Evidence map›Paper›PMID 40792460›Full record

ArticleNeuropathology and applied neurobiology2025

Standardised TruAI Automated Quantification of Intracellular Neuromelanin Granules in Human Brain Tissue Sections.

Anastasia Filimontseva, Thais Cuadros, Zac Chatterton, Amy Burke, Anahid Ansari Mahabadian, Joan Compte, YuHong Fu, Miquel Vila, Glenda M Halliday

Abstract read
In one paragraph

Article in Neuropathology and applied neurobiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

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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.

Anastasia FilimontsevaThe University of Sydney Brain and Mind Centre & Faculty of Medicine and Health, School of Medical Sciences, Sydney, NSW, Australia.ORCID 0000-0002-3316-9035
Thais CuadrosAligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, Maryland, USA.ORCID 0000-0001-7376-7470
Zac ChattertonThe University of Sydney Brain and Mind Centre & Faculty of Medicine and Health, School of Medical Sciences, Sydney, NSW, Australia.ORCID 0000-0002-6683-1400
Amy BurkeThe University of Sydney Brain and Mind Centre & Faculty of Medicine and Health, School of Medical Sciences, Sydney, NSW, Australia.ORCID 0009-0006-9274-9589
Anahid Ansari MahabadianThe University of Sydney Brain and Mind Centre & Faculty of Medicine and Health, School of Medical Sciences, Sydney, NSW, Australia.ORCID 0009-0000-0088-0544
Joan CompteNeurodegenerative Diseases Research Group, Vall d'Hebron Research Institute (VHIR)-Network Center for Biomedical Research in Neurodegenerative Diseases (CIBERNED), Barcelona, Spain.ORCID 0000-0001-5447-5122
YuHong FuThe University of Sydney Brain and Mind Centre & Faculty of Medicine and Health, School of Medical Sciences, Sydney, NSW, Australia.ORCID 0000-0003-4539-2039
Miquel VilaAligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, Maryland, USA.ORCID 0000-0002-1352-989X
Glenda M HallidayThe University of Sydney Brain and Mind Centre & Faculty of Medicine and Health, School of Medical Sciences, Sydney, NSW, Australia.ORCID 0000-0003-0422-8398

Funding

Aligning Science Across Parkinson's 020505National Health and Medical Research Council 1176607
6 · The paper itself

Abstract

aimsTo standardise and automate the quantitation of human-unique neuromelanin granules in catecholamine neurons in post-mortem tissue sections from healthy individuals at different ages to understand any changes in these granules with age.

methodsFive- to 6-μm-thick fixed and paraffin-embedded transverse midbrain tissue sections were supplied from 47 cases from three brain banks following ethical approvals. Sections were prepared and automated digital images acquired. Standardisation and automation of the quantification of neuromelanin granules was performed using the TruAI feature of the Olympus VS200 desktop platform. Comparisons between stained and unstained sections as well as correlations with age were performed.

resultsThe automated platform reliably identified both stained and unstained intracellular neuromelanin granules and extracellular pigments, showing high reproducibility in measurements across laboratories using different tissue processing methods. Extraneuronal pigments were significantly smaller than intracellular neuromelanin granules. Sections processed for haematoxylin and eosin staining impacted the size and colour of both neuromelanin and the neurons containing neuromelanin. Haematoxylin made neuromelanin bluer, and the increased tissue processing made the intracellular area occupied by neuromelanin smaller in younger people. There was an increase in neuromelanin optical density and colour change (browner) with age.

conclusionsThe TruAI automated platform reliably quantifies individual neuromelanin granules in catecholamine neurons. Extraneuronal pigments are considerably smaller in size than intracellular neuromelanin, and intracellular neuromelanin changes its properties with age. The darkening and colour change of intracellular neuromelanin suggest an increase in eumelanin over time in healthy individuals. These changes can be reliably identified using the automated platform.

Indexed as

BrainCytoplasmic GranulesImage Processing, Computer-AssistedMelaninsNeuronsAdolescentAdultAgedAged, 80 and overChildFemaleHumansMaleMiddle AgedYoung AdultMelaninsneuromelaninartificial intelligence automationcolour identificationcomputer‐assisted quantitationextracellular neuromelaninintracellular neuromelaninoptical density

Identifiers

PMID40792460
PMCPMC12340752

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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.