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Record W4404764582 · doi:10.1021/acs.nanolett.4c04147

Hydrophilic Metal–Organic Frameworks Regulated by Biomineralized Protein for Enhanced Stability and Drug Delivery

2024· article· en· W4404764582 on OpenAlexaff
Sajid Ur Rehman, Shuai Kong, Jing Zhang, Haining Xia, Ruiguo Chen, Zeyong Guo, Zehua Li, Rida Ahmed, Abbas Rehman, Hossein Kazemian, Yan-Yi Jiang, Shuai Xu, Kun Ma, Junfeng Wang

Bibliographic record

VenueNano Letters · 2024
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversity of Northern British Columbia
FundersHigh-end Foreign Experts Recruitment Plan of ChinaAlliance of International Science OrganizationsNational Natural Science Foundation of China
KeywordsDrug deliveryMetal-organic frameworkNanotechnologyDrugMaterials scienceMetalChemistryProtein stabilityChemical engineeringOrganic chemistryPharmacologyMetallurgyAdsorptionBiochemistry

Abstract

fetched live from OpenAlex

For bridging the gap between biological and synthetic materials, the fusion of Metal-Organic Frameworks (MOFs) with biological entities has emerged as a revolutionary strategy in functional materials. In this context, our study introduces a novel structure wherein Bovine Serum Albumin (BSA), a robust and versatile protein, encapsulates zeolitic imidazolate framework-8 (ZIF-8), forming a protein-caged MOF. Highlighting the advantages of this innovative design, the protein-encapsulation enhances the stability and dispersity of ZIF-8, and aids in the synthesis of smaller-sized nanoparticles, crucial for size-impact performance applications. Additionally, the BSA-caged ZIF-8 structure showcases potential in drug delivery applications, especially in the controlled delivery of chemotherapeutic drugs. The study thus elucidates the multifaceted applicability of this novel structure, marking a significant stride in the convergence of biological and synthetic materials.

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 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 score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.220
Teacher spread0.213 · 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

Citations19
Published2024
Admission routes1
Has abstractyes

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