High throughput fabrication of robust solid microneedles
Bibliographic record
Abstract
Needles are a key, and very common, component of modern medicine, used primarily for drug delivery and blood withdrawals. There are, however, many drawbacks to their use, such as insertion pain, tissue damage, and the development of fears and avoidance of medical care, especially in younger patients. Needle phobia (extreme fear of needles associated with avoidance) affects 1 in 10 people, who are then likely to avoid seeking any medical care. In addition, there are significant populations living with medical conditions, such as diabetes, that require multiple daily injections for effective management of their chronic health condition. Microneedles are small needles less than 1 mm in length that penetrate the skin with minimal or no pain. Microneedles can also reduce tissue damage that can lead to scarification and localized drug resistance in high frequency injection sites. By using high accuracy automated microfabrication techniques, we have developed a new method of quickly and effectively making microneedle arrays capable of interfacing with existing technologies, such as insulin pens and traditional syringes. This work shows a microneedle system which is inexpensive to mass fabricate and preliminary results point to minimal patient pain compared to other microneedle devices. The microneedle construction from a thin metal wire means there is minimal risk of fracture and deposition of material in the dermis that traditional polymeric or silicon microneedles face. This work presents the basis for a pain free injection system that will have significant impacts on patient health, both physical and mental, and healthcare system costs.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".