Microneedles From Shape‐Preserving Crosslinked Poly(Vinyl Alcohol) Hydrogels: Minimising Interference in Transdermal Proteomics
Bibliographic record
Abstract
Abstract Hydrogel‐forming microneedle array patches (HFMAPs) enable minimally invasive interstitial fluid (ISF) sampling for biomarker detection. However, optimising their formulation to enhance biomarker recovery and minimise analytical interference while maintaining mechanical properties remains a challenge. This study presents interference‐free HFMAPs fabricated from shape‐preserving polyvinyl alcohol and polyvinyl pyrrolidone (PVA‐PVP) hydrogel. Two formulations are developed with PVA‐PVP without chitosan (PP) and with chitosan (PPChi) using micromoulding and evaluated for mechanical strength, insertion efficiency, ISF absorption, and biomarker recovery. The impact of washing to remove interference and chitosan modification on IgG sampling is assessed ex vivo, while in vivo studies measure ISF uptake and skin response. Both formulations exhibit sufficient mechanical strength for insertion, with washed patches maintaining tip sharpness despite minor shrinkage. Formulated HFMAPs absorb over 5 µL of ISF and facilitate quantifiable IgG detection ex vivo, whereas chitosan‐modified patches reduce IgG recovery due to biomarker‐hydrogel interactions. In vivo, all formulations absorb over 1.5 µL of ISF within 2 h, obtaining sufficient samples for subsequent analysis. Proteomic study reveale 50 to 200 proteins, with chitosan affecting abundance but not the total number detected. These findings support HFMAPs as a promising tool for non‐invasive transdermal sampling of protein biomarkers, enabling subsequent proteomic analysis.
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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".