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Record W2115526918 · doi:10.1016/j.mce.2012.12.022

Peri-conceptional changes in maternal exposure to sewage sludge chemicals disturbs fetal thyroid gland development in sheep

2013· article· en· W2115526918 on OpenAlexafffund
Sabine Hombach‐Klonisch, Adrian Danescu, Farhana Begum, Maria R. Amezaga, S. M. Rhind, Richard M. Sharpe, Neil P. Evans, Michelle Bellingham, Corinne Cotinot, Béatrice Mandon‐Pepin, Paul Fowler, Thomas Klonisch

Bibliographic record

VenueMolecular and Cellular Endocrinology · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaWellcome Trust
KeywordsFetusThyroidEndocrinologyInternal medicineOffspringIn uteroPregnancyFollicular phasePhysiologyBiologyMedicine

Abstract

fetched live from OpenAlex

Ewes were exposed to sewage sludge-fertilized pastures in a study designed investigate pre-conceptual and/or gestational exposure to environmental chemicals. The in utero impact on fetal thyroid morphology and function at day 110 (of 145) of pregnancy was then determined. Pre-conceptual exposure increased the relative thyroid organ weights in male fetuses. The number of thyroid follicles in thyroids of fetuses after pre-conceptual or gestational exposure was reduced. This correlated with an increase in Ki67 positive cells. Pre-conceptual exposure to sewage sludge reduced small blood vessels in fetal thyroids. Thyroid tissues of exposed fetuses contained regions where mature angio-follicular units were reduced exhibiting decreased immunostaining for sodium-iodide symporter (NIS). Fetal plasma levels of fT3 and fT4 in exposed animals, however, were not different from controls suggesting compensatory changes in the thyroid gland to maintain homeostasis in exposed fetuses. The regional aberrations in thyroid morphology may impact on the post-natal life of the exposed offspring.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.307
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.009
GPT teacher head0.218
Teacher spread0.209 · 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

Citations25
Published2013
Admission routes2
Has abstractyes

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