Evidence map›Paper›PMID 33572130›Full record

ArticleSensors (Basel, Switzerland)2021

Hyperdimensional Imaging Contrast Using an Optical Fiber.

Jenu V Chacko, Han Nim Lee, Wenxin Wu, Marisa S Otegui, Kevin W Eliceiri

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. 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

5 authors.

Jenu V ChackoCenter for Quantitative Cell Imaging, University of Wisconsin, Madison, WI 53706, USA.
Han Nim LeeCenter for Quantitative Cell Imaging, University of Wisconsin, Madison, WI 53706, USA.ORCID 0000-0002-0429-6297
Wenxin WuCenter for Quantitative Cell Imaging, University of Wisconsin, Madison, WI 53706, USA.
Marisa S OteguiCenter for Quantitative Cell Imaging, University of Wisconsin, Madison, WI 53706, USA.
Kevin W EliceiriCenter for Quantitative Cell Imaging, University of Wisconsin, Madison, WI 53706, USA.ORCID 0000-0001-8678-670X

Funding

NIH HHS NIH R01 CA238191U.S. Department of Energy DE-SC0019013
6 · The paper itself

Abstract

Fluorescence properties of a molecule can be used to study the structural and functional nature of biological processes. Physical properties, including fluorescence lifetime, emission spectrum, emission polarization, and others, help researchers probe a molecule, produce desired effects, and infer causes and consequences. Correlative imaging techniques such as hyperdimensional imaging microscopy (HDIM) combine the physical properties and biochemical states of a fluorophore. Here we present a fiber-based imaging system that can generate hyper-dimensional contrast by combining multiple fluorescence properties into a single fluorescence lifetime decay curve. Fluorescence lifetime imaging microscopy (FLIM) with controlled excitation polarization and temporally dispersed emission can generate a spectrally coded, polarization-filtered lifetime distribution for a pixel. This HDIM scheme generates a better contrast between different molecules than that from individual techniques. This setup uses only a single detector and is simpler to implement, modular, cost-efficient, and adaptable to any existing FLIM microscope. We present higher contrast data from

Indexed as

Fluorescent DyesOptical FibersMicroscopy, FluorescenceOptical ImagingFluorescent Dyesanisotropyanthocyanin imagingFLIMfluorescence imagingfluorescence lifetimehyper dimensional contrast imaginghyper dimensional imaginghyperspectral imagingpolarization

Identifiers

PMID33572130
PMCPMC7914562

What OpenQuestion holds

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LicenceCC BY
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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.