Development of a DNA aptamer that binds specifically to group A <i>Streptococcus</i> serotype M3
Bibliographic record
Abstract
Group A streptococcus (GAS) is an important Gram-positive pathogen that causes various human diseases ranging from peripheral lesions to invasive infections. The M protein is one of the main virulence factors present on the cell surface and is associated with invasive GAS infections. Compared with other M types, serotype M3 has a predominant role in lethal infections and demonstrates epidemic behaviors, including streptococcal toxic shock syndrome, bacteremia, and necrotizing fasciitis. Traditional methods for M typing are time-consuming, tedious, contradictory, and generally restricted to reference laboratories. Therefore, development of a new M-typing technique is needed. Aptamers with the ability to detect their target with a high degree of accuracy and specificity can be ideal candidates for specific M-typing of Streptococcus pyogenes. In this study DNA aptamers with a high binding affinity towards S. pyogenes serotype M3 were selected through 12 iterative rounds of the Systematic Evolution of Ligands by EXponential (SELEX) enrichment procedure using live cells as a target. We monitored the progress of the SELEX procedure by flow cytometry analysis. Of several aptamer sequences analyzed, 12L18A showed the highest binding efficiency towards S. pyogenes type M3, with an apparent dissociation constant (Kd) of 7.47 ± 1.72 pmol/L being the lowest. Therefore the isolated aptamer can be used in any tool, such as a biosensor, for the detection of S. pyogenes and can be used in the development of a novel M-typing system.
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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".