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Record W2609792535 · doi:10.1096/fasebj.21.5.a247-b

Formate Kinetics in Folate Deficiency state in Young Swine

2007· article· en· W2609792535 on OpenAlexaff
AbdulRazaq Sokoro, Denis C. Lehotay, Gordon A. Zello, James D. House, Jane Alcorn

Bibliographic record

VenueThe FASEB Journal · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Health
Canadian institutionsUniversity of SaskatchewanSaskatchewan Disease Control LaboratorySaskatchewan HealthUniversity of ManitobaUniversity of Regina
Fundersnot available
KeywordsFormateChemistrySodium formateKineticsMetaboliteMetabolismBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Objective: To investigate the effect of folate deficiency on formate kinetics during formate insult in young swine. Introduction: Formate is a toxic metabolite of MeOH. Detoxification of formate is through its metabolism to CO 2 and H 2 O. Folate is a required cofactor. Method: Twelve young pigs were pair‐matched and randomly divided into two groups on acquisition (~5 weeks). One group was made folate deficient (FFD) by elimination of folic acid from the diet, the other group (FFC) was supplement with folate. Four animals (31–38 kg) from each group were infused with 351mg/kg of sodium formate. The remaining 2 animals were injected with normal saline. Blood samples were collected before, and at 10, 20, 30, 45, 60, 90, 120, 180, 240 and 480 min post dose. Results: Formate accumulation in the FFD group was higher than the FFC group (AUC of 75830±44868 vs 34671±12952, respectively). Elimination was also slower in the FFD (clearance of 0.13±0.05 ml/min in FFC group compared to 0.27±0.14 and a slope of −0.009±0.003 vs −0.046±0.049, respectively). Half‐life was 2.9 times higher in FFD group than in FFC group (86±29 min vs 30±22). A first order kinetics was observed in the FFC and zero order kinetics for the FFD. Conclusion: Adequate folate state is important in faster elimination of formate. A deficiency state alters formate pharmcokinetics, increasing risk of formate 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 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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.025
GPT teacher head0.258
Teacher spread0.232 · 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 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

Citations0
Published2007
Admission routes1
Has abstractyes

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