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Record W2951582266 · doi:10.3390/bioengineering6020052

A Multidisciplinary Approach toward High Throughput Label-Free Cytotoxicity Monitoring of Superparamagnetic Iron Oxide Nanoparticles

2019· article· en· W2951582266 on OpenAlexaff
Sonia Tan, Georg Zoidl, Ebrahim Ghafar‐Zadeh

Bibliographic record

VenueBioengineering · 2019
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsYork University
Fundersnot available
KeywordsViability assayCytotoxicityCell growthChemistryNanoparticleCellNanotechnologyIn vitroBiophysicsBiochemistryMaterials scienceBiology

Abstract

fetched live from OpenAlex

This paper focuses on cytotoxicity examination of superparamagnetic iron oxide nanoparticles (SPIONs) using different methods, including impedance spectroscopy. Recent advances of SPIONs for clinical and research applications have triggered the need to understand their effects in cells. Despite the great advances in adapting various biological and chemical methods to assess in-vitro toxicity of SPIONs, less attention has been paid on the development of a high throughput label-free screening platform to study the interaction between the cells and nanoparticles including SPIONs. In this paper, we have taken the first step toward this goal by proposing a label-free impedimetric method for monitoring living cells treated with SPIONs. We demonstrate the effect of SPIONs on the adhesion, growth, proliferation, and viability of neuroblastoma 2A (N2a) cells using impedance spectroscopy as a label-free method, along with other standard microscopic and cell viability testing methods as control methods. Our results have shown a decreased viability of the cells as the concentration of SPIONs increases with percentages of 59%, 47%, and 40% for 100 µg/mL (C4), 200 µg/mL (C5), 300 µg/mL (C6), respectively. Although all SPIONs concentrations have allowed the growth of cells within 72 hours, C4, C5, and C6 showed slower growth compared to the control (C1). The growth and proliferation of N2a cells are faster in the absence or low concentration of SPIONS. The percent coefficient of variation (% CV) was used to compare cell concentrations obtained by TBDE assay and a Scepter cell counter. Results also showed that the lower the SPIONs concentration, the lower the impedance is expected to be in the sensing electrodes without the cells. Meanwhile, the variation of surface area (∆S) was affected by the concentration of SPIONs. It was observed that the double layer capacitance was almost constant because of the higher attachment of cells, the lower surface area coated by SPIONs. In conclusion, impedance changes of electrodes exposed to the mixture of cells and SPIONs offer a wide dynamic range (>1 MΩ using Electric Cell-substrate Impedance electrodes) suitable for cytotoxicity studies. Based on impedance based, viability testing and microscopic methods' results, SPIONs concentrations higher than 100 ug/mL and 300 ug/mL cause minor and major effects, respectively. We propose that a high throughput impedance-based label-free platform provides great advantages for studying SPIONs in a cell-based context, opening a window of opportunity to design and test the next generation of SPIONs with reduced toxicity for biomedical or medical 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.017
GPT teacher head0.211
Teacher spread0.193 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2019
Admission routes1
Has abstractyes

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