MétaCan
Menu
Back to cohort
Record W4246340264 · doi:10.1002/ange.202012956

Molecularly Imprinted Polymer‐Based Smart Prodrug Delivery System for Specific Targeting, Prolonged Retention, and Tumor Microenvironment‐Triggered Release

2020· article· en· W4246340264 on OpenAlexaff
Zikuan Gu, Yueru Dong, Shuxin Xu, Lisheng Wang, Zhen Liu

Bibliographic record

VenueAngewandte Chemie · 2020
Typearticle
Languageen
FieldChemistry
TopicAnalytical chemistry methods development
Canadian institutionsUniversity of Ottawa
FundersNanjing UniversityMinistry of Science and Technology of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsProdrugDrug deliveryChemistryTumor microenvironmentTargeted drug deliveryPharmacologyCancer researchMedicineBiochemistryTumor cellsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Prodrug and drug delivery systems are two effective strategies for improving the selectivity of chemotherapeutics. Molecularly imprinted polymers (MIPs) have emerged as promising carriers in targeted drug delivery for cancer treatment, but they have not yet been integrated with the prodrug strategy. Reported here is an MIP‐based smart prodrug delivery system for specific targeting, prolonged retention time, and tumor microenvironment‐triggered release. 5′‐Deoxy‐5‐fluorocytidine (DFCR) and sialic acid (SA) were used as a prodrug and a marker for tumor targeting, respectively. Their co‐imprinted nanoparticles were prepared as a smart carrier. Prodrug‐loaded MIP specifically and sustainably accumulated at the tumor site and then gradually released. Unlike conventional prodrug designs, which often require in‐liver bioconversion, this MIP‐based prodrug delivery is liver‐independent but tumor‐dependent. Thus, this study opens new access to the development of smart prodrug delivery nanoplatforms.

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 categoriesMeta-epidemiology (narrow)
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 score0.015
Threshold uncertainty score1.000

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 head0.021
GPT teacher head0.220
Teacher spread0.199 · 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.

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

Citations37
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueAngewandte ChemieSame topicAnalytical chemistry methods developmentFrench-language works237,207