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Record W4392976755 · doi:10.1016/j.biopha.2024.116446

Innovative gelatin-based micelles with AS1411 aptamer targeting and reduction responsiveness for doxorubicin delivery in tumor therapy

2024· article· en· W4392976755 on OpenAlexafffund
Jingmou Yu, Yifei Zhang, Meilin Xu, Dengzhao Jiang, Wenbo Liu, Hongguang Jin, Pu Chen, Jing Xu, Lei Zhang

Bibliographic record

VenueBiomedicine & Pharmacotherapy · 2024
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversity of Waterloo
FundersHealth Commission of Jiangxi ProvinceChina Scholarship CouncilMitacs
KeywordsDoxorubicinMicelleGelatinAptamerCytotoxicityDrug deliveryPharmacologyNucleolinIn vitroTargeted drug deliveryChemistryIntracellularHeLaA549 cellBiophysicsCancer researchChemotherapyBiochemistryMolecular biologyMedicineBiologyInternal medicineOrganic chemistry

Abstract

fetched live from OpenAlex

Herein, we constructed innovative reduction-sensitive and targeted gelatin-based micelles for doxorubicin (DOX) delivery in tumor therapy. AS1411 aptamer-modified gelatin-ss-tocopherol succinate (AGSST) and the control GSST without AS1411 modification were synthesized and characterized. Antitumor drug DOX-containing AGSST (AGSST-D) and GSST-D nanoparticles were prepared, and their shapes were almost spherical. Reduction-responsive characteristics of DOX release in vitro were revealed in AGSST-D and GSST-D. Compared with non-targeted GSST-D, AGSST-D demonstrated better intracellular uptake and stronger cytotoxicity against nucleolin-overexpressed A549 cells. Importantly, AGSST-D micelles showed more effective killing activity in A549-bearing mice than GSST-D and DOX⋅HCl. It was revealed that AGSST-D micelles had no obvious systemic toxicity. Overall, AGSST micelles would have the potential to be an effective drug carrier for targeted tumor therapy.

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 score0.018
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.018
GPT teacher head0.296
Teacher spread0.277 · 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

Citations8
Published2024
Admission routes2
Has abstractyes

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