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Record W179529194 · doi:10.3233/bsi-130046

Establishing a baseline value for urinary arsenic:selenium ratio in unexposed populations in the United Kingdom

2013· article· en· W179529194 on OpenAlexaff
Eid I. Brima, Chris F. Harrington, Richard O. Jenkins, Andrew G. Gault, David A. Polya, Gareth F. Pearson, Gillian M. Greenway, Parvez I. Haris

Bibliographic record

VenueBiomedical Spectroscopy and Imaging · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsQueen's University
FundersEngineering and Physical Sciences Research CouncilUniversity of ManchesterDe Montfort University
KeywordsArsenicSeleniumUrinary systemMedicineBaseline (sea)Environmental chemistryInternal medicineChemistryBiology

Abstract

fetched live from OpenAlex

The relationship between arsenic (As) and selenium (Se) in the human body is poorly understood. We have investigated the concentrations of urinary As and Se in three ethnic groups (n=63) in the United Kingdom and show that there is a positive correlation (r=0.62, p<0.001) between total concentrations of As and Se and that the ratio of these two elements is stable, with a mean value (±SD) of 0.7±0.4. Furthermore, concentrations of individual arsenic species methylarsonate (MA), dimethylarsinate (DMA) and arsenobetaine (AB) in the urine samples show a positive correlation with total Se (As(III) and As(V) were not detected). The intra-individual variation of the As:Se ratio also remains stable over time, as determined by monitoring a volunteer over a period of one year, and deviates only after recent seafood consumption. It appears that the ratio is also stable across diverse populations across different cultures and continents, evident from our calculation of As:Se ratio from concentrations of these elements found in urine samples from different populations published in the literature. Our study involved analysis of 63 urine samples from three ethnic groups (White Caucasian n=20, Asian n=21 and Somali n=22), 58 urine samples from 29 Ramadan fasting volunteers and 12 from one volunteer whose urine samples were collected over a period of one year. All the participants completed a lifestyle questionnaire and were asked to refrain from eating seafood or fish for three days prior to collection of the sample. Total As and Se in urine were determined by inductively coupled plasma mass spectrometry (ICP-MS). As species (AB, DMA, MA, As(III), As(V)) were determined by using high performance liquid chromatography (HPLC) combined with ICP-MS. Mean ± SD As:Se ratios of 0.8±0.4, 0.7±0.4, 0.4±0.2, 0.7±0.3 and 1.2±0.3 were obtained for the Asian, White Caucasian, Somali, fasting, and one volunteer respectively, giving an overall mean of 0.7±0.4 (SD). It is noteworthy, that when comparing ethnic differences, the Somali group shows a statistically significant lower As:Se ratio (0.4±0.2, p<0.05) compared to Asian and White Caucasian groups; this is ascribed to lower urinary As concentrations in this group. The study over one year with a single volunteer revealed that recent (within 3 days) seafood consumption results in a significantly different (p<0.05) As:Se ratio (4.0). We have calculated from the literature the value of As:Se for populations, exposed to As through drinking water, can range from 2.0–9.6. Based on our own work and the values we calculated from other studies we suggest that the baseline range for mean As:Se ratio is 0.4–1.2, provided that the urine samples are collected in the absence of recent consumption of seafood. The relatively stable As:Se ratio observed in this study suggests a link between these two elements in humans adding support to earlier studies with animals and humans exposed to inorganic arsenic in drinking water which reported interaction between these elements and that Se may play a role in counteracting As toxicity.

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)0.004
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 score0.048
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.025
GPT teacher head0.291
Teacher spread0.266 · 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".

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Citations0
Published2013
Admission routes1
Has abstractyes

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