MétaCan
Menu
Back to cohort
Record W2185544990 · doi:10.1002/smll.201503121

Hollow Mesoporous Silica Nanocarriers with Multifunctional Capping Agents for In Vivo Cancer Imaging and Therapy

2015· article· en· W2185544990 on OpenAlexaff
Shun Yang, Dongyun Chen, Najun Li, Qingfeng Xu, Hua Li, Frank Gu, Jianping Xie, Jianmei Lu

Bibliographic record

VenueSmall · 2015
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversity of Waterloo
FundersNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsNanocarriersMesoporous silicaDrug deliveryNanotechnologyMaterials scienceAmphiphileIn vivoDrugDrug carrierNanoparticleBiocompatible materialTargeted drug deliveryMesoporous materialChemistryBiomedical engineeringOrganic chemistryPharmacologyCopolymerPolymerCatalysisMedicine

Abstract

fetched live from OpenAlex

Efficient drug loading and selectivity in drug delivery are two key features of a good drug-carrier design. Here we report on such a drug carrier formed by using hollow mesoporous silica nanoparticles (HMS NPs) as the core and specifically designed multifunctional amphiphilic agents as the encapsulating shell. These nanocarriers combine the advantages of the HMS NP core (favorable physical and structural properties) and the versatility of an organic-based shell (e.g., specificity in chemical properties and modifiability). Moreover, both the properties of the core and the shell can be independently varied. The varied core and shell could then be integrated into a single device (drug carrier) to provide efficient and specific drug delivery. In vitro and in vivo data suggests that these drug nanocarriers are biocompatible and are able to deliver hydrophobic drugs selectively to target tumor cells. After the break of the pH-labile linkages in the shell, the drug payload can be released and the tumor cells are killed.

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 score0.057
Threshold uncertainty score0.400

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 head0.036
GPT teacher head0.262
Teacher spread0.226 · 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

Citations47
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueSmallSame topicNanoparticle-Based Drug DeliveryFrench-language works237,207