Aptamer Based Aflatoxin B1 Detection System Using Graphene Oxide Quencher
Bibliographic record
Abstract
Aflatoxin B1 is one of the most common mycotoxins, toxic substances produced by fungi, and produced by Aspergillus flavus. It is classified as one of International Agency for Research on Cancer (IARC) Group 1 carcinogens and only few nanograms of aflatoxin B1 may cause liver cancer and stunted growth by permeation through skin. There are conventional diagnosis methods such as high-performance liquid chromatography (HPLC) and immunoassays. These methods provide reliable quantification results but require well-trained professionals, expensive instruments and materials, and they are time consuming. In this research, nanomaterials, graphene oxide and aptamer, are used to overcome these weaknesses. Aptamer is a single-strand oligonucleotide, which binds to a target specifically and it is easy to synthesize. Aptamer specific for aflatoxin B1 is used as a probe and it is modified with a fluorescence dye, FAM on the 5' end. If the aptamer binds to an aflatoxin B1 molecule, the aptamer and aflatoxin B1 complex becomes easy to interact with graphene oxide, a well-known fluorescence quencher. It results in the decrease of the fluorescence intensity, and the fluorescence intensity increases with the lack of aflatoxin B1, on the other hand.
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) | 0.002 | 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".