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Record W4416094229 · doi:10.1002/adfm.202520463

A Single‐Metal‐Doped Nanoplatform for Ferroptosis‐Driven cGAS‐STING Pathway Activation in Hepatocellular Carcinoma Immunotherapy

2025· article· en· W4416094229 on OpenAlexaff
Yuchen Zhang, Shuang Feng, Jie Luo, Kaige Xu, Junfeng Guo, Mengxue Yuan, Qiang Luo, Yu Huang, Chaoqiang Fan, Kibret Mequanint, Donghui Zhu, Malcolm Xing, Shiming Yang

Bibliographic record

VenueAdvanced Functional Materials · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
Topicinterferon and immune responses
Canadian institutionsWestern UniversityUniversity of Manitoba
Fundersnot available
KeywordsTumor microenvironmentImmune systemHepatocellular carcinomaImmunotherapyInnate immune systemReactive oxygen speciesSignal transduction

Abstract

fetched live from OpenAlex

Abstract The cGAS‐STING signaling pathway effectively activates antitumor immune responses and holds promise for overcoming drug resistance in hepatocellular carcinoma (HCC) immunotherapy. However, achieving specific activation of this pathway in HCC remains challenging. Here, it is introduced a single‐metal‐doped nanoplatform, ZMRPF, which leverages ferroptosis‐induced mitochondrial DNA (mtDNA) release to stimulate cGAS‐STING‐mediated immune activation. ZMRPF initiates HCC ferroptosis by inducing high levels of lipid reactive oxygen species, leading to mitochondrial stress and the release of endogenous mtDNA. This mtDNA synergistically activates the cGAS‐STING pathway, enhanced by immunoactivating Mn 2 ⁺ ions released from ZMRPF. Concurrently, the tumor antigens released during ferroptosis amplify the activity of antigen‐presenting cells, creating a cascade that links ferroptosis with innate immunity. This cascade drives a robust systemic antitumor immune response, effectively reversing the immunosuppressive microenvironment of HCC. These results demonstrate the ability of ZMRPF to reshape the immune microenvironment of HCC and offer a promising strategy for next‐generation tumor immunotherapy.

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 score0.012
Threshold uncertainty score0.922

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)0.0010.000

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 head0.021
GPT teacher head0.243
Teacher spread0.222 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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