Evidence map›Paper›PMID 37637472›Full record

ReviewFrontiers in neuroinformatics2023

Microglial morphometric analysis: so many options, so little consistency.

Jack Reddaway, Peter Eulalio Richardson, Ryan J Bevan, Jessica Stoneman, Marco Palombo

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in neuroinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.

0numbers the graph read from it
0cells of the map it votes in
68citing papers in PubMed
10.7field-weighted citation impact, top 1% 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

68 citing papers in PubMed, 87 citations in OpenAlex.

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  13. TLC-Derived High-Polar Fractions ofInternational journal of molecular sciences · 2026
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8 more citing papers are in PubMed but not listed here.

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 at 2 institutions in 1 country.

Jack ReddawayDivision of Neuroscience, School of Biosciences, Cardiff University, Cardiff, United Kingdom.
Peter Eulalio RichardsonDivision of Neuroscience, School of Biosciences, Cardiff University, Cardiff, United Kingdom.
Ryan J BevanUK Dementia Research Institute, Cardiff University, Cardiff, United Kingdom.
Jessica StonemanDivision of Neuroscience, School of Biosciences, Cardiff University, Cardiff, United Kingdom.
Marco PalomboCardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.
Cardiff University · GBCardiff Metropolitan University · GB

Funding

Medical Research Council MR/T020296/1
6 · The paper itself

Abstract

Quantification of microglial activation through morphometric analysis has long been a staple of the neuroimmunologist's toolkit. Microglial morphological phenomics can be conducted through either manual classification or constructing a digital skeleton and extracting morphometric data from it. Multiple open-access and paid software packages are available to generate these skeletons via semi-automated and/or fully automated methods with varying degrees of accuracy. Despite advancements in methods to generate morphometrics (quantitative measures of cellular morphology), there has been limited development of tools to analyze the datasets they generate, in particular those containing parameters from tens of thousands of cells analyzed by fully automated pipelines. In this review, we compare and critique the approaches using cluster analysis and machine learning driven predictive algorithms that have been developed to tackle these large datasets, and propose improvements for these methods. In particular, we highlight the need for a commitment to

Indexed as

cellular morphological changeshierarchical cluster analysismachine learningmicrogliamicroglia morphologyneuroimmune methods

Identifiers

PMID37637472
PMCPMC10448193
OpenAlexW4385724041

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.