MétaCan
Menu
Back to cohort

Testing predictions of the Reconnection Location with THEMIS

2009· article· en· W25261238 on OpenAlexaff
K. J. Trattner, S. M. Petrinec, S. A. Fuselier, T. D. Phan

Bibliographic record

VenueEGU General Assembly Conference Abstracts · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsMagnetopauseMagnetic reconnectionPhysicsLine (geometry)GeophysicsInterplanetary magnetic fieldSolar windNanoflaresMagnetic fieldCoronal mass ejectionGeometryMathematics

Abstract

fetched live from OpenAlex

Plasticity in cancer stem-like cells (CSC) may provide a key basis for cancer heterogeneity and therapeutic response. In this study, we assessed the effect of combining a drug that abrogates CSC properties with standard-of-care therapy in a Ewing sarcoma family tumor (ESFT). Emergence of CSC in this setting has been shown to arise from a defect in TARBP2-dependent microRNA maturation, which can be corrected by exposure to the fluoroquinolone enoxacin. In the present work, primary ESFT from four patients containing CD133(+) CSC subpopulations ranging from 3% to 17% of total tumor cells were subjected to treatment with enoxacin, doxorubicin, or both drugs. Primary ESFT CSC and bulk tumor cells displayed divergent responses to standard-of-care chemotherapy and enoxacin. Doxorubicin, which targets the tumor bulk, displayed toxicity toward primary adherent ESFT cells in culture but not to CSC-enriched ESFT spheres. Conversely, enoxacin, which enhances miRNA maturation by stimulating TARBP2 function, induced apoptosis but only in ESFT spheres. In combination, the two drugs markedly depleted CSCs and strongly reduced primary ESFTs in xenograft assays. Our results identify a potentially attractive therapeutic strategy for ESFT that combines mechanism-based targeting of CSC using a low-toxicity antibiotic with a standard-of-care cytotoxic drug, offering immediate applications for clinical evaluation.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.843
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

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 head0.014
GPT teacher head0.220
Teacher spread0.206 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueEGU General Assembly Conference AbstractsSame topicIonosphere and magnetosphere dynamicsFrench-language works237,207