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Record W4308435289 · doi:10.1680/jbibn.21.00053

Apoptosis-based topotecan-loaded superparamagnetic drug delivery system: an in vitro study

2022· article· en· W4308435289 on OpenAlexaff
Niyousha Yazdanmehr, Maryam Tajabadi, Razieh Bigdeli, Hanif Goran Orimi, Vahid Asgary

Bibliographic record

VenueBioinspired Biomimetic and Nanobiomaterials · 2022
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicBioactive Compounds and Antitumor Agents
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsTopotecanApoptosisCytotoxicityAnnexinPropidium iodideDrug deliveryMaterials scienceSuperparamagnetismCancer researchNanobiotechnologyNanoparticleChemistryNanotechnologyIn vitroMedicineProgrammed cell deathBiochemistry

Abstract

fetched live from OpenAlex

Biological barriers could be overcome using nanobiotechnology, which promotes the development of nanomaterial-based delivery systems. The primary objective of the present investigation is superparamagnetic iron oxide nanoparticle (SPION) production for the delivery of topotecan to human breast cancer cells (MCF-7). The X-ray diffraction results confirmed the formation of pure SPIONs. The Fourier transform infrared spectra indicated the functional groups related to aminopropyl trimethoxy silane as a coating agent and topotecan. Topotecan-loaded magnetite nanoparticles with an IC 50 of approximately 156 μg/ml exhibited dose-dependent cytotoxicity. The polymerase chain reaction method also proved that in the mentioned cell line, topotecan-loaded SPIONs could increase the Bax/Bcl-2 ratio and p53 gene expression. An annexin V/propidium iodide detection assay was done to detect the induction of apoptosis. According to the results, the nanoparticles inhibit the survival of MCF-7 breast cancer cells by boosting apoptosis, which helps slow the growth of tumor cells.

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)0.001
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 score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.069
GPT teacher head0.354
Teacher spread0.284 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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