Wearable Aptalyzer Integrates Microneedle and Electrochemical Sensing for In Vivo Monitoring of Glucose and Lactate in Live Animals
Bibliographic record
Abstract
Continuous monitoring of clinically relevant biomarkers within the interstitial fluid (ISF) using microneedle (MN)-based assays, has the potential to transform healthcare. This study introduces the Wearable Aptalyzer, an integrated system fabricated by combining biocompatible hydrogel MN arrays for ISF extraction with an electrochemical aptamer-based biosensor for in situ monitoring of blood analytes. The use of aptamers enables continuous monitoring of a wide range of analytes, beyond what is possible with enzymatic monitoring. The Wearable Aptalyzer is used for real-time and multiplexed monitoring of glucose and lactate in ISF. Validation experiments using live mice and rat models of type 1 diabetes demonstrate strong correlation between the measurements collected from the Wearable Aptalyzer in ISF and those obtained from gold-standard techniques for blood glucose and lactate, for each analyte alone and in combination. The Wearable Aptalyzer effectively addresses the limitations inherent in enzymatic detection methods as well as solid MN biosensors and the need for reliable and multiplexed bioanalytical monitoring in vivo.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".