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Record W2285724633 · doi:10.5772/62337

Gold Nanoparticles Impinge on Nucleoli and the Stress Response in MCF7 Breast Cancer Cells

2016· review· en· W2285724633 on OpenAlexafffund
Mohamed Kodiha, Hicham Mahboubi, Dušica Maysinger, Ursula Stochaj

Bibliographic record

VenueNanobiomedicine · 2016
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNucleolusColloidal goldCell biologyRibosomeCancer cellRibosome biogenesisOrganelleChemistryNucleusRibosomal proteinBiologyBiophysicsRNANanoparticleCancerNanotechnologyMaterials scienceBiochemistryGeneGenetics

Abstract

fetched live from OpenAlex

Cancer cells can take up gold nanoparticles of different morphologies. These particles interact with the plasma membrane and often travel to intracellular organelles. Among organelles, the nucleus is especially susceptible to the damage that is inflicted by gold nanoparticles. Located inside the nucleus, nucleoli are specialized compartments that transcribe ribosomal RNA genes, produce ribosomes and function as cellular stress sensors. Nucleoli are particularly prone to gold nanoparticle-induced injury. As such, small spherical gold nanoparticles and gold nanoflowers interfere with the transcription of ribosomal DNA. However, the underlying mechanisms are not fully understood. In this study, we examined the effects of gold nanoparticles on nucleolar proteins that are critical to ribosome biogenesis and other cellular functions. We show that B23/nucleophosmin, a nucleolar protein that is tightly linked to cancer, is significantly affected by gold nanoparticles. Furthermore, gold nanoparticles impinge on the cellular stress response, as they reduce the abundance of the molecular chaperone hsp70 and O-GlcNAc modified proteins in the nucleus and nucleoli. Together, our studies set the stage for the development of nanomedicines that target the nucleolus to eradicate proliferating cancer 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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score0.534

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.313
Teacher spread0.297 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations42
Published2016
Admission routes2
Has abstractyes

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