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Record W2164693933 · doi:10.1080/10408440500246801

Hormetic Effects of Hormones, Antihormones, and Antidepressants on Cancer Cell Growth in Culture: In Vivo Correlates

2005· review· en· W2164693933 on OpenAlexaff
Lorne J. Brandes

Bibliographic record

VenueCritical Reviews in Toxicology · 2005
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEstrogen and related hormone effects
Canadian institutionsCancerCare ManitobaUniversity of Manitoba
Fundersnot available
KeywordsHormesisCancerCancer cellIn vivoHormoneCell growthIn vitroPharmacologyBiologyBreast cancerInternal medicineCancer researchEndocrinologyMedicinePhysiologyBiochemistryOxidative stressBiotechnology

Abstract

fetched live from OpenAlex

Evidence is presented that the ability of hormones and antihormones to cause biphasic (hormetic) proliferative responses in cancer cells in vitro correlates with a similar effect of these substances in humans with cancer. Certain antidepressants also produce biphasic growth responses of cancer cells in vitro and stimulate cancer growth in rodents, correlating with an increased risk of breast and other cancers in some, but not all, epidemiological studies assessing early and/or late cancer incidence in patients on antidepressant drugs. The observation that certain drugs with biphasic effects on cancer cell growth in vitro may also produce an "up-down" effect on cancer growth in humans supports Calabrese's suggestion that the concept of the hormetic dose response must be taken seriously by toxicologists and regulators.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.017
GPT teacher head0.344
Teacher spread0.327 · 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 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

Citations23
Published2005
Admission routes1
Has abstractyes

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