Recent advances and challenges: Translational research of minimally invasive wearable biochemical sensors
Bibliographic record
Abstract
Wearable diagnostic devices that continuously monitor biomarkers of interest can significantly improve disease outcome. However currently, there is a lack of commercially available wearable biochemical health devices. Many of the commercially available devices are limited to monitoring the body's physical parameters. Even though we have seen progress in the development of minimally invasive wearable biochemical sensors (WBS), many challenges hinder their real-life implementation. In this review, we focus on studies that have evaluated the sensor's performance in vivo, classified as level 4 of the technology readiness level (TRL), to identify and understand the important technological factors and strategies towards the actualization of wearable biosensors. We first discuss recent progresses of WBS categorized by the transducers and target biofluid. Through comparative analysis, we show that sensor performance depends highly on the sensing element design such that the choice of bioreceptor, incorporation of nanomaterial, and surface area modifications have a profound effect on sensitivity, linear range, and stability. We observed that correctional analysis may be required to account the effect of external factors that can influence the sensor performance. Furthermore, translating in vitro sensor characterization is successful when monitoring simple biofluids, but personalized calibration that correlates the sensor response to a reference technique was observed to be the best method to accurately determine biomarker concentrations. By focusing on studies with validated in vivo experiments (compared to standard techniques), we assess how various design strategies influence the WBS’ in vivo performance.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".