Evidence map›Paper›PMID 36278016›Full record

ArticleFrontiers in neuroscience2022

Adaptive optics in single objective inclined light sheet microscopy enables three-dimensional localization microscopy in adult

Shih-Te Hung, Arnau Llobet Rosell, Daphne Jurriens, Marijn Siemons, Oleg Soloviev, Lukas C Kapitein, Kristin Grußmayer, Lukas J Neukomm, Michel Verhaegen, Carlas Smith

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In one paragraph

Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Adaptive optics in an oblique plane microscope.Biomedical optics express · 2024
    Article
  3. Adaptive Optics in an Oblique Plane Microscope.bioRxiv : the preprint server for biology · 2024
    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

10 authors.

Shih-Te HungDelft Center for Systems and Control, Delft University of Technology, Delft, Netherlands.
Arnau Llobet RosellDepartment of Fundamental Neurosciences, University of Lausanne, Lausanne, Switzerland.
Daphne JurriensCell Biology, Neurobiology and Biophysics, Department of Biology, Faculty of Science, Utrecht University, Utrecht, Netherlands.
Marijn SiemonsCell Biology, Neurobiology and Biophysics, Department of Biology, Faculty of Science, Utrecht University, Utrecht, Netherlands.
Oleg SolovievDelft Center for Systems and Control, Delft University of Technology, Delft, Netherlands.
Lukas C KapiteinCell Biology, Neurobiology and Biophysics, Department of Biology, Faculty of Science, Utrecht University, Utrecht, Netherlands.
Kristin GrußmayerDepartment of Bionanoscience, Kavli Institute of Nanoscience Delft, Delft University of Technology, Delft, Netherlands.
Lukas J NeukommDepartment of Fundamental Neurosciences, University of Lausanne, Lausanne, Switzerland.
Michel VerhaegenDelft Center for Systems and Control, Delft University of Technology, Delft, Netherlands.
Carlas SmithDelft Center for Systems and Control, Delft University of Technology, Delft, Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Single-molecule localization microscopy (SMLM) enables the high-resolution visualization of organelle structures and the precise localization of individual proteins. However, the expected resolution is not achieved in tissue as the imaging conditions deteriorate. Sample-induced aberrations distort the point spread function (PSF), and high background fluorescence decreases the localization precision. Here, we synergistically combine sensorless adaptive optics (AO),

Indexed as

adaptive opticsbrainDrosophilalocalization microscopySuper-resolution Microscopy

Identifiers

PMID36278016
PMCPMC9583434

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Registered trials

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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.