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Record W2006239204 · doi:10.1177/0091270007303767

Mesna as a Nonvitamin Intervention to Lower Plasma Total Homocysteine Concentration: Implications for Assessment of the Homocysteine Theory of Atherosclerosis

2007· article· en· W2006239204 on OpenAlexafffund
Bradley L. Urquhart, David J. Freeman, J. David Spence, Andrew A. House

Bibliographic record

VenueThe Journal of Clinical Pharmacology · 2007
Typearticle
Languageen
FieldMedicine
TopicFolate and B Vitamins Research
Canadian institutionsRobarts Clinical TrialsLawson Health Research Institute
FundersCanadian Institutes of Health ResearchLawson Health Research Institute
KeywordsHomocysteinePlasma homocysteineMedicineInternal medicine

Abstract

fetched live from OpenAlex

Elevated plasma total homocysteine is independently associated with atherosclerosis. Recent randomized trials show that vitamins lower total homocysteine but do not prevent cardiovascular events, suggesting the need for nonvitamin therapies to evaluate whether a causative relationship exists. Mesna (sodium 2-mercaptoethanesulfonate) is a thiol-containing drug capable of liberating homocysteine bound by disulfide bonds to proteins, facilitating its excretion. The effect of oral mesna on total homocysteine has not been evaluated and was the objective of this study. Eleven healthy volunteers received vehicle or 10 mg/kg mesna in random order, after which serial blood and urine samples were collected over 4 hours. Plasma total homocysteine decreased by 24.2% (P < .0001) following mesna. Urinary homocysteine excretion was significantly greater with mesna (3.9 +/- 2.4 mumol) compared to vehicle (0.4 +/- 0.1 mumol), P < .01. Oral mesna decreases plasma total homocysteine and is a potential nonvitamin treatment for assessing the homocysteine theory of atherosclerosis.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.107
GPT teacher head0.507
Teacher spread0.400 · 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 designNon-randomized trial
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

Citations16
Published2007
Admission routes2
Has abstractyes

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