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Record W2046865337 · doi:10.1021/bc900066j

Radiolabeling of Biodegradable Polymeric Microspheres with [<sup>99m</sup>Tc(CO)<sub>3</sub>]<sup>+</sup> and <i>in Vivo</i> Biodistribution Evaluation using MicroSPECT/CT Imaging

2009· article· en· W2046865337 on OpenAlexaff
Katayoun Saatchi, Urs O. Häfeli

Bibliographic record

VenueBioconjugate Chemistry · 2009
Typearticle
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBiodistributionChemistryIn vivoMicroparticleMicrosphereLigand (biochemistry)SpleenRadiochemistryNuclear chemistryIn vitroChemical engineeringBiochemistryMedicine

Abstract

fetched live from OpenAlex

Poly(L-lactide) (PLA) microspheres tailored with a tridentate chelating group were radiolabeled with [(99m)Tc(H(2)O)(3)(CO)(3)](+) and optimized for labeling efficiency and stability. Various ligand-polymer blend compositions with commercial PLA (from 2% to 100%) were evaluated. Labeling efficiencies over 95% were achieved in a 5 min reaction using 100% of the ligand-polymer or within 15 min using a 5% ligand-polymer blend. The addition of 1.5% of PEGylated copolymer to the blend did not affect the labeling efficiency of these particles but changed their in vivo behavior. MicroSPECT/CT imaging showed significant uptake of non-PEGylated microspheres by the murine lung, while only the liver and spleen took up PEGylated microspheres. Such (99m)Tc radiolabeled biodegradable microspheres will be useful diagnostic imaging agents for visualization of the functioning reticuloendothelial system (RES). Similarly, other sizes of the same microspheres will allow imaging of lung perfusion, bone marrow, lymph and inflammation scintigraphy, and radioembolization therapy.

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 categoriesMeta-epidemiology (narrow)
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 score0.040
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.284
Teacher spread0.267 · 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.

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

Citations29
Published2009
Admission routes1
Has abstractyes

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