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Record W2907841305 · doi:10.5267/j.ccl.2018.12.004

A theoretical survey of the ability of nanocarbon layers to deliver anti-cancer drug temozolomide to the target cancer cells

2018· article· en· W2907841305 on OpenAlexvenueno aff
Saba Hadidi, Farshad Shiri, Mohammadsaleh Norouzibazaz

Bibliographic record

VenueCurrent Chemistry Letters · 2018
Typearticle
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryCancerTemozolomideDrugCancer cellCancer researchPharmacologyCancer drugsAnticancer drugNanotechnologyInternal medicineGlioblastoma

Abstract

fetched live from OpenAlex

Density functional theory with the basis set of 6-31+G(d) was used to investigate on the carbon nano layers, C(sp2), potential as a drug delivery system for transferring of anti-cancer drug temozolomide to the target tissue. In order to elucidate the possibility of drug transmission by utilizing a carrier, the mechanisms of direct drug degradation, and loaded drug on the carrier are analyzed and examined completely. Two possible and different pathways for direct drug hydrolysis have been considered. According to obtained results activation barriers of these two pathways are 62.17 and 72.10 kcal mol -1 , and 64.30 and 70.10 kcal mol -1 for two gas mode and also two aqueous solvent conditions respectively. By comparison of outcomes, it can be found out that these activation barriers for both degradation pathways are significantly greater than the activation barriers for drug separation from the surface of carbon carrier (18.59 and 51.92 kcal mol -1 for gas mode and 11.79 and 44.67 kcal mol -1 for aqueous solvent). Therefore, by studying the achieved outcomes, it can be deduced that separation and releasing of the drug from the carrier occurs faster kinetically than direct degradation of temozolomide, so the drug can reach to the target before direct decomposition.

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 score0.008
Threshold uncertainty score0.612

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 science0.0010.000
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 head0.010
GPT teacher head0.242
Teacher spread0.232 · 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

Citations8
Published2018
Admission routes1
Has abstractyes

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