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Radiolabelling bacteria with 89Zr-DBN for PET/MRI: comparing [89Zr]Zr oxalate and [89Zr]Zr phosphate

2025· article· en· W4415532487 on OpenAlexafffund
Diksha Diksha, Sarah C. Donnelly, Mojmı́r Suchý, Neil Cockburn, Frank S. Prato, Michael S. Kovacs, Donna E. Goldhawk

Bibliographic record

VenueNuclear Medicine and Biology · 2025
Typearticle
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsLawson Health Research InstituteWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBacteriaOxalateLabellingChelationFacultativePhosphate

Abstract

fetched live from OpenAlex

Purpose Our bodies host a diverse and complex community of bacteria, known as the microbiota, which play a crucial role in overall wellness. The gut microbiota perform essential functions, such as food fermentation, pathogen protection, and vitamin production. Dysbiosis, or the imbalance of these bacterial communities, can disrupt these processes, leading to diseases like cancer, respiratory infections, and neurological disorders. To enable non-invasive tracking of microbial distribution in vivo , this study investigates [ 89 Zr]Zr oxalate as a more efficient and cost-effective alternative to [ 89 Zr]Zr phosphate for radiolabelling diverse bacterial species for PET/MRI. Methods To test radiolabelling efficiency, the bifunctional chelator DBN, consisting of desferrioxamine (DFO) to chelate 89 Zr and a lysine-reactive group (isothiocyanate) for cell surface protein attachment, was used. The bifunctional chelator was mixed with either neutralized [ 89 Zr]Zr phosphate (titrated to pH 7 with 1 M K 2 CO 3 ) in HEPES buffer or [ 89 Zr]Zr oxalate (titrated to pH 7 with 2 M Na 2 CO 3 ) in phosphate buffered saline (PBS). The efficiency of isotope chelation was evaluated by radio thin layer chromatography (radio-TLC). The efficiency of bacterial conjugation was examined with commensal Lactobacillus crispatus ATCC33820 and probiotic Escherichia coli Nissle 1917. A t -test with unequal variances assessed statistical differences in chelation and bacterial labelling efficiencies. Results [ 89 Zr]Zr oxalate showed significantly higher chelation efficiency to DBN (94.8 ± 5.5 %) compared to [ 89 Zr]Zr phosphate (66.6 ± 16.3 %; p < 0.05). Labelling efficiency for L. crispatus improved when [ 89 Zr]Zr oxalate was used either after one half-life had passed since production or within 1 half-life but diluted with water in a 1:1 ratio. Optimal labelling provided an average activity per live cell (colony-forming unit, CFU) between 0.003 and 0.12 Bq/CFU, with activities above 0.01 Bq/CFU causing significant bacterial death, while lower activities (below 0.006 Bq/CFU) effectively maintained bacterial viability. Comparable bacterial labelling efficiency results were obtained with Escherichia coli Nissle 1917. Conclusion This study establishes the first standardized protocol for radiolabelling viable bacteria using 89 Zr-DBN derived from [ 89 Zr]Zr oxalate. This method enables high chelation and labelling efficiency while preserving cell viability and is applicable to both L. crispatus (Gram-positive, facultative anaerobe) and E.coli (Gram-negative, aerobe), supporting its use in PET/MRI based bacterial imaging.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.054
GPT teacher head0.352
Teacher spread0.299 · 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".

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Citations0
Published2025
Admission routes2
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