MétaCan
Menu
Back to cohort
Record W2310380485 · doi:10.1002/chem.201504532

In Situ Loading of Drugs into Mesoporous Silica SBA‐15

2016· article· en· W2310380485 on OpenAlexaff
Mi Mi Wan, Yan Yan Li, Tian Yang, Tao Zhang, Xiao Sun, Jian Hua Zhu

Bibliographic record

VenueChemistry - A European Journal · 2016
Typearticle
Languageen
FieldMaterials Science
TopicMesoporous Materials and Catalysis
Canadian institutionsMinistry of Education and Child Care
FundersNational Natural Science Foundation of China
KeywordsBiocompatibilityDrugDrug deliveryMesoporous silicaMesoporous materialHemolysisChemistryHeparinIn situPartial thromboplastin timeThrombin timeMaterials scienceBiomedical engineeringChemical engineeringPlateletNanotechnologyPharmacologyOrganic chemistryBiochemistryMedicineCatalysis

Abstract

fetched live from OpenAlex

In a new strategy for loading drugs into mesoporous silica, a hydrophilic (heparin) or hydrophobic drug (ibuprofen) is encapsulated directly in a one-pot synthesis by evaporation-induced self-assembly. In situ drug loading significantly cuts down the preparation time and dramatically increases the loaded amount and released fraction of the drug, and appropriate drug additives favor a mesoporous structure of the vessels. Drug loading was verified by FTIR spectroscopy and release tests, which revealed much longer release with a larger amount of heparin or ibuprofen compared to postloaded SBA-15. Besides, the in vitro anticoagulation properties of the released heparin and the biocompatibility of the vessels were carefully assessed, including activated partial thromboplastin time, thrombin time, hemolysis, platelet adhesion experiments, and the morphologies of red blood cells. A concept of new drug-release agents with soft core and hard shell is proposed and offers guidance for the design of novel drug-delivery systems.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.008
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.220
Teacher spread0.212 · 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.

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

Citations45
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueChemistry - A European JournalSame topicMesoporous Materials and CatalysisFrench-language works237,207