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Record W2809196412 · doi:10.1089/hum.2018.026

The Oncolytic Adenovirus XVir-N-31 as a Novel Therapy in Muscle-Invasive Bladder Cancer

2018· article· en· W2809196412 on OpenAlexaff
Eva Lichtenegger, Florestan Koll, Helena Haas, Klaus Mantwill, Klaus‐Peter Janssen, Melanie Laschinger, Jürgen E. Gschwend, Katja Steiger, Peter C. Black, Igor Moskalev, Roman Nawroth, Per Sonne Holm

Bibliographic record

VenueHuman Gene Therapy · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOncolytic virusBladder cancerOncolytic adenovirusCancer researchMedicineCancerCancer cellProgrammed cell deathPathologyOncologyApoptosisInternal medicineBiology

Abstract

fetched live from OpenAlex

Muscle-invasive bladder cancer represents approximately 25% of diagnosed bladder cancer cases and carries a significant risk of death. Oncolytic viruses are novel antitumor agents with the ability to selectively replicate and lyse tumor cells while sparing healthy tissue. We explored the efficiency of the oncolytic YB-1-selective adenovirus XVir-N-31 in vitro and in an orthotopic mouse model for bladder cancer by intramural injection under ultrasound guidance. We demonstrated that XVir-N-31 replicated in bladder cancer cells and induced a stronger immunogenic cell death than wild-type adenovirus by facilitating enhanced release of HMGB1 and exosomal Hsp70. The intratumoral delivery of XVir-N-31 by ultrasound guidance delayed tumor growth in an immunodeficient model, demonstrating the feasibility of this approach to deliver oncolytic viruses directly into the tumor.

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)0.0000.000

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 head0.048
GPT teacher head0.355
Teacher spread0.308 · 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

Citations26
Published2018
Admission routes1
Has abstractyes

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