Simplified gluten detection approach in the presence of a black hole quencher (BHQ)
Bibliographic record
Abstract
Optical biosensors based in the fluorescence resonance energy transfer (FRET) phenomenon require of fluorophores. The use of fluorophores requires of a complex optical system that often leads to signal loss. Thus, a new approach based in a Black Hole Quencher (BHQ) that further simplifies the biosensor is sought. This approach potentially increases the sensitivity and accuracy. For the development of this approach, the gliadin conjugate with BHQ-10 must be established first. In the present work, we used the physical phenomenon of FRET to study the gliadin conjugation with BHQ-10 molecule. We performed an experiment with a fluorophore (6-Carboxyfluorescein or 6-FAM) labeled aptamer and non-covalently attached to graphene oxide (GO). The gliadin from gluten was conjugated with BHQ using two different cross-linking reagents present in BHQ: BHQ-10 succinimidyl ester and BHQ-10 carboxylic acid. BHQ-10 with carboxylic acid as the cross-linking reagent demonstrated to be an efficient reaction for gliadin conjugation with BHQ-10. The average quenching efficiency obtained was 62% in comparison to the gliadin as a control experiment. This sets the basis for the breakthrough development of a simplified gluten detection biosensor based on absorbance measurements instead of different wavelength ranges as fluorophores do. The aim is to develop a point-of-care microfluidic system that measures gliadin from gluten in a sensitive, accurate and cost-efficient manner.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".