Bibliographic record
Abstract
Concentric Förster resonance energy transfer (cFRET) configurations based on semiconductor quantum dots (QDs) are promising probes for biological sensing because they offer multiplexing capability in a single vector with robust ratiometric detection by exploiting a network of FRET pathways. To expand the scope and utility of cFRET probes, it is necessary to develop and validate cFRET imaging methodology. In this technical note, we present such a methodology using a protease-sensitive cFRET configuration that comprises a green-emitting QD, Alexa Fluor 555 (A555), and Alexa Fluor 647 (A647). Photoluminescence (PL) images were acquired with three filter-based emission channels to permit measurement of A555/QD and A647/QD PL ratios. With reference to calibration samples, these PL ratios were used to calculate quantitative progress curves for proteolytic activity in regions of interest in the acquired images. Importantly, the imaging methodology reproduces quantitative results obtained with a monochromator-based fluorescence plate reader. Spatiotemporal resolution is demonstrated by tracking the activity of two prototypical proteases, trypsin and chymotrypsin, as they diffuse down the length of a capillary. This methodology is expected to enable the future use of cFRET probes for cellular sensing and other imaging assays.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".