Lateral flow assays for Ochratoxin A using metal nanoparticles: comparison of “adsorption–desorption” approach to linkage inversion assembled nano-aptasensors (LIANA)
Bibliographic record
Abstract
Nano-aptamer probes were prepared and used in lateral flow colorimetric assays for the detection of Ochratoxin A (OTA). In this study, two approaches were examined using 5'-biotin-modified OTA aptamers and silver or gold nanoparticles (AgNP or AuNP). The first method used an "adsorption-desorption" approach wherein aptamers were adsorbed onto the metal nanoparticle surface. Upon the addition of OTA, the aptamer binds specifically to the target, releasing the NPs. The above solutions were applied on a lateral flow assay (LFA) and a detection limit of 6.3 nM was achieved with both metal nanoparticles. The second method used a labelled approach based on Linkage Inversion Assembled Nano-Aptasensors (LIANAs) using a DNA linker containing a 5'-5' linkage inversion (5'-5' linker) to assemble biotinylated aptamer-functionalized metal nanoparticles. In the presence of target, OTA specifically binds with its aptamer leading to release of the linker and disassembly of LIANA aggregates into dispersed nanoparticles. The same solutions were applied in LFA format and the lowest detection limit of 0.63 nM was achieved. The results indicated that the LIANA-based LFA strips were more sensitive than the "adsoprtion-desorption" LFAs. Both lateral flow assays are inexpensive, simple, and rapid to perform and produces results visible to the naked-eye.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".