Harnessing a Nanostructured Fluorescence Energy Transfer Sensor for Quick Detection of Extremely Small Amounts of Glucose
Bibliographic record
Abstract
Fluorescence technique is one of the major solutions for achieving the continuous and noninvasive glucose sensor for diabetes. In this article, a highly sensitive nanostructured sensor is developed to detect extremely small amounts of aqueous glucose by applying fluorescence energy transfer (FRET). A one-pot method is applied to produce the dextran-fluorescein isothiocyanate (FITC)-conjugating mesoporous silica nanoparticles (MSNs), which afterward interact with the tetramethylrhodamine isothiocyanate (TRITC)-labeled concanavalin A (Con A) to form the FRET nanoparticles (FITC-dextran-Con A-TRITC@MSNs). The nanostructured glucose sensor is then formed via the self-assembly of the FRET nanoparticles on a transparent, flexible, and biocompatible substrate, e.g., poly(dimethylsiloxane). Our results indicate the diameter of the MSNs is 60 ± 5 nm. The difference in the images before and after adding 20 μl of glucose (0.10 mmol/liter) on the FRET sensor can be detected in less than 2 min by the laser confocal laser scanning microscope. The correlation between the ratio of fluorescence intensity, I(donor)/I(acceptor), of the FRET sensor and the concentration of aqueous glucose in the range of 0.04-4 mmol/liter has been investigated; a linear relationship is found. Furthermore, the durability of the nanostructured FRET sensor is evaluated for 5 days. In addition, the recorded images can be converted to digital images by obtaining the pixels from the resulting matrix using Matlab image processing functions. We have also studied the in vitro cytotoxicity of the device. The nanostructured FRET sensor may provide an alternative method to help patients manage the disease continuously.
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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 | 0.001 | 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".