Evidence map›Paper›PMID 33935625›Full record

ArticleFrontiers in neuroscience2021

Automated Quantitative Analysis of

Michael Nesbit, John C Mamo, Maimuna Majimbi, Virginie Lam, Ryusuke Takechi

Abstract read
In one paragraph

Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Trial
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  10. Frontiers in endocrinology · 2023
    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

5 authors.

Michael NesbitFaculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Perth, WA, Australia.
John C MamoFaculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Perth, WA, Australia.
Maimuna MajimbiFaculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Perth, WA, Australia.
Virginie LamFaculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Perth, WA, Australia.
Ryusuke TakechiFaculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Perth, WA, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAn increase in blood brain barrier permeability commonly precedes neuro-inflammation and cognitive impairment in models of dementia. Common methods to estimate capillary permeability have potential confounders, or require laborious and subjective semi-manual analysis. NEW

methodHere we used snap frozen mouse and rat brain sections that were double-immunofluorescent labeled for immunoglobulin G (IgG; plasma protein) and laminin-α4 (capillary basement membrane). A Machine Learning Image Analysis program (Zeiss ZEN Intellisis) was trained to recognize and segment laminin-α4 to equivocally identify blood vessels in large sets of images. An IgG subclass based on a threshold intensity was segmented and quantitated only in extravascular regions. The residual parenchymal IgG fluorescence is indicative of blood-to-brain extravasation of IgG and was accurately quantitated.

resultsAutomated machine-learning and threshold based segmentation of only parenchymal IgG extravasation accentuates otherwise indistinct capillary permeability, particularly frequent in minor BBB leakage. Comparison with Existing Methods: Large datasets can be processed and analyzed quickly and robustly to provide an overview of vascular permeability throughout the brain. All human bias or ambiguity involved in classifying and measuring leakage is removed.

conclusionHere we describe a fast and precise method of visualizing and quantitating BBB permeability in mouse and rat brain tissue, while avoiding the confounding influence of unphysiological conditions such as perfusion and eliminating any human related bias from analysis.

Indexed as

blood-brain barrierIgG extravasationimmunofluorescenceIntellisislaminin-α4machine-learningquantitationsegmentation

Identifiers

PMID33935625
PMCPMC8086794

What Socratic 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 Socratic graph.