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Record W2092090776 · doi:10.1021/tx049904e

Mercury Binding to the Chelation Therapy Agents DMSA and DMPS and the Rational Design of Custom Chelators for Mercury

2004· article· en· W2092090776 on OpenAlex
Graham N. George, Roger C. Prince, Jürgen Gailer, Gavin A. Buttigieg, M. Bonner Denton, Hugh H. Harris, Ingrid J. Pickering

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChemical Research in Toxicology · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsChelationChemistryMercury (programming language)Chelation therapyDimercaptosuccinic acidMERCURY EXPOSUREInorganic chemistryEnvironmental chemistryMedicine

Abstract

fetched live from OpenAlex

Clinical chelation therapy of mercury poisoning generally uses one or both of two drugs--meso-dimercaptosuccinic acid (DMSA) and dimercaptopropanesulfonic acid (DMPS), commercially sold as Chemet and Dimaval, respectively. We have used a combination of mercury L(III)-edge X-ray absorption spectroscopy and density functional theory calculations to investigate the chemistry of interaction of mercuric ions with each of these chelation therapy drugs. We show that neither DMSA nor DMPS forms a true chelate complex with mercuric ions and that these drugs should be considered suboptimal for their clinical task of binding mercuric ions. We discuss the design criteria for a mercuric specific chelator molecule or "custom chelator", which might form the basis for an improved clinical treatment.

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.170
Threshold uncertainty score0.365

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.138
GPT teacher head0.398
Teacher spread0.260 · 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