ulrike AlexievInstitute of Experimental Physics, Freie Universität Berlin, Arnimallee 14, 14195 Berlin, Germany.
Freie Universität Berlin · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fluorescence microscopy is widely used for high content screening in 2D cell cultures and 3D models. In particular, 3D tissue models are gaining major relevance in modern drug development. Enabling direct multiparametric evaluation of complex samples, fluorescence lifetime imaging (FLIM) adds a further level to intensity imaging by the sensitivity of the fluorescence lifetime to the microenvironment. However, the use of FLIM is limited amongst others by the acquisition of sufficient photon numbers without phototoxic effects in live cells. Herein, we developed a new cluster-based analysis method to enhance insight, and significantly speed up analysis and measurement time for the accurate translation of fluorescence lifetime information into pharmacological pathways.

Indexed as

AlgorithmsCarbocyaninesChildDrug Evaluation, PreclinicalFibroblastsFluorescent DyesHumansImage EnhancementImaging, Three-DimensionalKeratinocytesMaleMicroscopy, Fluorescence, MultiphotonNanoparticlesSkinCarbocyaninesFluorescent Dyesdrug developmentFLIM automatizationfluorescence lifetime imaging microscopyhigh throughput imagingnanomedicine

Identifiers

PMID32483455
PMCPMC7255044
OpenAlexW3027268103

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.