Proteolytic Assays on Quantum-Dot-Modified Paper Substrates Using Simple Optical Readout Platforms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Paper-based assays are a promising diagnostic format for point-of-care applications, field deployment, and other low-resource settings. To date, the majority of efforts to integrate nanomaterials with paper-based assays have utilized gold nanoparticles. Here, we show that semiconductor quantum dots (QDs), in combination with Förster resonance energy transfer (FRET), are also suitable nanomaterials for developing paper-based assays. Paper fibers were chemically modified with thiol ligands to immobilize CdSeS/ZnS QDs, the QDs were self-assembled with dye-labeled peptides to generate efficient FRET, and steady-state and fluorescence lifetime imaging microscopy (FLIM) were used for characterization. Peptides were selected as substrates for three different proteases and a series of kinetic assays for proteolytic activity was carried out, including multiplexed assays and pro-enzyme activation assays. Quantitative results were obtained within 5-60 min at levels as low as 1-2 nM of protease. These assays were possible using simple optical readout platforms that did not negate the low cost, ease of use, and overall accessibility advantages of paper-based assays. A violet light-emitting diode (LED) excitation source and color imaging with either a digital camera, consumer webcam, or smartphone camera were sufficient for analysis on the basis of a red/green color intensity ratio. At most, a universal serial bus (USB) connection to a computer was required and the instrumentation cost orders of magnitude less than that typically utilized for in vitro bioanalyses with QDs. This work demonstrates that QDs are valuable probes for developing a new generation of paper-based diagnostics.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it