MétaCan
Menu
Back to cohort
Record W2006914648 · doi:10.2147/ijn.s31365

Liver cell specific targeting by the preS1 domain of hepatitis B virus surface antigen displayed on protein nanocages

2012· article· en· W2006914648 on OpenAlexaff
Masaharu Murata, Sayoko Narahara, Kaori Umezaki, Riki Toita, Shigekazu Tabata, Jing Shu Piao, Kana Abe, Jeong‐Hun Kang, Kenoki Oouchida, Lin Cui, Hashizume

Bibliographic record

VenueInternational Journal of Nanomedicine · 2012
Typearticle
Languageen
FieldMedicine
TopicHepatitis B Virus Studies
Canadian institutionsPancreas Centre (Canada)
Fundersnot available
KeywordsNanocagesPEGylationHepatocyteHeat shock proteinMaterials scienceHepatitis B virusIn vitroCytotoxicityChemistryCell biologyNanotechnologyBiophysicsVirusBiochemistryBiologyVirology

Abstract

fetched live from OpenAlex

Protein nanocages are self-organized complexes of oligomers whose three-dimensional architecture can been determined in detail. These structures possess nanoscale inner cavities into which a variety of molecules, including therapeutic or diagnostic agents, can be encapsulated. These properties yield these particles suitable for a new class of drug delivery carrier, or as a bioimaging reagent that might respond to biochemical signals in many different cellular processes. We report here the design, synthesis, and biological characterization of a hepatocyte-specific nanocage carrying small heat-shock protein. These nanoscale protein cages, with a targeting peptide composed of a preS1 derivative from the hepatitis B virus on their surfaces, were prepared by genetic engineering techniques. PreS1-carrying nanocages showed lower cytotoxicity and significantly higher specificity for human hepatocyte cell lines than other cell lines in vitro. These results suggested that small heat-shock protein-based nanocages present great potential for the development of effective targeted delivery of various agents to specific cells.

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)0.001
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 score0.197
Threshold uncertainty score0.487

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.014
GPT teacher head0.267
Teacher spread0.253 · 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

Citations24
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of NanomedicineSame topicHepatitis B Virus StudiesFrench-language works237,207