Integration of Nanomaterials and Bioluminescence Resonance Energy Transfer Techniques for Sensing Biomolecules
Bibliographic record
Abstract
Bioluminescence resonance energy transfer (BRET) techniques offer a high degree of sensitivity, reliability and ease of use for their application to sensing biomolecules. BRET is a distance dependent, non-radiative energy transfer, which uses a bioluminescent protein to excite an acceptor through the resonance energy transfer. A BRET sensor can quickly detect the change of a target biomolecule quantitatively without an external electromagnetic field, e.g., UV light, which normally can damage tissue. Having been developed quite recently, this technique has evolved rapidly. Here, different bioluminescent proteins have been reviewed. In addition to a multitude of bioluminescent proteins, this manuscript focuses on the recent development of BRET sensors by utilizing quantum dots. The special size-dependent properties of quantum dots have made the BRET sensing technique attractive for the real-time monitoring of the changes of target molecules and bioimaging in vivo. This review offers a look into the basis of the technique, donor/acceptor pairs, experimental applications and prospects.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".