Development of a novel microfluidic immunoassay for the detection of Helicobacter pylori infection
Bibliographic record
Abstract
The miniaturization of laboratory processes offers substantial advantages over traditional techniques in terms of cost, speed, and potential for multistage automation. To date, only a few studies have reported successful microfluidics-based immunoassays, most of which rely on fluorescence detection technologies. The goal of this study was to develop a poly(dimethylsiloxane) microfluidics-based immunoassay methodology and a versatile colorimetric quantification scheme for the detection of visual colour changes resulting from immune reactions in microchannels. The novel immunoassay technique was applied towards the detection of Helicobacter pylori infection using 20 human serum samples of known infection status, and results compared with conventional nitrocellulose membrane-based dot-ELISA. The microchannel immunoassay reliably detected H. pylori antigens in quantities on the order of 10 ng, which provides a sensitivity of detection comparable to conventional dot-blot assays. Sensitivity was 100%, specificity was 90%, positive predictive value 91%, and negative predictive value 100%, with an overall accuracy of 95%. The software developed generated results that were consistent with visual observations and by automatically taking into account background intensity changes, the software minimized subjectivity. Volumes of solutions used were 100-fold less compared with conventional immunoassays. Miniaturization of the ELISA using this technique provides a means for the accurate diagnosis of microbial infection while minimizing waste production.
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How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".