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Record W2141520688 · doi:10.1155/2012/673291

Nanobiotechnological Nanocapsules Containing Polyhemoglobin-Tyrosinase: Effects on Murine B16F10 Melanoma Cell Proliferation and Attachment

2012· article· en· W2141520688 on OpenAlexafffund
Yun Wang, Thomas Ming Swi Chang

Bibliographic record

VenueJournal of Skin Cancer · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topicmelanin and skin pigmentation
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsTyrosinaseNanocapsulesMelanomaCell growthMedicineCellPharmacologyChemistryCancer researchNanotechnologyBiochemistryMaterials scienceEnzyme

Abstract

fetched live from OpenAlex

We have reported previously that daily intravenous infusions of a soluble nanobiotechnological complex, polyhemoglobin-tyrosinase [polyHb-Tyr], can suppress the growth of murine B16F10 melanoma in a mouse model. In order to avoid the need for daily intravenous injections, we have now extended this further as follows. We have prepared two types of biodegradable nanocapsules containing [polyHb-Tyr]. One type is to increase the circulation time and decrease the frequency of injection and is based on polyethyleneglycol-polylactic acid (PEG-PLA) nanocapsules containing [polyHb-Tyr]. The other type is to allow for intratumoural or local injection and is based on polylactic acid (PLA) nanocapsules containing [polyHb-Tyr]. Cell culture studies show that it can inhibit the proliferation of murine B16F10 melanoma cells in the "proliferation model". It can also inhibit the attachment of murine B16F10 melanoma cells in the "attachment model." This could be due to the action of tyrosinase on the depletion of tyrosine or the toxic effect of tyrosine metabolites. The other component, polyhemoglobin (polyHb), plays a smaller role in nanocapsules containing [polyHb-Tyr], and this is most likely by its depletion of nitric oxide needed for melanoma cell growth.

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 score0.020
Threshold uncertainty score0.385

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 head0.008
GPT teacher head0.268
Teacher spread0.261 · 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

Citations20
Published2012
Admission routes2
Has abstractyes

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