Nanostructured biosensor for tear glucose detection based on bioluminescence resonance energy transfer (BRET) mechanism.
Bibliographic record
Abstract
Bioluminescence Resonance Energy Transfer (BRET), a sensitive, non-destructive and self-illuminated method, has been now commonly used to test protein interactions. Here, we describe a BRET sensor for non-invasively detecting glucose molecules. The sensor is made by the bioconjugation of quantum dots and recombinant protein. The recombinant protein contains the bacterial glucose binding protein (GBP) and a bioluminescent protein, Renilla luciferase (Rluc), used as the donor with the emission peak at 470 nm, which is able to excite the acceptor of BRET sensor made of cadmium tellurium quantum dots ( CdTe QDs) with the emission peak at 570 nm. The distance between the BRET pair depends on the shape of GBP. In the presence of glucose, the conformational change of GBP leads to the enhanced BRET phenomenon due to the short distance between the BRET pair. To optimal the BRET sensor, the ratios of the acceptor (QDs) to the donor (GBP-Rluc), the bioconjugation process, and the performance of recombinant protein have been thoroughly investigated. The results indicate a linear relationship between luminescence intensity ratio and the concentration of glucose in the two ranges, from 0 mM to 0.1 mM and from 0.2mM to 0.8mM, respectively. The limit of detection (LOD) is calculated as15 nM. Meanwhile, tear glucose of animal model were measured by this designed sensor. This study demonstrates a proof-of-concept of a non-invasive measurement system for monitoring glucose.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".