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Record W2030857804 · doi:10.1002/jor.1100180213

Met oncogene aberrant expression in canine osteosarcoma

2000· article· en· W2030857804 on OpenAlex
Riccardo Ferracini, Paola Angelini, E. Cagliero, Alessandra Linari, Marina Martano, Jay S. Wunder, Paolo Buracco

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Orthopaedic Research® · 2000
Typearticle
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsMount Sinai Hospital
Fundersnot available
KeywordsOsteosarcomaOncogeneCancer researchBiopsyMetastasisPathologyPrimary tumorMedicineBiologyCancerInternal medicineCell cycle

Abstract

fetched live from OpenAlex

The objective of this study was to investigate the role of the MET oncogene in canine osteosarcoma. Seven large-breed dogs affected by spontaneous skeletal osteosarcoma underwent en bloc tumor excision. Total RNA was extracted from frozen tumor samples and assessed for expression of the MET oncogene by Northern blot analysis. Five of seven biopsy samples expressed high levels of the MET oncogene; its expression in the primary tumors was comparable with that previously identified in primary osteosarcomas in humans. A lung metastasis from one of the dogs expressed MET at a higher level than did its primary tumor. Spontaneously arising osteosarcoma in dogs clinically and pathologically mimics the corresponding disease in humans. We previously demonstrated that the MET oncogene was aberrantly expressed in a high percentage of human osteosarcomas. The results of the current study also provide a molecular parallel between the tumors in dogs and humans. This in vivo model may be helpful in evaluating new strategies for therapy against osteosarcoma.

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.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.104
GPT teacher head0.430
Teacher spread0.326 · 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