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Record W2126549321 · doi:10.1155/2010/174528

Therapeutic Antibodies Targeting CSF1 Impede Macrophage Recruitment in a Xenograft Model of Tenosynovial Giant Cell Tumor

2010· article· en· W2126549321 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSarcoma · 2010
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal synovial abnormalities and treatments
Canadian institutionsBC Cancer AgencyUniversity of British Columbia
FundersMichael Smith Health Research BCCancer Research SocietySarcoma Foundation of America
KeywordsMedicineGiant cellMacrophageMonoclonal antibodyCancer researchAntibodyInfiltration (HVAC)PathologyImmunologyIn vitroBiology

Abstract

fetched live from OpenAlex

Tenosynovial giant cell tumor is a neoplastic disease of joints that can cause severe morbidity. Recurrences are common following local therapy, and no effective medical therapy currently exists. Recent work has demonstrated that all cases overexpress macrophage colony-stimulating factor (CSF1), usually as a consequence of an activating gene translocation, resulting in an influx of macrophages that form the bulk of the tumor. New anti-CSF1 drugs have been developed; however there are no preclinical models suitable for evaluation of drug benefits in this disease. In this paper, we describe a novel renal subcapsular xenograft model of tenosynovial giant cell tumor. Using this model, we demonstrate that an anti-CSF1 monoclonal antibody significantly inhibits host macrophage infiltration into this tumor. The results from this model support clinical trials of equivalent humanized agents and anti-CSF1R small molecule drugs in cases of tenosynovial giant cell tumor refractory to conventional local therapy.

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)0.000
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 score0.286
Threshold uncertainty score0.746

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 head0.028
GPT teacher head0.291
Teacher spread0.263 · 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