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Record W2125628685 · doi:10.1093/carcin/23.1.1

Tissue-specific resistance to cancer development in the rat: phenotypes of tumor-modifier genes

2002· review· en· W2125628685 on OpenAlexaff
Geoffrey A. Wood

Bibliographic record

VenueCarcinogenesis · 2002
Typereview
Languageen
FieldMedicine
TopicCancer, Stress, Anesthesia, and Immune Response
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsImmunosurveillancePhenotypeCarcinogenesisBiologyMammary glandCancerCancer researchMammary tumorGeneApoptosisGeneticsBreast cancer

Abstract

fetched live from OpenAlex

Resistance to carcinogenesis in the rat is both strain- and tissue-specific. The phenotypic characteristics of resistance in the mammary gland, liver and peripheral nervous system (PNS) are strikingly similar. In all three tissues, initiation is intact with subsequent formation of preneoplastic cells and lesions. In the mammary gland and PNS, activation of the Ha-ras and neu proto-oncogenes, respectively, takes place. A number of different modifier genes are involved in resistance, many of which appear to be tissue-specific in their action with no overlap between strains. A single resistance phenotype, however, involving the formation, growth and subsequent loss of preneoplastic lesions is common to all three tissues of resistant strains. In the PNS, there is evidence that preneoplastic cells are eliminated by apoptosis or immunosurveillance. In the mammary gland and liver, the immune system is not involved in the loss of preneoplastic lesions and there are no clear differences between susceptible and resistant strains in the kinetics of proliferation and apoptosis of preneoplastic cells. The evidence to date favors a mechanism in which preneoplastic cells from these tissues undergo a process of remodeling/redifferentiation to yield cells with a normal phenotype. Identification of human homologues of rodent tumor-modifier genes will result in a better understanding of cancer development and potentially provide new strategies for prevention and 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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.076
GPT teacher head0.328
Teacher spread0.252 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations39
Published2002
Admission routes1
Has abstractyes

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