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Antitumor Efficacy of Photodynamic Therapy Using Novel Nanoformulations of Hypocrellin Photosensitizer SL052

2011· article· en· W2126787644 on OpenAlexaff
Mladen Korbelik, Ragupathy Madiyalakan, Thomas Woo, Azita Haddadi

Bibliographic record

VenuePhotochemistry and Photobiology · 2011
Typearticle
Languageen
FieldMedicine
TopicPhotodynamic Therapy Research Studies
Canadian institutionsUniversity of SaskatchewanQuest University CanadaBC Cancer Agency
Fundersnot available
KeywordsPhotodynamic therapyPhotosensitizerPLGAPolyvinylpyrrolidoneGlycolic acidChemistryDrugPharmacologyNanoparticleMedicineNanotechnologyMaterials scienceLactic acidPhotochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Recent preclinical and clinical testing of hypocrellin-based photosensitizer SL052 for use in photodynamic therapy (PDT) of cancer has shown encouraging results. Further optimization of its formulation for delivery could considerably extend the therapeutic efficiency of this drug. A nanoformulation encapsulating SL052 into biodegradable polymer poly(lactic-co-glycolic acid) (PLGA) was developed using a single-emulsion solvent evaporation technique and characterized in terms of particle size and loading of the photosensitizing agent. This nanoformulation, SL052-PLGA-nanoparticles (NPs), was compared with recently created nanoformulation based on polyvinylpyrrolidone (SL052-PVP-NPs) and standard liposomal SL052 preparation in terms of efficacy when used for PDT treatment of squamous cell carcinomas SCCVII growing subcutaneously in syngeneic mice. The therapeutic effect of PDT using these three different SL052 formulations was tested for both 1 and 4 h intervals between drug injection and tumor light exposure. The longer time interval produced higher tumor cure rates with all SL052 preparations. With both drug-light intervals, PDT based on SL052-PLGA-NPs produced superior therapeutic benefit compared with the other two SL052 formulations.

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 score0.012
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.065
GPT teacher head0.322
Teacher spread0.257 · 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

Citations19
Published2011
Admission routes1
Has abstractyes

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