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Record W2243110723

Comparison of radiolabeled somatostatin receptor agonist and antagonists in a mouse model of human breast cancer

2014· article· en· W2243110723 on OpenAlexaff
Maral Pourghiasian, Zhengxing Zhang, Navjit Hundal-Jabal, Kuo‐Shyan Lin, François Bénard

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsSomatostatin receptorDOTAAgonistIn vivoChemistryBreast cancerRadioligandReceptorSomatostatinPancreasImaging agentBiodistributionSomatostatin receptor 2MCF-7In vitroCell cultureCancer researchCancerNuclear medicineMedicineInternal medicineHuman breastBiologyBiochemistry
DOInot available

Abstract

fetched live from OpenAlex

1051 Objectives High expression of somatostatin receptor subtype 2a has been shown in a various malignancies including neuroendocrine tumors and breast cancer. Most of reported studies for imaging SST2a expression used mice bearing tumors derived from SST2a transfected cell lines and rat pancreatic cancer cell line (AR42J). The objective of this study was to compare and evaluate 68Ga-DOTA-TATE, 68Ga-NOTA-BASS and 68Ga-NODAGA-LM3 as potent SSt2a agonist and antagonists in a murine model of human breast cancer. Methods 68Ga labeling was performed in HEPES buffer (pH 5.1) via microwave heating for 1 min, followed by HPLC purification. In vitro binding assays were performed using human breast cancer (Zr-75-1) membranes. ZR-75-1 cells were inoculated subcutaneously in NSG mice and dynamic PET/CT imaging and biodistribution studies were carried 1h post injection. Blocking studies were performed to determine the specificity of radioligand to the receptors. Results The radiolabeling yield and purity were more than 99%. Ga-DOTA-TATE showed a high binding affinity of 0.6 nM compared to two other peptides (68Ga-NOTA-BASS 158 nM; 68Ga-NODAGA-LM3 10.7 nM). In vivo studies showed low tumor uptake in 68Ga-NOTA-BASS. Excellent tumor visualization with low background at 1 h post-injection were observed with 68Ga-DOTA-TATE and 68Ga-NODAGA-LM3 with %ID/g of 21 ± 4.2 and 24.4 ± 7.8 respectively. %ID/g of pancreas was higher (31.3 ± 19%) in 68Ga-DOTA-TATE. In blocking studies, the uptake in tumors and pancreas were reduced by >90%. Tumor-to-blood and Tumor-to-muscle ratios were 33 and 52%ID/g respectively with 68Ga-DOTA-TATE and 30 and 41%ID/g with 68Ga-NODAGA-LM3. Conclusions A high tumor to background ratio was achieved in Zr-75-1 tumor model with both 68Ga-DOTA-TATE and 68Ga-NODAGA-LM3 imaging. Despite similar absolute tumor uptake, the tumor-to-muscle ratio of 68Ga-DOTA-TATE was higher than 68Ga-NODAGA-LM3. The ZR-75 human tumor cell line is a promising breast cancer model for SST imaging by PET.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.042
GPT teacher head0.384
Teacher spread0.342 · 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
Published2014
Admission routes1
Has abstractyes

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