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Record W2056288720 · doi:10.1159/000138873

Plasma Concentrations and Pharmacokinetics of Phenylalanine in Rats and Mice Administered Aspartame

2008· article· en· W2056288720 on OpenAlexaff
Jerry J. Hjelle, Robert E. Dudley, M. Marietta, Paul G. Sanders, Bruce C. Dickie, Jerry Brisson, Frank N. Kotsonis

Bibliographic record

VenuePharmacology · 2008
Typearticle
Languageen
FieldNursing
TopicBiochemical Analysis and Sensing Techniques
Canadian institutionsNutrasource
Fundersnot available
KeywordsAspartamePhenylalanineDipeptidePharmacokineticsChemistryEndocrinologyOral administrationPharmacologyTyrosineInternal medicinePlasma concentrationAmino acidBiochemistryMedicine

Abstract

fetched live from OpenAlex

Aspartame (L-aspartyl-L-phenylalanine methyl ester) is an esterified, dipeptide sweetener that is rapidly and completely metabolized in the gastrointestinal tract to phenylalanine, aspartic acid and methanol. The pharmacokinetics of phenylalanine (PHE) and tyrosine (TYR) were examined following the administration of oral doses of aspartame (APM) to fasted male Sprague-Dawley rats (0, 50, 100, 200, 500 and 1,000 mg/kg) and CD-1 mice (0, 100, 200, 500, 1,000 and 2,000 mg/kg). Peak plasma PHE/large neutral amino acid (LNAA) ratios were calculated. Maximal plasma PHE and TYR concentrations were observed within 1 h after dosing and returned to baseline within 4-8 h in both species regardless of the dose of APM. Mean PHE Cmaxs ranged from 73.6 to 1,161 nmol/ml in the rat, and from 78.6 to 1,967 nmol/ml in the mouse. TYR Cmaxs ranged from 91.6 to 502 nmol/ml and from 89.2 to 792 nmol/ml in the rat and mouse, respectively. AUCs and Cmaxs were linear with dose in both species. Peak plasma PHE/LNAA ratios ranged from 0.112 to 1.117 in rats and from 0.121 to 1.769 in mice. Comparison of these ratios with those observed previously in humans indicates that rodents require a 2-6 times higher dose of APM than humans to produce similar increases in plasma PHE/LNAA ratios.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.031
GPT teacher head0.309
Teacher spread0.278 · 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 designBench or experimental
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

Citations23
Published2008
Admission routes1
Has abstractyes

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