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Record W2105966918 · doi:10.1002/ijch.201100054

Synthesis and Development of Lipoprotein‐Based Nanocarriers for Light‐Activated Theranostics

2012· article· en· W2105966918 on OpenAlexaff
Honglin Jin, Juan Chen, Jonathan F. Lovell, Zhihong Zhang, Gang Zheng

Bibliographic record

VenueIsrael Journal of Chemistry · 2012
Typearticle
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsUniversity Health NetworkUniversity of TorontoOntario Institute for Cancer Research
Fundersnot available
KeywordsNanocarriersChemistryBiocompatible materialNanotechnologyPhotodynamic therapyDrug deliveryBiomedical engineeringOrganic chemistryMaterials science

Abstract

fetched live from OpenAlex

Abstract The design and synthesis of new light‐activated contrast agents for theranostics (therapy/diagnosis) has the potential to facilitate multifunctional and improved personalized medicine. The use of light as a remote activation strategy provides spatial and temporal control of drug effect and nanotechnology can play a key role in this process. Lipoproteins (LDL and HDL), which transport water‐insoluble cholesteryl esters and triacylglycerols in nature, have evolved to efficiently ferry exogenous hydrophobic compounds in vivo. They are naturally biocompatible, maneuverable due to their small size (<30 nm), and can be loaded through various methods, and are therefore ideal vehicles to load and transport hydrophobic theranostic agents. This review examines the history and ongoing research activities regarding the design and synthesis of lipoprotein‐based formulations, and their applications or potential applications as light‐activated theranostic agents, with a main focus on photodynamic therapy. This field, while still in its infancy, will benefit from improved design and modulation of enhanced lipoprotein‐based nanocarriers, with the ultimate goal of simultaneous imaging and photoactivation of therapeutic agents in a clinical setting.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.011
GPT teacher head0.212
Teacher spread0.200 · 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

Citations7
Published2012
Admission routes1
Has abstractyes

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