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Record W2100781932 · doi:10.2174/1874325001307010109

Expressional Analysis of GFP-Tagged Cells in an In Vivo Mouse Model of Giant Cell Tumor of Bone

2013· article· en· W2100781932 on OpenAlexafffund
Shalini Singh, Mohini Singh, Isabella W.Y. Mak, Michelle Ghert

Bibliographic record

VenueThe Open Orthopaedics Journal · 2013
Typearticle
Languageen
FieldMedicine
TopicBone Tumor Diagnosis and Treatments
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health ResearchMcMaster University
KeywordsGreen fluorescent proteinStromal cellIn vivoMesenchymal stem cellHaematopoiesisGiant-cell tumor of boneCell biologyCellCell fusionBone marrowCell cultureGiant cellOsteoclastCancer researchMedicinePathologyIn vitroBiologyStem cellGeneGenetics

Abstract

fetched live from OpenAlex

Giant cell tumor of bone in a neoplastic stromal cell which survives for multiple passages in primary cell culture with a stable phenotype. In the pathological environment of GCT, the neoplastic nature of the mesenchymal stromal component drives local hematopoietic precursors to undergo fusion and form multinucleated osteoclast like giant cells. There is currently very limited knowledge about the pathogenesis of GCT due to the lack of suitable in vivo models for this tumor. Here we report stable gene transfer of Green fluorescence protein (GFP) in GCT stromal cells. In the present study, we have used GCT stromal cells that stably express enhanced green fluorescence protein (GFP) that are used in a new in vivo culture model. Our results show the utility of the GFP tagged cell lines that stably express GFP signals up to 52 weeks of continuous growth. The in vivo model described herein can serve as an excellent system for in vivo therapeutic and mechanistic evaluation of existing and novel targets for GCT.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.287
Teacher spread0.259 · 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

Citations14
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueThe Open Orthopaedics JournalSame topicBone Tumor Diagnosis and TreatmentsFrench-language works237,207