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Applications of Cellulose-based Materials in Sustained Drug Delivery Systems

2018· review· en· W2726037479 on OpenAlexaff
Bo Sun, M. Zhang, Jing Shen, Zhibin He, Pedram Fatehi, Yonghao Ni

Bibliographic record

VenueCurrent Medicinal Chemistry · 2018
Typereview
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsLakehead UniversityUniversity of New Brunswick
Fundersnot available
KeywordsCelluloseDrug deliverySolubilityDrugChemistryDrug carrierNanotechnologyMaterials scienceCombinatorial chemistryOrganic chemistryPharmacologyMedicine

Abstract

fetched live from OpenAlex

Bio-compatible, bio-degradable, and bio-available excipients are of critical interest for drug delivery systems. Cellulose and its derivative-based excipients have been well studied due to their green/natural and unique encapsulation/binding properties. They are often used in controlled/sustained drug delivery systems. In these applications, cellulose and its derivatives function generally can modify the solubility/gelling behavior of drugs, resulting in different mechanisms for controlling the release profiles of drugs. In this paper, the current knowledge in the structure and chemistry of conventional cellulose derivatives, and their applications in drug delivery systems are briefly reviewed. The development of innovative cellulose-based materials, including micro-cellulose (MC) and nano-cellulose (NC) in the applications of sustained drug delivery, is also discussed.

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)0.000
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.041
GPT teacher head0.359
Teacher spread0.318 · 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

Citations182
Published2018
Admission routes1
Has abstractyes

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