Optimizing Colocalized Cell Counting Using Automated and Semiautomated Methods.

Hasita V Nalluri, Shantelle A Graff, Dragan Maric, John D Heiss

Abstract read

In one paragraph

Article in Neuroinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Hasita V NalluriSurgical Neurology Branch, Flow and Imaging Cytometry Core Facility, Bethesda, MD, USA.

Shantelle A GraffSurgical Neurology Branch, Flow and Imaging Cytometry Core Facility, Bethesda, MD, USA. shantelle.graff@nih.gov.

Dragan MaricSurgical Neurology Branch, Disorders and Stroke, National Institute of Neurological, National Institutes of Health, Bethesda, MD, USA.

John D HeissSurgical Neurology Branch, Disorders and Stroke, National Institute of Neurological, National Institutes of Health, Bethesda, MD, USA.

Funding

Molecular Pathogenesis of NeoplasiaZIANS003052 · NINDS · NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE · PI HEISS, JOHN · 2009 to 2025

$20.7M

Intramural NIH HHS ZIA NS003052NHLBI NIH HHS NIH ZIANS003052-15

6 · The paper itself

Abstract

Inflammation within the spinal subarachnoid space leads to arachnoid hypercellularity. Multiplex immunohistochemistry (MP-IHC) enables the quantification of immune cells to assess arachnoid inflammation, but manual counting is time-consuming, impractical for large datasets, and prone to operator bias. Although automated colocalization methods exist, many clinicians prefer manual counting due to challenges with diverse cell morphologies and imperfect colocalization. Object-based colocalization analysis (OBCA) tools address these issues, improving accuracy and efficiency. We evaluated semi-automated and automated OBCA techniques for quantifying colocalized immune cells in human arachnoid tissue sections. Both methods demonstrated sufficient reliability across morphologies (P < 0.0001). While automated counts differed significantly from manual counts, their strong correlation (R

Indexed as

ArachnoidImage Processing, Computer-AssistedCell CountHumansImmunohistochemistryReproducibility of ResultsColocalizationQuantificationTissue

Identifiers

PMID40113626

DOI10.1016/j.proghi.2009.03.001

PMCPMC11926031

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.