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Record W2073066818 · doi:10.1093/nar/gkn967

Phototoxic aptamers selectively enter and kill epithelial cancer cells

2008· article· en· W2073066818 on OpenAlexafffund
C. S. M. Ferreira, Melissa Cheung, Sotiris Missailidis, Stuart K. Bisland, Jean Gariépy

Bibliographic record

VenueNucleic Acids Research · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity Health NetworkOntario Institute for Cancer Research
FundersCanadian Breast Cancer Research Alliance
KeywordsAptamerBiologyCytotoxic T cellCancer cellOligonucleotideCancer researchEndosomeCancerCell biologyIntracellularMolecular biologyBiochemistryDNAIn vitro

Abstract

fetched live from OpenAlex

The majority of cancers arise from malignant epithelial cells. We report the design of synthetic oligonucleotides (aptamers) that are only internalized by epithelial cancer cells and can be precisely activated by light to kill such cells. Specifically, phototoxic DNA aptamers were selected to bind to unique short O-glycan-peptide signatures on the surface of breast, colon, lung, ovarian and pancreatic cancer cells. These surface antigens are not present on normal epithelial cells but are internalized and routed through endosomal and Golgi compartments by cancer cells, thus providing a focused mechanism for their intracellular delivery. When modified at their 5' end with the photodynamic therapy agent chlorin e(6) and delivered to epithelial cancer cells, these aptamers exhibited a remarkable enhancement (>500-fold increase) in toxicity upon light activation, compared to the drug alone and were not cytotoxic towards cell types lacking such O-glycan-peptide markers. Our findings suggest that these synthetic oligonucleotide aptamers can serve as delivery vehicles in precisely routing cytotoxic cargoes to and into epithelial 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 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 score0.001
Threshold uncertainty score0.002

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 integrity0.0000.000
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 head0.031
GPT teacher head0.339
Teacher spread0.309 · 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

Citations211
Published2008
Admission routes2
Has abstractyes

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