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Record W4406617825 · doi:10.1016/j.xcrp.2024.102399

An injectable hydrogel for synergistic therapy in colorectal cancer by targeting glutathione

2025· article· en· W4406617825 on OpenAlexaff
Jingqiu Zhou, Dongyu Jia, Lei Jin, Wanli He, Zhili Zhang, Zaishan Zhang, Tao Wu, Urs O. Häfeli, Xu Liu, Xue Han, Shibo Wei, Tianxing Gong

Bibliographic record

VenueCell Reports Physical Science · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSulfur Compounds in Biology
Canadian institutionsUniversity of British Columbia
FundersLiaoning Revitalization Talents ProgramKey Research Project of LiaoningChina Medical UniversityNatural Science Foundation of Liaoning Province
KeywordsColorectal cancerGlutathioneMedicineCancer therapyCancer researchCancerOncologyPharmacologyInternal medicineChemistryBiochemistry

Abstract

fetched live from OpenAlex

Colorectal cancer (CRC) remains one of the leading causes of cancer-related deaths, with chemoresistance driven by elevated intratumoral glutathione (GSH) levels posing a major challenge for effective treatment. Here we report a multi-enzyme-like hydrogel (MELH), composed of thiolated carboxymethyl cellulose (CMC-SH), designed to deplete GSH and enhance the efficacy of 5-fluorouracil (5-FU). MELH modulates the tumor microenvironment through multi-functional roles, including glucose depletion and reactive oxygen species (ROS) generation. In cell-derived and patient-derived xenograft models, MELH significantly reduces tumor growth, increases apoptosis, and exhibits synergistic anti-tumor effects without notable toxicity. These findings demonstrate that MELH is an effective adjuvant therapy to overcome chemoresistance and improve treatment outcomes for advanced CRC.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.007
GPT teacher head0.289
Teacher spread0.283 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2025
Admission routes1
Has abstractyes

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Same venueCell Reports Physical ScienceSame topicSulfur Compounds in BiologyFrench-language works237,207