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Record W4412933326 · doi:10.1021/acsnano.5c02886

The Uptake of Metallic Nanoparticles in Breast Cancer Cell Lines is Modulated by the Hyaluronan-CD44 Axis

2025· article· en· W4412933326 on OpenAlexaff
Marie Hullo, Cécile Mathé, Núria Fonknechten, Céline Lacrouts, Guillaume Piton, Johanna Noireaux, Sylvie Chevillard, Anna Campalans, Emmanuelle Bourneuf

Bibliographic record

VenueACS Nano · 2025
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsEnvironment and Climate Change Canada
FundersUniversité Paris-SaclayCommissariat à l'Énergie Atomique et aux Énergies AlternativesMinistère de l'Education Nationale, de l'Enseignement Superieur et de la Recherche
KeywordsCD44Materials scienceNanoparticleBreast cancerMetalNanotechnologyCancer researchCancerCellOptoelectronicsNuclear magnetic resonanceMedicineChemistryInternal medicinePhysicsBiochemistryMetallurgy

Abstract

fetched live from OpenAlex

Radiation enhancement is a promising anticancer approach based on a local radiation dose increase due to the presence of metallic nanoparticles (NPs) within cancer cells. Depending on their composition, size, and cellular properties, NPs can follow multiple cellular pathways and entry routes. We observed that gold, platinum, and TiO 2 NPs are internalized at higher levels in mesenchymal cells compared to epithelial cells in breast cancer models. A global survey of gene expression between epithelial and mesenchymal cells exposed to 4 different NP types revealed an involvement of membrane structure, and further experiments confirmed that hyaluronic acid (HA) and its receptor CD44 are mediators of metallic NP uptake into cells. We extended our results to a larger panel of breast cancer cell lines and again showed a preferential uptake of all NPs tested in mesenchymal cells and relied on the HA/CD44 axis. These data provide considerations for the design of NP-based therapies targeting mesenchymal cancer cells, which are often resistant to treatment and correlate with poor prognosis and tumor recurrence.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.219

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.268
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

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