Development and evaluation of an immunoassay for the quantification of N-acetylneuraminic acid (Neu5Ac) in foods and biosamples
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
N -acetylneuraminic acid is an active ingredient in tonic foods and an important additive in foods and biopharmaceuticals. To address the limitations of existing methods of N -acetylneuraminic acid quantification, we developed an immunoassay based on antibodies induced in hens using artificial antigen, showing high sensitivity and specificity with no cross-reactivity with eight N -acetylneuraminic acid analogues. An IgY-based indirect competitive enzyme-linked immunosorbent assay showed a detection range of 1.14 to 70.08 ng/mL and a limit of detection of 0.57 ng/mL. In spiked samples, recoveries by the indirect competitive enzyme-linked immunosorbent assay ranged from 74.05% to 110.87% compared with HPLC (73.01% to 108.8%). Consistency between the indirect competitive enzyme-linked immunosorbent assay and HPLC was satisfactory (R 2 = 0.9736), demonstrating this established immunoassay as a rapid and reliable approach for N -acetylneuraminic acid analysis. The assay described in this study provides an important method for the screening of N -acetylneuraminic acid in biological samples and foodstuffs. • An IgY-based ic-ELISA for the analysis of Neu5Ac was developed for the first time. • This immunoassay exhibited no significant cross-reactivity with other Neu5Ac analogs. • The linear range was 1.14–70.08 ng/mL with recoveries ranging from 74.05% to 110.87%. • The assay detected Neu5Ac in food and clinical samples. • Immunoassay is a promising approach to analyse SA for broad biomedical purposes.
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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 it