A Paper-Based Colorimetric Aptasensor for the Detection of Gentamicin
Bibliographic record
Abstract
Antibiotics are classes of antimicrobial substances that are administered widely in the field of veterinary science to promote animal health and feed efficiency. Cattle-administered antibiotics hold a risk of passing active residues to milk, during the milking process. This becomes a public health concern as these residues can cause severe allergic reactions to sensitive groups and considerable economic losses to the farmer. Hence, to ensure that the produced milk is safe to consume and adheres to permissible limits, an on-farm quick and reliable test is essential. This study illustrates the design and development of a microfluidic paper biosensor as a proof-of-concept detection system for gentamicin in milk. Localized surface plasmon resonance (LSPR) properties of gold nanoparticles have been explored to provide the user a visual feedback on the test, which was also corroborated by RGB analysis performed using Image J. The assay involves the use of a short stretch of single stranded DNA, called aptamer, which is very specific to the gentamicin present in the milk sample. The camera-based LOD for the fabricated paper device for milk samples spiked with gentamicin was calculated to be 300 nM, with a reaction time of 2 min.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".