Toward non-invasive diagnostics through AuNSs@Nano-MIP biosensor for sensitive lactoferrin detection in sweat
Bibliographic record
Abstract
Lactoferrin (LF) is implicated in a wide range of health conditions, including inflammatory disorders such as Inflammatory bowel disease (IBD), autoimmune pathologies, and various cancers. Here, we present a novel biomimetic biosensor for non-invasive sweat-based detection of LF. The biosensor incorporates gold nanostars (AuNSs, ∼80 nm), synthesized via a cost-effective one-step fabrication method. AuNSs' sharp tips, ∼10-20 nm, enhance the electroactive surface and electron transfer in the electrochemical biosensor. The sensor, composed of AuNSs and a molecularly imprinted polymer (MIP) layer, was validated by transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), and cyclic voltammetry (CV). In addition, a microfluidic patch was designed to collect sweat from three distinct body regions of four valountiers, yielding an average collection time of ∼15mins and a sweat volume ranging 20-100 μL. This biomimetic biosensor demonstrated reliable performance in complex biological matrices, providing a measurable electrochemical response to LF across a concentration range of 2-2000 ng/mL, with sensitivities of 2.54 μA mL/ng and 2.30 μA mL/ng in LF-spiked PBS and sweat, respectively. The biosensor achieved a limit of detection (LOD) of 1.74 ng/mL in LF-spiked PBS and 1.92 ng/mL in LF-spiked sweat. Additionally, the biosensor showed reproducibility across four independently fabricated devices, with relative standard deviation (RSD) values of <5 % at different concentrations. Overall, this study presents a step toward developing affordable, biomimetic wearable technologies for sweat-based detection of inflammatory biomarkers such as LF. By enabling non-invasive monitoring, this approach may offer valuable insights into disease recurrence and progression, with potential applications across a range of inflammatory and immune-related disorders.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".