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Record W3013982119 · doi:10.1039/c9dt04804a

A versatile and multifunctional metal–organic framework nanocomposite toward chemo-photodynamic therapy

2020· article· en· W3013982119 on OpenAlexaff
Ying Pan, Zhidong Luo, Xiaoxiong Wang, Qianyi Chen, Junhao Chen, Yucheng Guan, Dong Liu, Hongjia Xu

Bibliographic record

VenueDalton Transactions · 2020
Typearticle
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsCentre for Drug Research and Development
FundersShenzhen Peacock PlanNational Natural Science Foundation of China
KeywordsNanocompositeMetal-organic frameworkPhotodynamic therapyMaterials scienceComponent (thermodynamics)NanotechnologyChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Previously most of the applications of targeting components have been based on the enhanced permeability and retention effect achieved using folic acid, which consider the side effects of the targeting components to some extent. Herein, we report a new strategy to decorate the surface of MOFs using a pemetrexed (MTA) targeting molecule, affording a new drug delivery system of ALA@UIO-66-NH-FAM/MTA (ALA = 5-amino-levulinic acid and FAM = 5-carboxyfluorescein). The confocal microscopy and flow cytometry results showed that ALA@UIO-66-NH-FAM/MTA presented a better targeting effect compared to ALA@UIO-66-NH-FAM/FA (FA = folic acid) and indicated a gradually increasing tendency of the targeting effect with the increasing expression of folate receptors on the tumor cell cytomembrane. Furthermore, the cytotoxicity experiment indicates that the combination of chemotherapy and photodynamic therapy is a more effective therapy model than single chemotherapy and photodynamic therapy. This work demonstrates the first attempt at folic acid antagonist (MTA) modification for NMOFs, providing a new concept for the design of MOFs with folate receptor targeting capacity for clinical applications.

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: none
Teacher disagreement score0.608
Threshold uncertainty score0.989

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 head0.014
GPT teacher head0.215
Teacher spread0.201 · 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

Citations75
Published2020
Admission routes1
Has abstractyes

Explore more

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