Paper-based biodetection using luminescent nanoparticles
Bibliographic record
Abstract
Point-of-care and in-field technologies for rapid, sensitive and selective detection of molecular biomarkers have attracted much interest. Rugged bioassay technology capable of fast detection of markers for pathogens and genetic diseases would in particular impact the quality of health care in the developing world, but would also make possible more extensive screening in developed countries to tackle problems such as those associated with water and food quality, and tracking of infectious organisms in hospitals and clinics. Literature trends indicate an increasing interest in the use of nanomaterials, and in particular luminescent nanoparticles, for assay development. These materials may offer attributes for development of assays and sensors that could achieve improvements in analytical figures of merit, and provide practical advantages in sensitivity and stability. There is opportunity for cost-efficiency and technical simplicity by implementation of luminescent nanomaterials as the basis for transduction technology, when combined with the use of paper substrates, and the ubiquitous availability of cell phone cameras and associated infrastructure for optical detection and transmission of results. Luminescent nanoparticles have been described for a broad range of bioanalytical targets including small molecules, oligonucleotides, peptides, proteins, saccharides and whole cells (e.g., cancer diagnostics). The luminescent nanomaterials that are described herein for paper-based bioassays include metal nanoparticles, quantum dots and lanthanide-doped nanocrystals. These nanomaterials often have broad and strong absorption and narrow emission bands that improve opportunity for multiplexed analysis, and can be designed to provide emission at wavelengths that are efficiently processed by conventional digital cameras. Luminescent nanoparticles can be embedded in paper substrates that are designed to direct fluid flow, and the resulting combination of technologies can offer competitive analytical performance at relatively low cost.
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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) | 0.000 | 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) | 0.002 | 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".