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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 OpenAlexaff
Tao Zheng, Wentao Wang, Jon Ashley, Ming Zhang, Xiaotong Feng, Jian Shen, Yi Sun

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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

Citations44
Published2020
Admission routes1
Has abstractyes

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