Evidence map›Paper›PMID 25271959›Full record

ReviewCurrent protocols in cytometry2014

Correlative fluorescence and electron microscopy.

Randall T Schirra, Peijun Zhang

Open access · greenAbstract readComparative StudyReview
In one paragraph

Review in Current protocols in cytometry, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.2field-weighted citation impact, top 15% 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

5 citing papers in PubMed, 10 citations in OpenAlex.

  1. Review
  2. Article
  3. DEEP LEARNING BASED SUPERVISED SEMANTIC SEGMENTATION OF ELECTRON CRYO-SUBTOMOGRAMS.Proceedings. International Conference on Image Processing · 2018
    Article
  4. Article
  5. Review
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

2 authors at 1 institution in 1 country.

Randall T SchirraDepartment of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
Peijun Zhang
University of Pittsburgh · US

Funding

X-Ray Core P50GM082251 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI CALERO, GUILLERMO ALBERTO · 2007 to 2018
$48.1M
Structure and function of membrane receptor signaling complex in bacterial chemotR01GM085043 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI ZHANG, PEIJUN · 2009 to 2013
$1.4M
NIGMS NIH HHS GM082251NIGMS NIH HHS GM085043NIGMS NIH HHS P50 GM082251NIGMS NIH HHS R01 GM085043
6 · The paper itself

Abstract

Correlative fluorescence and electron microscopy (CFEM) is a multimodal technique that combines dynamic and localization information from fluorescence methods with ultrastructural data from electron microscopy, to give new information about how cellular components change relative to the spatiotemporal dynamics within their environment. In this review, we will discuss some of the basic techniques and tools of the trade for utilizing this attractive research method, which is becoming a very powerful tool for biology labs. The information obtained from correlative methods has proven to be invaluable in creating consensus between the two types of microscopy, extending the capability of each, and cutting the time and expense associated with using each method separately for comparative analysis. The realization of the advantages of these methods in cell biology has led to rapid improvement in the protocols and has ushered in a new generation of instruments to reach the next level of correlation--integration.

Indexed as

AnimalsHumansMicroscopy, ElectronMicroscopy, Fluorescencecell biologycorrelativecryoEMelectron microscopyfluorescenceimagingimmunolabelintegrated microscopelight microscopytomography

Identifiers

PMID25271959
PMCPMC4211606
OpenAlexW1585855093

What OpenQuestion holds

Textmetadata
LicenceTDM
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.