Hydrogen-bonded organic framework-based signal amplification strategy combined with high-specificity sandwich immune response system for dual-mode ultrasensitive detection of gliadin allergen
Bibliographic record
Abstract
Gliadin (Gli) poses a serious health risk to individuals with celiac disease and gluten sensitivity, prompting the development of specific and ultrasensitive method for Gli detection. Herein, we integrated a hydrogen-bonded organic framework (HOF)-based signal amplification strategy with a highly specific sandwich immune response system to exploit an ELISA and electrochemical dual-mode immunosensor for ultrasensitive Gli detection. Specifically, we have successfully synthesized an iron porphyrin-based HOF at room temperature, which exhibits high peroxidase-like activity and exceptional electrochemical performance. Building on this, a dual-mode sandwich immune-sensing platform for ultrasensitive and specific Gli detection was successfully developed by integrating Gli-specific aptamers and antibodies, achieving detection limits of 6.98 ng/mL (ELISA mode) and 41 pg/mL (electrochemical mode). Notably, the developed ELISA sensor allows for qualitative analysis by the naked eye, with a visual limit of detection (LOD) of 10 ng/mL. The established dual-mode detection method demonstrates strong consistency with commercially available ELISA kits, offering a novel perspective for the ultrasensitive and accurate detection of Gli in gluten-free and cross-contaminated foods.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".