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Record W2070099742 · doi:10.1593/neo.07313

Longitudinal Studies of Angiogenesis in Hormone-Dependent Shionogi Tumors

2007· article· en· W2070099742 on OpenAlexaff
Trevor Wade, Piotr Kozłowski

Bibliographic record

VenueNeoplasia · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsUniversity of British Columbia HospitalVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsAngiogenesisInternal medicineMagnetic resonance imagingRank correlationEndocrinologyStatistical significanceTumor progressionAndrogenPathologyUrologyOncologyCancerMedicineBiologyHormoneRadiologyMathematics

Abstract

fetched live from OpenAlex

Vessel size imaging was used to assess changes in the average vessel size of Shionogi tumors throughout the tumor growth cycle. Changes in R(2) and R(2)* relaxivities caused by the injection of a superparamagnetic contrast agent (ferumoxtran-10) were measured using a 2.35-T animal magnetic resonance imaging system, and average vessel size index (VSI) was calculated for each stage of tumor progression: growth, regression, and relapse. Statistical analysis using Spearman rank correlation test showed no dependence between vessel size and tumor volume at any stage of the tumor growth cycle. Paired Student's t test was used to assess the statistical significance of the differences in average vessel size for the three stages of the tumor growth cycle. The average VSI for regressing tumors (15.1 +/- 6.6 microm) was significantly lower than that for growing tumors (35.2 +/- 25.5 microm; P < .01). Relapsing tumors also had an average VSI (45.4 +/- 41.8 microm) higher than that of regressing tumors, although the difference was not statistically significant (P = .067). This study shows that VSI imaging is a viable method for the noninvasive monitoring of angiogenesis during the progression of a Shionogi tumor from androgen dependence to androgen independence.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.517

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 head0.027
GPT teacher head0.307
Teacher spread0.281 · 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 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

Citations23
Published2007
Admission routes1
Has abstractyes

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