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Record W2885727665 · doi:10.2967/jnumed.118.215350

Monosodium Glutamate Reduces <sup>68</sup>Ga-PSMA-11 Uptake in Salivary Glands and Kidneys in a Preclinical Prostate Cancer Model

2018· article· en· W2885727665 on OpenAlexafffund
Étienne Rousseau, Joseph Lau, Hsiou‐Ting Kuo, Zhengxing Zhang, Helen Merkens, Navjit Hundal-Jabal, Nadine Colpo, Kuo‐Shyan Lin, François Bénard

Bibliographic record

VenueJournal of Nuclear Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsBC Cancer AgencyUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsBiodistributionKidneyChemistryLNCaPSalineProstate cancerEndocrinologyMonosodium glutamateProstateInternal medicineSalivary glandMedicineCancerBiochemistryIn vitro

Abstract

fetched live from OpenAlex

We evaluated the ability of monosodium glutamate (MSG) to reduce salivary and kidney uptake of a prostate-specific membrane antigen (PSMA) radioligand without affecting tumor uptake. Methods: LNCaP tumor-bearing mice were intraperitoneally injected with MSG (657, 329, or 164 mg/kg) or phosphate-buffered saline (PBS). Fifteen minutes later, the mice were intravenously administered 68 Ga-PSMA-11. PET/CT imaging and biodistribution studies were performed 1 h after administration. Results: Tumor uptake (percentage injected dose per gram [%ID]) was not statistically different between groups, at 8.42 1.40 %ID in the 657 mg/kg group, 7.19 0.86 %ID in the 329 mg/kg group, 8.20 2.44 %ID in the 164 mg/kg group, and 8.67 1.97 %ID in the PBS group. Kidney uptake was significantly lower in the 657 mg/kg group (85.8 24.2 %ID) than in the 329 mg/kg (159 26.2 %ID), 164 mg/kg (211 27.4 %ID), and PBS groups (182 33.5 %ID) (P , 0.001). Salivary gland uptake was lower in the 657 mg/kg (3.72 2.12 %ID) and 329 mg/kg (5.74 0.62 %ID) groups than in the PBS group (10.04 2.52 %ID) (P , 0.01). Conclusion: MSG decreased salivary and kidney uptake of 68 Ga-PSMA-11 in a dose-dependent manner, whereas tumor uptake was unaffected.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.056
GPT teacher head0.380
Teacher spread0.324 · 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

Citations72
Published2018
Admission routes2
Has abstractyes

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