A simple, rapid one‐step <scp>ELISA</scp> using antibody–antibody complex
Bibliographic record
Abstract
The enzyme-linked immunosorbent assay (ELISA) is one of the most frequently employed assays for clinical diagnostic testing and biological research. However, its time-consuming operation is a major drawback. The present work aims to establish a one-step ELISA method through the preparation of a primary antibody (Ab)-secondary Ab complex (Ab-Ab complex) in hopes of realizing more sensitive and faster detection of the trace amount of antigen (Ag). By controlling the mole ratio of the primary Ab to the secondary Ab, one-step ELISA can be successfully achieved. Compared with the traditional ELISA, the one-step ELISA could not only improve the detection sensitivity to 1 ng mL(-1) , but also reduce the operating time by 30%. Moreover, the signal intensity can be controlled by adjusting the ratio of the secondary Ab in the complex or by changing the color development time. This technique is further optimized to detect trace amounts of proteins adsorbed onto poly(N-vinylpyrrolidone) (PVP)-modified silicon surfaces (Si-PVP), and the results are close to the radiolabeling method. It is concluded that the simple one-step ELISA can be used for the rapid detection of trace amount of protein. The method holds promise for the clinical detection of trace Ag in solution and on low-adsorption material surfaces.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".