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Record W2114018465 · doi:10.2217/17435889.2.3.375

Mimicking Nature‘S Nanocarrier: Synthetic Low-Density Lipoprotein-Like Nanoparticles for Cancer-Drug Delivery

2007· letter· en· W2114018465 on OpenAlex
Ian R. Corbin, Gang Zheng

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNanomedicine · 2007
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsOntario Institute for Cancer Research
Fundersnot available
KeywordsNanocarriersDrug deliveryNanotechnologyNanoparticleDrugTargeted drug deliveryLow-density lipoproteinMaterials scienceChemistryPharmacologyMedicineCholesterolBiochemistry

Abstract

fetched live from OpenAlex

Evaluation of: Nikanjam M, Blakely EA, Bjornstad KA, Shu X, Budinger TF, Forte TK: Synthetic nano-low density lipoprotein as targeted drug delivery vehicle for glioblastoma multiforme. Int. J. Pharm. 3287, 86–94 (2007) [1]. Low-density lipoproteins have long been recognized as a viable nanocarrier for targeted delivery of drug and imaging agents. Many groups have published promising initial findings; however, progress in this field has been impeded by the need to isolate low-density lipoproteins from fresh donor plasma. In a recent paper by Nikanjam and colleagues, synthetic low-density lipoprotein-like nanoparticles were prepared from commercial lipids and a bifunctional synthetic peptide containing the low-density lipoprotein receptor-binding domain and the lipid-binding motif. These particles were shown to behave similarly to native low-density lipoproteins and also to bind to the low-density lipoprotein receptor on cancer cells. Herein, we evaluate the utility of this novel delivery vehicle and discuss what role this technology may have in nanomedicine.

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.

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 categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.496
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.001
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 head0.008
GPT teacher head0.251
Teacher spread0.243 · 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