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Record W4319831888 · doi:10.1002/cjce.24850

Glucosamine conjugated iron oxide and graphene oxide nanohybrid for smart drug delivery

2023· article· en· W4319831888 on OpenAlexaffvenue
Maedehsadat Mousavi, Zeinab Salehi, Alireza Rezvanpour, Mehdi Mosayebi, Masoumeh Farahani, Mohammad Asadi Tokmedash, Mohammad Ali Shokrgozar

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsCarleton University
Fundersnot available
KeywordsGrapheneDrug deliveryOxideConjugated systemNanoparticleMaterials scienceAdsorptionNanotechnologyDrugTargeted drug deliverySmart polymerChemistryChemical engineeringNuclear chemistryPolymerOrganic chemistryPharmacologyMedicine

Abstract

fetched live from OpenAlex

Abstract Smart drug delivery systems have attracted a lot of attention as one of the new treatment methods for cancer. In this study, a smart drug delivery system carrying anticancer drugs was obtained by the intelligent synthesis of glucosamine (GA)‐functionalized graphene oxide (GO)‐based iron oxide nanoparticles (Fe 3 O 4 @GO‐GA) using Hummers and chemical co‐precipitation processes. Nanohybrids have a high surface area (280.26 m 2 /g) and superparamagnetic behaviour (Ms = 26.017 emu/g), indicating a significant loading capacity (373.78 mg/mg) and efficiency (96.3%) for pharmaceutical loading. An adsorption study of conventional daunorubicin (DNR) on this carrier showed that the drug release is more prone to occur under acidic conditions (pH = 5.5), at moderately high temperatures ( T = 40°C), and in the absence of smart carriers. The toxicity of the smart nanohybrids was examined using the sulphorhodamine B (SRB) assay in Michigan Cancer Foundation‐7 (MCF‐7) cell lines. The rate of death of cells exposed to smart drug‐containing systems in comparison to the systems without GA shows that GA reduces the toxicity of Fe 3 O 4 @GO.

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 score0.018
Threshold uncertainty score0.488

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 head0.007
GPT teacher head0.175
Teacher spread0.169 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

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