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Zebrafish xenografts as a tool for <i>in vivo</i> studies on human cancer

2012· review· en· W1577201120 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.

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2012
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicZebrafish Biomedical Research Applications
Canadian institutionsDalhousie UniversityIzaak Walton Killam Health Centre
FundersDeutsche Krebshilfe
KeywordsZebrafishXenotransplantationBiologyModel organismComputational biologyEmbryonic stem cellMutagenesisFunction (biology)AngiogenesisGenetic screenCancerOrganogenesisCell biologyGeneticsGeneTransplantationMutationMedicinePhenotype

Abstract

fetched live from OpenAlex

The zebrafish has become a powerful vertebrate model for genetic studies of embryonic development and organogenesis and increasingly for studies in cancer biology. Zebrafish facilitate the performance of reverse and forward genetic approaches, including mutagenesis and small molecule screens. Moreover, several studies report the feasibility of xenotransplanting human cells into zebrafish embryos and adult fish. This model provides a unique opportunity to monitor tumor-induced angiogenesis, invasiveness, and response to a range of treatments in vivo and in real time. Despite the high conservation of gene function between fish and humans, concern remains that potential differences in zebrafish tissue niches and/or missing microenvironmental cues could limit the relevance and translational utility of data obtained from zebrafish human cancer cell xenograft models. Here, we summarize current data on xenotransplantation of human cells into zebrafish, highlighting the advantages and limitations of this model in comparison to classical murine models of xenotransplantation.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.553
Threshold uncertainty score0.583

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.252
GPT teacher head0.492
Teacher spread0.240 · 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