A Universal Hydrogel Microneedle for on-Site Detection of Small Molecules, Proteins, and Ribonucleic Acids
Bibliographic record
Abstract
Point-of-care testing (POCT) of clinical biomarkers is critical to health monitoring and timely treatment, yet biosensing assays capable of detecting biomarkers without the need for costly external equipment and reagents are limited. Blood-based assays are, specifically, challenging as blood collection is invasive, and several processing steps are required. A promising alternative to blood is interstitial fluid (ISF), which originates from blood and fills the extracellular space; thus, it has common biomarkers with plasma/serum while also containing biomarkers unique to the local cells. A wide range of metabolites, including amino acids, lipids, nucleotides, and protein biomarkers, can be detected in ISF, emphasizing the potential of ISF for health monitoring. Microneedle (MN)-based biosensors have recently emerged as a promising approach for investigating the ISF. They enable minimally invasive penetration through the skin for ISF access. Specifically, Hydrogel-based MNs have two main advantages; i) their fabrication is cost-effective and simple, ii) they are highly biocompatible. Therefore, they overcome the challenges with conventional solid MNs. However, Hydrogel Microneedles (HMN) capable of both extracting ISF and sensing an analyte on-microneedle (without any need for post-sampling processing) have been limited. Here, we report a versatile assay that employs HMNs to both extract (ISF) and perform on-MN sensing of a specific analyte/biomarker in a minimally invasive manner. Our assay incorporates graphene oxide-nucleic acid (GO.NA) optical sensors for sensing. The GO.NA optical sensor consists of GO nanosheets conjugated to fluorophore-modified nucleic acid (NA), in which GO acts as a quencher. Single-stranded NAs have high affinity for binding to GO, therefore, in the absence of the biomarker of interest the NAs are tightly bound to GO; in turn, their fluorophore tag is quenched. However, in the presence of a specific biomarker, the NAs bind to their target, inducing a conformational change that distances the fluorophore tag from GO, leading to fluorescence recovery and generation of an optical signal. GO also acts as a substrate for NA immobilization; therefore, it protects the NA from degradation and prevents any potential NA release from HMN-GO.NA. To enable microscope-free patch visualization and optical measurements for POCT, we developed a miniaturized smartphone-based system that captures fluorescence images of the HMN-GO.NA patches (Scheme 1A and 1B), which are then analyzed using freely available software (ImageJ). Our system could successfully measure six clinically important biomarkers (glucose, uric acid, insulin, and serotonin as well as microribonucleic acid 210 and 21) in-vitro (using phantom gel) and ex-vivo (using porcine skin). Further, we have been able to accurately detect glucose and uric acid in live diabetic animal models, proving the efficacy of our system. Figure 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".