Development of a Nanoparticle-Labeled Microfluidic Immunoassay for Detection of Pathogenic Microorganisms
Bibliographic record
Abstract
The light-scattering properties of submicroscopic metal particles ranging from 40 to 120 nm in diameter have recently been investigated. These particles scatter incident white light to generate monochromatic light, which can be seen either by the naked eye or by dark-field microscopy. The nanoparticles are well suited for detection in microchannel-based immunoassays. The goal of the present study was to detect Helicobacter pylori- and Escherichia coli O157:H7-specific antigens with biotinylated polyclonal antibodies. Gold particles (diameter, 80 nm) functionalized with a secondary antibiotin antibody were then used as the readout. A dark-field stereomicroscope was used for particle visualization in poly(dimethylsiloxane) microchannels. A colorimetric quantification scheme was developed for the detection of the visual color changes resulting from immune reactions in the microchannels. The microchannel immunoassays reliably detected H. pylori and E. coli O157:H7 antigens in quantities on the order of 10 ng, which provides a sensitivity of detection comparable to those of conventional dot blot assays. In addition, the nanoparticles within the microchannels can be stored for at least 8 months without a loss of signal intensity. This strategy provides a means for the detection of nanoparticles in microchannels without the use of sophisticated equipment. In addition, the approach has the potential for use for further miniaturization of immunoassays and can be used for long-term archiving of immunoassays.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".