Microneedle Assays for Continuous Health Monitoring: Challenges and Solutions
Bibliographic record
Abstract
Continuous health monitoring aims to reduce hospitalization and the need for constant supervision of the patients. For an outpatient monitoring device to be effective, it must meet certain criteria: it should demand minimal patient involvement, be reliable, be connected, remain stable with infrequent replacements, be cost-efficient, be compatible with humans, and ultimately be self-powered. Microneedle (MN) technology, designed for transdermal biosensing, offers a promising solution for meeting a wide range of these demands in the field of continuous health monitoring. A variety of MN platforms have been developed to facilitate this crucial function. Our focus in this Perspective is on the significant challenges linked to MN-based biosensors. These challenges include ensuring skin compatibility, the effective integration of biorecognition elements into the MN systems, and the durability concerns of these sensors in enabling extended periods of continuous monitoring. Tackling these hurdles could pave the way for more effective and reliable MN-based health monitoring solutions in the future.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| 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".