Evidence map›Paper›PMID 35299715›Full record

ArticleBiophysical reports2022

An economic, square-shaped flat-field illumination module for TIRF-based super-resolution microscopy.

Jeff Y L Lam, Yunzhao Wu, Eleni Dimou, Ziwei Zhang, Matthew R Cheetham, Markus Körbel, Zengjie Xia, David Klenerman, John S H Danial

Abstract read
In one paragraph

Article in Biophysical reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
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  4. Lecanemab preferentially binds to smaller aggregates present at early Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
    Article
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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

9 authors.

Jeff Y L LamYusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom.
Yunzhao WuYusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom.
Eleni DimouYusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom.
Ziwei ZhangYusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom.
Matthew R CheethamYusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom.
Markus KörbelYusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom.
Zengjie XiaYusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom.
David KlenermanYusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom.
John S H DanialYusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Super-resolution microscopy allows complex biological assemblies to be observed with remarkable resolution. However, the presence of uneven Gaussian-shaped illumination hinders its use in quantitative imaging or high-throughput assays. Methods developed to circumvent this problem are often expensive, hard to implement, or not applicable to total internal reflection fluorescence imaging. We herein demonstrate a cost-effective method to overcome these challenges using a small square-core multimodal optical fiber as the coupler. We characterize our method with synthetic, recombinant, and cellular systems imaged under total internal reflection fluorescence and highly inclined and laminated optical sheet illuminations to demonstrate its ability to produce highly uniform images under all conditions.

Identifiers

PMID35299715
PMCPMC8914601

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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.