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Record W2008735245 · doi:10.2174/187152206775528905

Impact of Environmental Endocrine Disruption on the Reproductive System for Human Health

2006· article· en· W2008735245 on OpenAlexaff
Kyung‐Chul Choi, Eui‐Bae Jeung, Peter C. K. Leung

Bibliographic record

VenueImmunology Endocrine & Metabolic Agents - Medicinal Chemistry · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicEffects and risks of endocrine disrupting chemicals
Canadian institutionsUniversity of British Columbia
FundersUNICEF
KeywordsEndocrine systemHormoneReproductive systemBiologyOvaryUterusPolycystic ovaryReproductive healthPhysiologyEndocrinologyMedicinePopulationDiabetes mellitusEnvironmental health

Abstract

fetched live from OpenAlex

Endocrine disruptors (EDs) are environmental chemicals that interfere with physiological systems, adversely affecting hormone balance (endocrine system), or disrupting normal function in the organs which hormones regulate or modulate, i.e., the female and male reproductive systems. Although endocrine disruption is a global concern for human health, its impact and significance and the screening strategy for detecting these synthetic or man-made chemicals are not well described in female and male reproductive functions. Thus, this review summarizes the interference of environmental EDs on reproductive development and function, and introduces biomarkers and screening methods for EDs in in vitro and in vivo models, in particular, female reproductive system. These methods include studying of the uterine expression of Calbindin-D9k (CaBP-9k), a cytosolic calcium binding protein regulated by estrogenic or progestogenic compounds. In addition, this review highlights the effect of exposure to multiple EDs on reproductive functions, and brings attention to major sources of exposure. Keywords: Endocrine disruption, steroids, estrogenic chemicals, uterus, ovary

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), Insufficient payload (model declined to judge)
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 score0.328
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.342
Teacher spread0.330 · 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 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

Citations5
Published2006
Admission routes1
Has abstractyes

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