Aptamer-Based Impedimetric Sensor for Bacterial Typing
Bibliographic record
Abstract
The development of an aptamer-based impedimetric sensor for typing of bacteria (AIST-B) is presented. Highly specific DNA aptamers to Salmonella enteritidis were selected via Cell-SELEX technique. Twelve rounds of selection were performed; each comprises a positive selection step against S. enteritidis and a negative selection step against a mixture of related pathogens, including Salmonella typhimurium, Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Citrobacter freundii, to ensure the species-specificity of the selected aptamers. After sequencing of the pool showing the highest binding affinity to S. enteritidis, a DNA sequence of high affinity to the bacteria was integrated into an impedimetric sensor via self-assembly onto a gold nanoparticles-modified screen-printed carbon electrode (GNPs-SPCE). Remarkably, this aptasensor is highly selective and can successfully detect S. enteritidis down to 600 CFU mL(-1) (equivalent to 18 CFU in 30 μL assay volume) in 10 min and distinguish it from other Salmonella species, including S. typhimurium and S. choleraesuis. This report is envisaged to open a new venue for the aptamer-based typing of a variety of microorganisms using a rapid, economic, and label-free electrochemical platform.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".