Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

AFM-Based Deep Learning Decodes Human Macrophage Mechanophenotypes.

Small methods - 1 Feb 2026

Chen Jiaxin, Wu Hao, Yang Wenjie, Li Haonan, Li Qi, Li Su, Liu Yingnan, Liu Fangfang, Xu Yunping, Chang Yan-Zhong, Himly Martin, Italiani Paola, Boraschi Diana, Zhang Guofang, Galluzzi Massimiliano, Li Yang

Abstract excerpt

Macrophage polarization into inflammatory (M1) and repairing/healing (M2) functional phenotypes are fundamental mechanisms in immune defensive responses, tissue repair, and disease control. Conventional phenotyping approaches based on molecular biomarkers are limited by destructive protocols, static endpoint analyses, and a disregard for the biomechanical attributes of cells. In this study, an integrated...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Deep Learning
Share to AFM gene

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Macrophage Activation

Share to Macrophages

Share to Microscopy, Atomic Force

Share to Phenotype

Source

Identifiers

PMID: 40722235
DOI: 10.1002/smtd.202500953

Genes

AFM

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

AFM-Based Deep Learning Decodes Human Macrophage Mechanophenotypes.PMID 40722235Read ↗

Multiplexed high-throughput immune cell imaging reveals molecular health-associated phenotypesSemantic proximity Read ↗

Multiplexed high-throughput immune cell imaging reveals molecular health-associated phenotypes.Semantic proximity Read ↗

Insights into the Identification of iPSC- and Monocyte-Derived Macrophage-Polarizing Compounds by AI-Fueled Cell Painting Analysis Tools.Semantic proximity Read ↗

DynaMorph: self-supervised learning of morphodynamic states of live cellsSemantic proximity Read ↗

Mapping human microglial morphological diversity via handcrafted and deep learning-derived image featuresSemantic proximity Read ↗

Spatial Mechanomics for Tissue-Scale Biomechanical Mapping and Multi-omics IntegrationSemantic proximity Read ↗

Label-free macrophage phenotype classification using machine learning methods.Semantic proximity Read ↗

Prediction of six macrophage phenotypes and their IL-10 content based on single-cell morphology using artificial intelligence.Semantic proximity Read ↗

Automated prediction of fibroblast phenotypes using mathematical descriptors of cellular features.Semantic proximity Read ↗

Label-free imaging of M1 and M2 macrophage phenotypes in the human dermis in vivo using two-photon excited FLIM.Semantic proximity Read ↗

Automated Prediction of Fibroblast Phenotypes Using Mathematical Descriptors of Cellular FeaturesSemantic proximity Read ↗

napariTFM: An Open-Source Tool for Traction Force Microscopy and Monolayer Stress MicroscopySemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.