MétaCan
Menu
Back to cohort
Record W3084014297 · doi:10.1016/j.heliyon.2020.e04862

The effect of PEGylated iron oxide nanoparticles on sheep ovarian tissue: An ex-vivo nanosafety study

2020· article· en· W3084014297 on OpenAlexaff
Sareh Karimi, Seyed Nasrollah Tabatabaei, Arno C. Gutleb, Marefat Ghaffari Novin, Alireza Ebrahimzadeh‐Bideskan, Zahra Shams Mofarahe

Bibliographic record

VenueHeliyon · 2020
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticles: synthesis and applications
Canadian institutionsUniversité de Montréal
FundersShahid Beheshti University of Medical Sciences
KeywordsOxidative stressOvarian CortexEx vivoCytotoxicityNanotechnologyNanomaterialsNanoparticleIron oxide nanoparticlesNanotoxicologyIn vivoMaterials scienceChemistryBiophysicsMedicineIn vitroOvarian tissueOvaryBiologyInternal medicineBiochemistryBiotechnology

Abstract

fetched live from OpenAlex

Today, nanotechnology plays an important role in our ever-continuous quest to improve the quality of human life. Because of their infinitesimal size, nanostructures can actively interact and alter cellular functions. Therefore, while the clinical benefits of nanotechnology may outweigh most of the associated risks, assessment of the cytotoxicity of nanostructures in respect to cells and tissues early in product development processes is of great significance. To the best of our knowledge, no such assessment has been performed for nanomaterials on the ovarian cortex before. Herein, silica-coated, PEGylated silica-coated, and uncoated iron oxide nanoparticles (IONP) with core diameter of 11 nm (±4.2 nm) were synthesized. The oxidative stress in cultured ovarian tissue exposed to the various IONP was subsequently assessed. The results indicate that among the four groups, uncoated IONP induce the most oxidative stress on the ovarian cortex while tissues treated with PEGylated IONP exhibit no significant change in oxidative stress.

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 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 score0.011
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.268
Teacher spread0.252 · 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

Citations15
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueHeliyonSame topicNanoparticles: synthesis and applicationsFrench-language works237,207