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Record W3112875688 · doi:10.3390/molecules25235762

Benefits and Detriments of Gadolinium from Medical Advances to Health and Ecological Risks

2020· review· en· W3112875688 on OpenAlexafffund
Colin Unruh, Nicolas Van Bavel, Max Anikovskiy, Elmar J. Prenner

Bibliographic record

VenueMolecules · 2020
Typereview
Languageen
FieldEnvironmental Science
TopicHeavy Metal Exposure and Toxicity
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGadoliniumHuman healthNephrogenic systemic fibrosisChemistryMembraneCalciumMagnetic resonance imagingMedicineBiophysicsBiochemistryNanotechnologyBiologyInternal medicineMaterials scienceEnvironmental health

Abstract

fetched live from OpenAlex

Gadolinium (Gd)-containing chelates have been established as diagnostics tools. However, extensive use in magnetic resonance imaging has led to increased Gd levels in industrialized parts of the world, adding to natural occurrence and causing environmental and health concerns. A vast amount of data shows that metal may accumulate in the human body and its deposition has been detected in organs such as brain and liver. Moreover, the disease nephrogenic systemic fibrosis has been linked to increased Gd3+ levels. Investigation of Gd3+ effects at the cellular and molecular levels mostly revolves around calcium-dependent proteins, since Gd3+ competes with calcium due to their similar size; other reports focus on interaction of Gd3+ with nucleic acids and carbohydrates. However, little is known about Gd3+ effects on membranes; yet some results suggest that Gd3+ interacts strongly with biologically-relevant lipids (e.g., brain membrane constituents) and causes serious structural changes including enhanced membrane rigidity and propensity for lipid fusion and aggregation at much lower concentrations than other ions, both toxic and essential. This review surveys the impact of the anthropogenic use of Gd emphasizing health risks and discussing debilitating effects of Gd3+ on cell membrane organization that may lead to deleterious health consequences.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.999
Threshold uncertainty score0.634

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.071
GPT teacher head0.365
Teacher spread0.294 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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

Citations49
Published2020
Admission routes2
Has abstractyes

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