Biopolymer nanoparticle production for controlled release of biopharmaceuticals
Bibliographic record
Abstract
For drug applications, nanoparticles, used as drug carriers, offer the advantage of controlled release, therapeutic impact and targeted delivery. In drug delivery applications, biodegradable polymers can be extracted from natural sources or prepared synthetically by polymerization. Natural polymers typically have varying compositions and physiochemical properties. As a result, methods which utilize natural polymers to encapsulate drugs are more varied and polymer dependent. The following polymers are discussed in this review article: alginate, chitosan, gelatin, albumin, gliadin, pullulan, and dextran. Specialized encapsulation nanotechnologies will be discussed such as ionotropic gelation, complexation, the reverse microemulsion technique, cross-linking methods, emulsion-dependent methods, desolvation methods and self-assembly methods. For each biopolymer an overview of the structure is presented with the corresponding encapsulation techniques. Understanding the structure of the biopolymer is important as to not only understand the rational for current encapsulation techniques but to continue to develop new encapsulation techniques in pursuit of the ideal drug carrier for application in therapeutic treatments.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".