Development of a colloidal gold immunochromatographic strip assay for simple and fast detection of human α-lactalbumin in genetically modified cow milk
Bibliographic record
Abstract
The qualitative and quantitative declaration of food ingredients is important to consumers, especially for genetically modified food as it experiences a rapid increase in sales. In this study, we designed an accurate and rapid detection system using colloidal gold immunochromatographic strip assay (GICA) methods to detect genetically modified cow milk. First, we prepared 2 monoclonal antibodies for human α-lactalbumin (α-LA) and measured their antibody titers; the one with the higher titer was used for further experiments. Then, we found the optimal pH value and protein amount of GICA for detection of pure milk samples. The developed strips successfully detected genetically modified cow milk and non-modified cow milk. To determine the sensitivity of GICA, a quantitative ELISA system was used to determine the exact amount of α-LA, and then genetically modified milk was diluted at different rates to test the sensitivity of GICA; the sensitivity was 10 μg/mL. Our results demonstrated that the applied method was effective to detect human α-LA in cow milk.
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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 | 0.001 | 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".