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Record W3043458950 · doi:10.1007/s40820-020-00490-6

Self-Assembly Protein Superstructures as a Powerful Chemodynamic Therapy Nanoagent for Glioblastoma Treatment

2020· article· en· W3043458950 on OpenAlex

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

VenueNano-Micro Letters · 2020
Typearticle
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsMinistry of Education and Child Care
FundersChina Scholarship CouncilVillum Fonden
KeywordsNanocarriersGlioblastomaBiocompatibilityReactive oxygen speciesIn vivoCytotoxicityDrug deliveryCancer cellIn vitroRed blood cellCancer researchCell biologyBiophysicsChemistryMaterials scienceBiochemistryCancerBiologyNanotechnology

Abstract

fetched live from OpenAlex

Glioblastoma (GBM) remains a formidable challenge in oncology. Chemodynamic therapy (CDT) that triggers tumor cell death by reactive oxygen species (ROS) could open up a new door for GBM treatment. Herein, we report a novel CDT nanoagent. Hemoglobin (Hb) and glucose oxidase (GOx) were employed as powerful CDT catalysts. Instead of encapsulating the proteins in drug delivery nanocarriers, we formulate multimeric superstructures as self-delivery entities by crosslinking techniques. Red blood cell (RBC) membranes are camouflaged on the protein superstructures to promote the delivery across blood-brain barrier. The as-prepared RBC@Hb@GOx nanoparticles (NPs) offer superior biocompatibility, simplified structure, and high accumulation at the tumor site. We successfully demonstrated that the NPs could efficiently produce toxic ROS to kill U87MG cancer cells in vitro and inhibit the growth of GBM tumor in vivo, suggesting that the new CDT nanoagent holds great promise for treating GBM.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.057
Threshold uncertainty score1.000

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 head0.206
Teacher spread0.198 · 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