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Record W3112212898 · doi:10.1039/d0md00317d

Normal breast epithelial MCF-10A cells to evaluate the safety of carbon dots

2020· article· en· W3112212898 on OpenAlexaff
Nuno Vale, Sara Silva, Diana Duarte, Diana M. A. Crista, Luís Pinto da Silva, Joaquim C. G. Esteves da Silva

Bibliographic record

VenueRSC Medicinal Chemistry · 2020
Typearticle
Languageen
FieldMaterials Science
TopicCarbon and Quantum Dots Applications
Canadian institutionsMcGill-Queen's University Press
FundersEuropean Regional Development FundFundação para a Ciência e a Tecnologia
KeywordsMCF-7Viability assayBiocompatibilityMaterials scienceNanotechnologyQuantum dotCytotoxicityToxicityBiophysicsChemistryCellCancer cellIn vitroMedicineHuman breastBiochemistryBiologyCancerInternal medicineOrganic chemistry

Abstract

fetched live from OpenAlex

three distinct bottom-up synthetic routes and their safety was tested against the normal breast cell line MCF-10A. The characterization results demonstrated a similar size range and composition for all the C-dots. The MCF-10A cells were treated with different concentrations of C-dots for 24, 48 and 72 h to evaluate the cell viability over time. For the 24 h incubation, there were no significant decreases in the viability of the MCF-10A cells. For the 48 h treatment, there was a significant decrease in the viability of the cells treated with calcination-based C-dots, but without significant cellular viability changes for microwave and hydrothermal-based C-dots. For 72 h, cells treated with hydrothermal-based C-dots have the most promising viability profile. Also, compared with paclitaxel, these C-dots have a safety profile very close to that of an antineoplastic in non-tumor cells. Our results suggest that these new C-dots have potential as imaging candidates or biosensing tools as well as drug carriers, and further investigation in animal models is needed for future application in medicine.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 integrity0.0010.000
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 head0.014
GPT teacher head0.252
Teacher spread0.237 · 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

Citations54
Published2020
Admission routes1
Has abstractyes

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