Engineering Size and Structure of Particles in Novel Modified-Release Delivery Systems
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".