Proteolytic Assays on Quantum-Dot-Modified Paper Substrates Using Simple Optical Readout Platforms
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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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".