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Record W2801778180 · doi:10.1016/j.omtn.2018.04.004

Antibody-Antisense Oligonucleotide Conjugate Downregulates a Key Gene in Glioblastoma Stem Cells

2018· article· en· W2801778180 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMolecular Therapy — Nucleic Acids · 2018
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsMontreal Neurological Institute and HospitalMcGill UniversityUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsConjugateGlioblastomaOligonucleotideAntibodyStem cellGeneCancer researchMolecular biologyChemistryBiologyCell biologyImmunologyBiochemistryMathematics

Abstract

fetched live from OpenAlex

Glioblastoma stem cells (GSCs) are invasive, treatment-resistant brain cancer cells that express downregulated in renal cell carcinoma (DRR), also called FAM107A, a genetic driver of GSC invasion. We developed antibody-antisense oligonucleotide (AON) conjugates to target and reduce DRR/FAM107A expression. Specifically, we used antibodies against antigens expressed on the GSCs, such as CD44 and EphA2, conjugated to chemically modified AONs against DRR/FAM107A, which were designed as chimeras of DNA and 2'-deoxy-2'-fluoro-beta-D-arabinonucleic acid (FANA) for increased nuclease stability and mRNA affinity. We demonstrate that these therapeutic conjugates successfully internalize, accumulate, and reduce DRR/FAM107A expression in patient-derived GSCs. This is the first example of an antibody-antisense strategy against cancer stem 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.

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 categoriesMeta-epidemiology (narrow)
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 score0.047
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.011
GPT teacher head0.264
Teacher spread0.253 · 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