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Record W4319597444 · doi:10.3390/polym15040836

Recent Progress in Proteins-Based Micelles as Drug Delivery Carriers

2023· review· en· W4319597444 on OpenAlexaff
Aleena Mustafai, Muhammad Zubair, Ajaz Hussain, Aman Ullah

Bibliographic record

VenuePolymers · 2023
Typereview
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMicelleGelatinDrug deliveryBiocompatibilitySolubilizationTargeted drug deliveryDrugNanotechnologyDrug carrierChemistryMaterials sciencePharmacologyMedicineBiochemistryOrganic chemistryAqueous solution

Abstract

fetched live from OpenAlex

Proteins-derived polymeric micelles have gained attention and revolutionized the biomedical field. Proteins are considered a favorable choice for developing micelles because of their biocompatibility, harmlessness, greater blood circulation and solubilization of poorly soluble drugs. They exhibit great potential in drug delivery systems as capable of controlled loading, distribution and function of loaded agents to the targeted sites within the body. Protein micelles successfully cross biological barriers and can be incorporated into various formulation designs employed in biomedical applications. This review emphasizes the recent advances of protein-based polymeric micelles for drug delivery to targeted sites of various diseases. Most studied protein-based micelles such as soy, gelatin, casein and collagen are discussed in detail, and their applications are highlighted. Finally, the future perspectives and forthcoming challenges for protein-based polymeric micelles have been reviewed with anticipated further advances.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.034
GPT teacher head0.307
Teacher spread0.273 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations51
Published2023
Admission routes1
Has abstractyes

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