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Record W2015354352 · doi:10.1586/14737140.5.6.1001

Antisense oligodeoxynucleotide therapy for bladder cancer: recent advances and future prospects

2005· review· en· W2015354352 on OpenAlexaff
Hideaki Miyake, Isao Hara, Masato Fujisaw, Martin Gleave

Bibliographic record

VenueExpert Review of Anticancer Therapy · 2005
Typereview
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineBladder cancerAntisense therapyGenetic enhancementCancer researchCancerRadiation therapyCancer therapyTargeted therapyChemotherapeutic drugsInternal medicineGeneBiologyOligonucleotide

Abstract

fetched live from OpenAlex

Despite remarkable progress in therapeutic options for the management of bladder cancer, it remains a challenge for urologists to achieve successful outcomes in the treatment of both superficial and invasive bladder cancers. In this review, recent advances in the field of antisense oligodeoxynucleotide therapy targeting several genes playing functionally important roles in the progression and recurrence of bladder cancer are summarized. Data showing the synergistic antitumor activities of antisense oligodeoxynucleotide therapy, combined with several treatments, including cytotoxic chemotherapy, radiation and other molecular targeting therapies, are also presented. Finally, the future direction of antisense oligodeoxynucleotide therapy in the therapeutic strategy of bladder cancer is discussed. These findings may help clarify the significance of antisense oligodeoxynucleotide therapy as an attractive alternative to conventional strategies.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.044
GPT teacher head0.416
Teacher spread0.372 · 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 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

Citations9
Published2005
Admission routes1
Has abstractyes

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