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Record W2941674411 · doi:10.18433/jpps30253

Engineering Size and Structure of Particles in Novel Modified-Release Delivery Systems

2019· review· en· W2941674411 on OpenAlexvenueno aff
Qianqian Dong, Xiaoming Zheng, Miaomiao Zhou, Fei Wu, Yanlong Hong, Xiao Lin, Lan Shen

Bibliographic record

VenueJournal of Pharmacy & Pharmaceutical Sciences · 2019
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicDrug Solubulity and Delivery Systems
Canadian institutionsnot available
FundersShanghai University of Traditional Chinese MedicineNatural Science Foundation of Shanghai
KeywordsTabletingCoatingNanotechnologyDrug deliveryMaterials scienceBiochemical engineeringComputer scienceComposite materialEngineering

Abstract

fetched live from OpenAlex

Particle engineering has become a hot topic in the field of modified-release delivery systems during last decades. It has a wide range of pharmaceutical applications and is a bridge linking between drugs and drug delivery systems. Particles are an important part of many dosage forms and viewed as a carrier of drugs. Their size, shape, crystalline form, and structure directly affect the stability and releasing pattern of drugs. Engineering size or modifying particles by forming porous, core-shell, or skeleton structures can realize the development and utilization of functionally modified release systems (including fast-release systems, sustained-release systems, and targeted-release systems). However, there are certain problems in the practical application, such as bitter taste and coating damage. Combining with some polymer or lipid materials to form core-shell or embedded structures is considered as the key to taste masking. And, using cushioning agents is proven to be effective in preserving the integrity of the functional coating film of multiparticulates during tableting. To sum up, this review, from a particle engineering point, expounds the influence of different factors on the functionality of particles and offers some useful comments and suggestions for industry personnel.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.938
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.270
GPT teacher head0.479
Teacher spread0.209 · 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 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

Citations7
Published2019
Admission routes1
Has abstractyes

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