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Record W2314980109 · doi:10.1158/1538-7445.am2011-554

Abstract 554: The impact of matrigel on the evaluation of nanomedicines in a mouse model of human breast adenocarcinoma

2011· article· en· W2314980109 on OpenAlexaff
Adam J. Shuhendler, Preethy Prasad, Kelvin Hui, Andrew M. Rauth, Jeffrey T. Henderson, Xiao Yu Wu

Bibliographic record

VenueCancer Research · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Research and Treatments
Canadian institutionsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMatrigelMedicineIndocyanine greenBreast cancerPathologyCancerPerfusionCancer researchBiomedical engineeringInternal medicineAngiogenesis

Abstract

fetched live from OpenAlex

Abstract There exists a disparity in the literature on the use of matrigel, a mouse tumor derived basement protein scaffold solution, in the establishment of orthotopic mouse models of human breast cancer. Whether or not a tumor model requires matrigel is rarely, if ever, discussed, and whether or not matrigel has any confounding effects on the assessment of nanomedicines in these models has not been determined. To this end, the effects of matrigel on the assessment of a potential nanoparticle carrier in an orthotopic model of human breast cancer was evaluated. Bilateral human enhanced green fluorescent protein-expressing MDA435 breast tumors were established in the inguinal mammary fat pads of Nude mice with (left side, M+) or without (right side M-) co-injection of Matrigel. To determine if M- or M+ breast tumors resulted in altered retention of near-infrared emitting solid lipid nanoparticles (SLN), SLN were injected intratumorally and the tumor-retained near-infrared fluorescence was tracked over time. Lymphatic flow was tracked in a caudal-to-rostral direction through the subcutaneous injection of indocyanine green into the tail interstitium lateral to the ventral caudal artery. Tumor and lymph node morphologies were assessed with high frequency ultrasound imaging, and local perfusion of these structures was measured with destruction-replenishment of microbubbles. There was a significant difference in the retention time of SLN within M+ and M- tumors, with the retention of SLN in M- tumors beyond 6 hours but near total SLN clearance from M+ tumors within 2 hours of administration. Anatomical interrogation of the M+ and M- tumor growth with fluorescence imaging revealed occlusion and overgrowth of the inguinal lymph nodes by M+ tumors, whereas M- tumors neither occluded nor overgrew the lymph node, but rather grew in its periphery. Corroboration of these findings was achieved using ultrasound imaging, demonstrating the reduction of lymph node area by the overgrowing M+ tumors that was not seen with M- tumors. M+ and M- tumors also differed physiologically both in lymphatic flow rates and lymph node blood perfusion rates, where the M+ associated lymphatics showed significantly greater lymphatic flow and lymph node perfusion relative to the M- associated lymphatics. The alteration of both anatomical and physiological parameters of lymphatic flow and lymph node perfusion by M+ orthotopic breast tumors relative to M- tumors and the effect of these alterations on the performance (i.e. retention) of nanomedicines warrant scrutiny of the use of matrigel in the establishment of tumor models for nanomedicine evaluation. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 554. doi:10.1158/1538-7445.AM2011-554

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)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.239
GPT teacher head0.469
Teacher spread0.230 · 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
Published2011
Admission routes1
Has abstractyes

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