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Record W4409528838 · doi:10.1016/j.isci.2025.112448

Inflammatory targeted nanoplatform incorporated with antioxidative nano iron oxide to attenuate ulcerative colitis progression

2025· article· en· W4409528838 on OpenAlexaff
Haojun Chen, Wei Sun, Can Li, Qiuyang Wang, Xucai Wang, Yingjie Du, Wenbo Chen, Min Wang, Caoxing Huang, Rong Wang

Bibliographic record

VenueiScience · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsUniversity of British Columbia
FundersNational Natural Science Foundation of China
KeywordsUlcerative colitisNano-ChemistryMedicinePharmacologyNanotechnologyInternal medicineMaterials scienceDisease

Abstract

fetched live from OpenAlex

Antioxidative nanomaterials with reactive oxygen species (ROS) scavenging capabilities hold promise for the treatment of ulcerative colitis (UC). However, their clinical application is limited by rapid diffusion, susceptibility to inactivation, and insufficient targeting of inflammatory sites. This study focuses on developing a nanoplatform by integrating iron oxide nanoparticles (IONPs) into zeolitic imidazolate frameworks-8 (ZIF-8), termed as ZIF-8@IONPs. ZIF-8@IONPs exhibited good biocompatibility and effective ROS scavenging capabilities in RAW 264.7 cells. To enhance inflammatory targeting, HA@ZIF-8@IONPs were generated through hyaluronic acid (HA) surface modification. HA@ZIF-8@IONPs effectively reduced damage to intestinal tissues in the UC mouse model. Mechanistic revealed that HA@ZIF-8@IONPs exhibited antioxidant and anti-inflammatory activities by eliminating endogenous ROS, activating the Nrf2 signaling pathway, and inhibiting the NF-κB signaling pathway. This study highlights the nanoplatform's potential as a promising candidate for UC treatment due to its great targeting of inflammatory microenvironments and efficient ROS scavenging.

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 score0.340
Threshold uncertainty score0.703

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 head0.006
GPT teacher head0.250
Teacher spread0.244 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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