DNA Aptamers Binding to Multiple Prevalent M-Types of <i>Streptococcus pyogenes</i>
Bibliographic record
Abstract
This paper describes the selection of high affinity DNA aptamers binding to multiple M-types of the pathogenic species Streptococcus pyogenes (Group A Streptococcus or GAS). Unlike common aptamer selection techniques that use purified molecules of a monoclonal cell population as targets, this work has achieved the selection of aptamers against the various M-types of S. pyogenes. Cell mixtures containing equal numbers of the 10 most prevalent S. pyogenes M-types were incubated with 80-nucleotide DNA libraries, centrifuged, and washed to separate cell-bound from unbound DNA sequences. The DNA bound to the cells was amplified using the polymerase chain reaction, and the amplicons were tested for their binding to the target cells. The amplicons were also used as new DNA libraries for subsequent rounds of selection. Cloning, sequencing, and subsequent analysis of selected aptamers showed that they bind preferentially to GAS over other common and related bacteria. Resultant DNA aptamers showed strong and preferential binding to GAS, including the 10 most prevalent GAS M-types and another 10 minor M-types tested. Estimated K(d) values were in the range of 4 to 86 nM. Two aptamers, 20A24P and 15A3P (with estimated binding dissociation constants of 9 and 10 nM, respectively), are particularly promising. These aptamers could potentially be used to improve the detection of GAS, a pathogen that is the causative agent of many infectious diseases, most notably strep throat.
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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".