Bibliographic record
Abstract
Lateral-flow immunochromatographic assays are low-cost, simple-to-use, rapid tests for point-of-care screening of infectious diseases, drugs of abuse, and pregnancy. However, lateral flow assays are generally not quantitative, give a yes/no answer, and lack multiplexing. Threads have recently been proposed as a support for transporting and mixing liquids in lateral-flow immunochromatographic assays, but their use for quantitative high-sensitivity immunoassays has yet to be demonstrated. Here, we introduce the immunochromatographic assay on thread (ICAT) in a cartridge format that is suitable for multiplexing. The ICAT is a sandwich assay performed on a cotton thread knotted to a nylon fiber bundle, both of which are precoated with recognition antibodies against one target analyte. Upon sample application, the assay results become visible to the eye within a few minutes and are quantified using a flatbed scanner. Assay conditions were optimized, the binding curves for C-reactive protein (CRP) in buffer and diluted serum were established and a limit of detection of 377 pM was obtained. The possibility of multiplexing was demonstrated using three knotted threads coated with antibodies against CRP, osteopontin, and leptin proteins. The performance of the ICAT was compared with that of the paper-based and conventional assays. The results suggest that thread is a suitable support for making low-cost, sensitive, simple-to-use, and multiplexed diagnostic tests.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".