Remote‐Controlled Sensing and Drug Delivery via 3D‐Printed Hollow Microneedles
Bibliographic record
Abstract
Remote health monitoring and treatment serve as critical drivers for advancing health equity, bridging geographical and socioeconomic disparities, ensuring equitable access to quality healthcare for those in underserved or remote regions. By democratizing healthcare, this approach offers timely interventions, continuous monitoring, and personalized care independent of one's location or socioeconomic status, thereby striving for an equitable distribution of health resources and outcomes. Meanwhile, microneedle arrays (MNAs), revolutionize painless and minimally invasive access to interstitial fluid for drug delivery and diagnostics. This paper introduces an integrated theranostic MNA system employing an array of colorimetric sensors to quantitatively measure -pH, glucose, and lactate, alongside a remotely-triggered system enabling on-demand drug delivery. Integration of an ultrasonic atomizer streamlines the drug delivery, facilitating rapid, pumpless, and point-of-care drug delivery, enhancing system portability while reducing complexities. An accompanying smartphone application interfaces the sensing and drug delivery components. Demonstrated capabilities include detecting pH (3 to 8), glucose (up to 16 mm), and lactate (up to 1.6 mm), showcasing on-demand drug delivery, and assessing delivery system performance via a scratch assay. This innovative approach confronts drug delivery challenges, particularly in managing chronic diseases requiring long-term treatment, while also offering avenues for non-invasive health monitoring through microneedle-based sensors.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".