Evidence map›Paper›PMID 39527221›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2025

Comparative Automated Quantification of Tertiary Lymphoid Structures in Two Distinct Mouse Models of Inflammation.

Ana Houel, Clémence Riffard

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Ana HouelSorbonne University UMRS1135, Paris, France. houel@transgene.fr.
Clémence RiffardSorbonne University UMRS1135, Paris, France. clemence.riffard@inserm.fr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tertiary lymphoid structures (TLS) have been reported to form within nonlymphoid tissues upon various inflammatory conditions, such as tumors or bacterial infections. In particular, the lungs of patients with NSCLC or bacterial infections (such as tuberculosis or aspergillosis) are the sites of TLS neogenesis. An increasing number of preclinical models have been used over the years to recapitulate as accurately as possible the mechanisms and kinetics of TLS formation that are continuously reported in the clinic.We used herein two distinct murine models of pulmonary inflammation, relying on orthotopic lung tumor implantation, or intranasal administration of bacterial products, to generate TLS formation within mouse lung tissue. This review compares different workflows aiming at the detection and quantification of TLS using automated image analysis and artificial intelligence-assisted tissue classification. We also describe different methods for partitioning serial sections, depending on the tissue morphology and the experimental goal.

Indexed as

Disease Models, AnimalTertiary Lymphoid StructuresAnimalsHumansImage Processing, Computer-AssistedInflammationLungLung NeoplasmsMicePneumoniaArtificial intelligenceH&E stainingImage analysis softwareImmunohistochemistryInflammationLPSMouse modelMultiplex immunofluorescenceTC-1-luciferaseTertiary lymphoid structure

Identifiers

What OpenQuestion holds

Textmetadata
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.