MétaCan
Menu
Back to cohort
Record W4386573971 · doi:10.1016/j.dib.2023.109545

Data demonstrating the in vivo anti-tumor efficacy of thermosensitive liposome formulations of a drug combination in pre-clinical models of breast cancer

2023· article· en· W4386573971 on OpenAlexafffund
Xuehan Wang, Maximilian Regenold, Michael Dunne, Pauric Bannigan, Christine Allen

Bibliographic record

VenueData in Brief · 2023
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchUniversity of Toronto
KeywordsIn vivoDoxorubicinLiposomeBreast cancerChemotherapyPharmacologyMedicineDrug deliveryCancerIn vitroCancer researchHyperthermiaBreast cancer chemotherapyChemistryInternal medicineBiologyBiochemistry

Abstract

fetched live from OpenAlex

Thermosensitive liposomes in combination with localized mild hyperthermia can improve the delivery of drug to solid tumor sites. For this reason, thermosensitive liposome formulations of a range of chemotherapy drugs have been designed. Our group previously developed and characterized a thermosensitive liposome formulation of the heat shock protein 90 inhibitor alvespimycin as a companion therapeutic to a thermosensitive liposome formulation equivalent in composition to ThermoDox (i.e., ThermoDXR), with the goal of increasing the therapeutic index of doxorubicin as the combination was revealed to be highly synergistic in a panel of human breast cancer cell lines including MDA-MB-231 (Dunne et al., 2019). The data presented here further describes the effect of the doxorubicin (DXR) and alvespimycin (ALV) combination in vitro and in vivo. Specifically, the combination effect in mouse breast cancer 4T1 cells and the in vivo efficacy of this heat-activated chemotherapy combination in both immunocompromised (MDA-MB-231 tumor bearing female SCID mice) and immunocompetent (4T1 tumor bearing female BALB/c mice) models of breast cancer.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.802
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.068
GPT teacher head0.345
Teacher spread0.277 · 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 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

Citations2
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueData in BriefSame topicNanoparticle-Based Drug DeliveryFrench-language works237,207