Single-step bioassays in serum and whole blood with a smartphone, quantum dots and paper-in-PDMS chips
Bibliographic record
Abstract
The development of nanoparticle-based bioassays is an active and promising area of research, where point-of-care (POC) diagnostics are one of many prospective applications. Unfortunately, the majority of nanoparticle-based assays that have been developed to date have failed to address two important considerations for POC applications: use of instrumentation amenable to POC settings, and measurement of analytes in biological sample matrices such as serum and whole blood. To address these considerations, we present design criteria and demonstrate proof-of-concept for a semiconductor quantum dot (QD)-based assay format that utilizes smartphone readout for the single-step, Förster resonance energy transfer (FRET)-based detection of hydrolase activity in serum and whole blood, using thrombin as a model analyte. Important design criteria for assay development included (i) the size and emission wavelength of the QDs, which had to balance brightness for smartphone imaging, optical transmission through blood samples, and FRET efficiency for signaling; (ii) the wavelength of a light-emitting diode (LED) excitation source, which had to balance transmission through blood and the efficiency of excitation of QDs; and (iii) the use of an array of paper-in-polydimethylsiloxane (PDMS)-on-glass sample chips to reproducibly limit the optical path length through blood to ca. 250 μm and permit multiplexing. Ultimately, CdSe/CdS/ZnS QDs with peak emission at 630 nm were conjugated with Alexa Fluor 647-labeled peptide substrates for thrombin and immobilized on paper test strips inside the sample cells. This FRET system was sensitive to thrombin activity, where the recovery of QD emission with hydrolytic loss of FRET permitted kinetic assays in buffer, serum and whole blood. Quantitative results were obtained in less than 30 min with a limit of detection 18 NIH units mL(-1) of activity in 12 μL of whole blood. Proof-of-concept for a competitive binding assay was also demonstrated with the same platform. Overall, this work demonstrates that the integration of QDs with smartphones and other consumer electronics can potentiate bioassays that are highly amenable to future point-of-care diagnostic applications.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".