Characterization and Application of a Histamine Aptamer-Based Biosensor
Bibliographic record
Abstract
Histamine is a biogenic amine, identified as a natural contaminant in food and alcoholic beverages, with high levels eliciting allergy-related conditions.Exposure to allergens can lead to abnormal high levels of histamine in the body that is detectable in blood and urine.A rapid histamine biosensor that can readily detect histamine levels in food, beverages and also be used as a diagnostic tool for allergy-related conditions will be an ideal point of care testing tool that is readily available at low cost.In addition, histamine has been identified as a close-range aggregation pheromone component in bed bug feces.Bed bug infestation has been on the rise in developed countries mainly in hotel, homes, shelters and school settings.A low cost, portable biosensor that readily detects bed bugs in these settings is in demand.Nano-aptamer based biosensors have previously been used to detect wide range of targets in medical diagnosis, agriculture, and industry.In this study, selected histaminebinding aptamers (Hist_2, Hist_23, Hist_1min, Hist_2min, and Hist_23min) were characterized in solution with target histamine by a colorimetric binding assay and microscale thermophoresis.A head-to-head comparison of binding affinity and specificity for these group of aptamers were conducted, and the aptamer candidates best suited for biosensor application was selected.Aptamers Hist_2, Hist_23, and Hist_23min were selected out of the five aptamers due to their lowest LODs (400 nM, 600 nM, and 300 nM respectively) and high specificity towards target histamine through colorimetric binding studies.Further binding studies of these selected aptamers were conducted using microscale thermophoresis, and aptamers Hist_2 and Hist_23 were selected due to the observed specificity and strong affinity with KD values of 1.29 µM and 68 nM, respectively towards the target.The aptamer candidate was further applied on an aptamer-based lateral III flow assay biosensor, that proved to be highly sensitivity with Hist_23 aptamer giving an apparent LOD of less than 10 nM.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".