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Record W2013760476 · doi:10.1289/ehp.10381

Hair Mercury Negatively Correlates with Calcium Pump Activity in Human Term Newborns and Their Mothers at Delivery

2007· article· en· W2013760476 on OpenAlexaff
Guy Huel, Josiane Sahuquillo, Ginette Debotte, Jean‐François Oury, Larissa Takser

Bibliographic record

VenueEnvironmental Health Perspectives · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy Metal Exposure and Toxicity
Canadian institutionsUniversité de Sherbrooke
FundersAgence Française de Sécurité Sanitaire de l'Environnement et du TravailMinistry of Environment
KeywordsMercury (programming language)CalciumMedicinePregnancyEnvironmental healthPhysiologyToxicologyPediatricsBiologyInternal medicineGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: Calcium homeostasis is a known target of several environmental toxicants including lead and mercury. OBJECTIVE: Our goal was to determine the relationship between Hg exposure and erythrocyte Ca pump activity in women at delivery and in their newborns. METHODS: We determined total Hg as well as Pb concentrations in 81 hair and blood samples obtained at delivery. Basal and calmodulin-stimulated Ca pump activity was measured in red blood cells from cord blood and maternal erythrocyte plasma membranes. RESULTS: Maternal hair Hg negatively correlates with Ca pump activity in maternal and cord blood erythrocytes. Pb and Hg both independently correlate negatively with Ca pump activity without any statistically significant interaction. After adjustment for potential confounders, Pb and Hg explain about 30% and 7% of total variance of Ca pump activity in newborns and mothers, respectively. CONCLUSION: Our findings confirm results reported in previous experimental studies and support the use of biomarkers in newborns from general population.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0020.000

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 head0.014
GPT teacher head0.263
Teacher spread0.249 · 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 designObservational
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

Citations17
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueEnvironmental Health PerspectivesSame topicHeavy Metal Exposure and ToxicityFrench-language works237,207