Dextran-Gold Nanoparticle-Based Tablets and Swabs for Colorimetric Detection of Urinary H<sub>2</sub>O<sub>2</sub>
Bibliographic record
Abstract
Diagnosis of oxidative stress is essential to avoiding serious life-threatening situations. Hydrogen peroxide (H 2 O 2 ) is a potential biomarker of oxidative stress. Herein, we introduce a reagent-free nanoscale approach for the colorimetric detection of urinary H 2 O 2 utilizing dextran-gold nanoparticles (dAuNPs). The plasmonic properties of these nanoparticles are central to their function, leveraging their high surface area and tunable optical characteristics for sensitive detection. We transformed the colloidal dAuNPs solution into two formats: as a tablet (dAuNPs-Tablet) or impregnated on a cotton swab (dAuNPs-Swab). The assay generates a hydroxyl radical (•OH) from H 2 O 2 via the Fenton reaction, followed by nanoscale-driven detection of H 2 O 2 using a plasmonic tablet and swab sensors. In the presence of H 2 O 2 in a sample, the red color of the tablet solution or plasmonic swab turns to blue due to salt-induced nanoparticles aggregation. The transition in color is observed due to •OH-assisted degradation of the dextran layer around dAuNPs, leading to the loss of colloidal stability and subsequent aggregation of dAuNPs. Sodium chloride acts as the aggregating agent, enhancing the nanoscale interactions. The detection limit in artificial urine is found to be 50 μM for the tablet sensor and 100 μM for the swab sensor. The plasmonic tablet is more stable as compared to a plasmonic swab which gradually loses stability, after one month, with approximately 40% degradation within three months. Interference studies demonstrate the high selectivity of both platforms for H 2 O 2 detection. Notably, we investigated the H 2 O 2 levels in human urine samples from healthy volunteers (both female and male) before and after green tea consumption. The observed decrease in the H 2 O 2 level in urine postgreen tea consumption suggests a potential role of green tea antioxidants in mitigating oxidative stress. The utilization of nanoprobes in our research not only enhances our understanding of oxidative stress dynamics but also drives advancements in point-of-care detection platforms, offering enhanced portability and ease of use of nanoprobes. These platforms open exciting avenues in healthcare diagnosis.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| 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".