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Molecular-size fractionation of pentastarch, radiolabelling with 99mTc, and evaluation of biological behaviour in mice

2005· article· en· W2031778142 on OpenAlexaff
Kennedy Mang era, Margaret Krzyzelewski, Shelley Greaves, David H. Greenberg, Mervyn W. Billinghurst

Bibliographic record

VenueNuclear Medicine Communications · 2005
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsUniversity of ManitobaHealth Sciences Centre
Fundersnot available
KeywordsChemistryFractionationChromatography

Abstract

fetched live from OpenAlex

BACKGROUND: Pentastarch is used clinically as a plasma volume expander for the management of substantial blood loss. 99mTc labelled pentastarch may be useful as a diagnostic agent in place of 99mTc labelled red blood cells. METHODS: Commercial pentastarch (PS; molecular weight (MW) 240 kDa) was separated according to molecular size by using chromatography, and the fractions were pooled as small (MW 128 kDa), medium (MW 277 kDa) and large (MW 510 kDa) pentastarch. We studied the effect of various physicochemical parameters on the efficiency of radiolabelling with 99mTc and on the stability of the products, and evaluated the biological properties of the 99mTc labelled preparations. RESULTS: We developed an optimised kit formulation containing 3.25 mg pentastarch and 0.13 mg gentisic acid that can be reliably labelled with 99mTc at pH 6.6-8.2 with good stability. In mice, the 99mTc labelled medium pentastarch showed the more favourable blood retention properties (56% of initial blood activity is retained after 3 h) with lower liver levels.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.065
GPT teacher head0.348
Teacher spread0.283 · 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 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

Citations0
Published2005
Admission routes1
Has abstractyes

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Same venueNuclear Medicine CommunicationsSame topicBlood transfusion and managementFrench-language works237,207