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Record W2321351135 · doi:10.1158/1538-7445.am2013-5613

Abstract 5613: Enhanced treatment of lung metastasis of triple negative breast cancer by doxorubicin-mitomycin C co-loaded polymer lipid nanoparticles.

2013· article· en· W2321351135 on OpenAlexaff
Preethy Prasad, Ping Cai, Andrew M. Rauth, Xiao Yu Wu

Bibliographic record

VenueCancer Research · 2013
Typearticle
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsOntario Institute for Cancer ResearchUniversity of Toronto
Fundersnot available
KeywordsBiodistributionDoxorubicinIn vivoMedicineTriple-negative breast cancerMetastatic breast cancerBioluminescence imagingMetastasisCancer researchBreast cancerCancerPathologyLung cancerEx vivoLymph nodeToxicityChemotherapyInternal medicineBiologyLuciferaseCell cultureTransfection

Abstract

fetched live from OpenAlex

Abstract Background: Treatment of triple negative breast cancer (TNBC) is a big challenge due to its aggressiveness, metastases, lack of therapeutic target and receptor proteins. Our laboratory has developed doxorubicin (Dox) -mitomycin C (MMC) co-loaded stealth polymer lipid hybrid nanoparticles (DMsPLN) and demonstrated their anti-cancer synergy in vitro and high efficacy in vivo. The purpose of this study is to evaluate the biodistribution, in vivo efficacy and safety of DMsPLN in a lung metastatic human TNBC model. Method: Lung metastasis of breast tumor was established using MDA-MB 231-luc-D3H2LN, a luciferase expressing cell line that was derived from spontaneous lymph node metastasis. The cells were injected via tail vein to develop lung metastasis in SCID mice. The biodistribution and tumor accumulation of the nanoparticles were examined by whole animal optical imaging using near infrared fluorescence labeled nanoparticles. The efficacy and systemic toxicity of DMsPLN were evaluated against clinically used doxorubicin (Dox). DMsPLN and free Dox solution were administered intravenously at various equivalent Dox doses. The tumor burden was monitored weekly by bioluminescence imaging. The size and number of tumor nodules in the lungs were examined at different time points and the systemic toxicity was monitored by repeated measurement of body weight. Results: A lung metastatic breast tumor model was successfully developed using the MDA-MB 231-luc-D3H2LN which allowed for non-destructive monitoring of tumor growth and metastases using bioluminescence imaging. Whole animal imaging demonstrated the accumulation of the fluorescent nanoparticles in the lung metastatic site. Treatment with free Dox (10 mg/kg) resulted in severe total body weight loss over 20% which is ruled as a toxic endpoint. However, the DMsPLN (10 mg/kg equivalent Dox dose) group did not show any systemic toxicity. Treatment with DMsPLN at various Dox doses resulted in a reduction in tumor burden compared to the saline and Dox-treated groups. Citation Format: Preethy Prasad, Ping Cai, Andrew Rauth, Xiao Wu. Enhanced treatment of lung metastasis of triple negative breast cancer by doxorubicin-mitomycin C co-loaded polymer lipid nanoparticles. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5613. doi:10.1158/1538-7445.AM2013-5613 Note: This abstract was not presented at the AACR Annual Meeting 2013 because the presenter was unable to attend.

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)0.000
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 score0.003

Distilled classifier scores by category (both heads)

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

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 head0.031
GPT teacher head0.334
Teacher spread0.303 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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