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Record W2110999499 · doi:10.1369/jhc.2009.954644

Immunocytochemical Localization of Sex Steroid Hormone Receptors in Normal Human Mammary Gland

2009· article· en· W2110999499 on OpenAlexaff
Sijie Li, Bing Han, Guojin Liu, Songyun Li, Johanne Ouellet, Fernand Labrie, Georges Pelletier

Bibliographic record

VenueJournal of Histochemistry & Cytochemistry · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEstrogen and related hormone effects
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMammary glandReceptorSteroidSteroid hormoneSex hormone receptorHormone receptorEndocrinologyHormoneSex steroidInternal medicineBiologyImmunocytochemistryMedicineEstrogen receptorCancerBreast cancer

Abstract

fetched live from OpenAlex

The sex steroids, estrogens, progesterone, and androgens, all play a role in mammary development and function. To precisely identify the sites of action of these steroids, we studied the localization of the estrogen receptor alpha (ERalpha) and ERbeta, the progesterone receptor A (PRA) and PRB, and androgen receptors (AR) in the normal human mammary gland. Immunocytochemical localization of ERalpha, ERbeta, PRA, PRB, and AR was performed with reduction mammoplasty specimens from premenopausal women. ERalpha, PRA, PRB, and AR were localized mostly to the inner layer of epithelial cells lining acini and intralobular ducts, as well as to myoepithelial cells scattered in the external layer of interlobular ducts. AR was also found in some stromal cells. ERbeta staining was more widespread, resulting in epithelial and myoepithelial cells being labeled in acini and ducts as well as stromal cells. These results suggest that all sex steroids can directly act on epithelial cells to modulate development and function of the human mammary gland. Estrogens and androgens can also indirectly influence epithelial cell activity by an action on stromal cells.

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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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)0.0030.001

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 head0.004
GPT teacher head0.233
Teacher spread0.229 · 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

Citations54
Published2009
Admission routes1
Has abstractyes

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