Microneedle arrays for brain drug delivery: the potential of additive manufacturing
Bibliographic record
Abstract
For a long time, the treatment of brain diseases has been a significant challenge. Drug delivery to the brain has recently become one of the most challenging problems for patients with severe forms of central nervous system diseases. The blood–brain barrier (BBB) poses a significant challenge for drug delivery to the brain. While extensive efforts focus on finding materials to overcome the BBB for brain tumor treatment, it limits the penetration of chemotherapeutic drugs for the broader treatment of brain diseases. The oral method of drug administration has several drawbacks, such as the loss of drugs because of metabolism and gastrointestinal environmental issues. Besides, using the intravenous route to administer medicines has several disadvantages, including discomfort at the injection site, infection, bleeding, anxiety, and incompetence toward patients. Fabrication and development of microneedles to overcome the drawbacks mentioned above of traditional drug delivery methods may be a viable alternative. Drug delivery using microneedle arrays (MNAs) has recently been shown to be an effective method for delivering drugs to the brain. Different fabricating methods like three-dimensional printing could be used for the fabrication of personalized drug delivery systems, like MNAs, with precise control over spatiotemporal drug distribution. This article presents a review of using MNAs for drug delivery to the brain.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".