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Record W3179068261 · doi:10.1126/scitranslmed.aba4627

Repurposed floxacins targeting RSK4 prevent chemoresistance and metastasis in lung and bladder cancer

2021· article· en· W3179068261 on OpenAlexfundno aff
Stelios Chrysostomou, Rajat Roy, Filippo Prischi, Lucksamon Thamlikitkul, Kathryn Chapman, Uwais Mufti, Robert L. Peach, Laifeng Ding, David C. Hancock, Christopher Moore, Míriam Molina‐Arcas, Francesco Mauri, David J. Pinato, Joel Abrahams, Silvia Ottaviani, Leandro Castellano, Georgios Giamas, Jennifer Pascoe, Devmini C. Moonamale, Sarah Pirrie, Claire Gaunt, Lucinda Billingham, Neil Steven, Michael Cullen, David Hrouda, Mathias Winkler, John Post, Philip Cohen, Seth J. Salpeter, Vered Bar, Adi Zundelevich, Shay Golan, Dan Leibovici, Romain Lara, David R. Klug, Sophia N. Yaliraki, Mauricio Barahona, Yulan Wang, Julian Downward, Mark Skehel, Maruf M. U. Ali, Michael J. Seckl, Olivier E. Pardo

Bibliographic record

VenueScience Translational Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicCancer Mechanisms and Therapy
Canadian institutionsnot available
FundersEuropean Research CouncilImperial Experimental Cancer Medicine CentreEngineering and Physical Sciences Research CouncilNational Institute for Health and Care ResearchImperial Oil LimitedNIHR Imperial Biomedical Research CentreCancer Treatment and Research TrustCancer Research UKWellcome TrustFrancis Crick InstituteMedical Research CouncilCentro Singular de Investigación de GaliciaUniversity of Kentucky
KeywordsCancer researchMedicineMetastasisBladder cancerLung cancerEx vivoIn vivoGene silencingChemotherapyCancerPathologyBiologyInternal medicine

Abstract

fetched live from OpenAlex

= 0.048) long-term overall survival times. Hence, we suggest that RSK4 inhibition may represent an effective therapeutic strategy for treating lung and bladder cancer.

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 score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.018
GPT teacher head0.327
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

Citations35
Published2021
Admission routes1
Has abstractyes

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