Microfabrication for Drug Delivery
Bibliographic record
Abstract
This review is devoted to discussing the application of microfabrication technologies to target challenges encountered in life processes by the development of drug delivery systems. Recently, microfabrication has been largely applied to solve health and pharmaceutical science issues. In particular, fabrication methods along with compatible materials have been successfully designed to produce multifunctional, highly effective drug delivery systems. Microfabrication offers unique tools that can tackle problems in this field, such as ease of mass production with high quality control and low cost, complexity of architecture design and a broad range of materials. Presented is an overview of silicon- and polymer-based fabrication methods that are key in the production of microfabricated drug delivery systems. Moreover, the efforts focused on studying the biocompatibility of materials used in microfabrication are analyzed. Finally, this review discusses representative ways microfabrication has been employed to develop systems delivering drugs through the transdermal and oral route, and to improve drug eluting implants. Additionally, microfabricated vaccine delivery systems are presented due to the great impact they can have in obtaining a cold chain-free vaccine, with long-term stability. Microfabrication will continue to offer new, alternative solutions for the development of smart, advanced drug delivery systems.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".