Selection of Aptamers against Live Bacterial Cells
Bibliographic record
Abstract
Single-stranded DNA or RNA aptamer molecules have usually been selected against purified target molecules. To eliminate the need of purifying target molecules on the cell surface, we have developed a selection technique using live bacterial cells in suspension as targets, to select for ssDNA aptamers specific to cell surface molecules. Lactobacillus acidophilus cells were chosen to demonstrate proof of principle based on their high abundance of surface molecules (potential targets). Aptamer pools obtained after 6-8 rounds of selection demonstrated high affinity for and selective binding with L. acidophilus cells when tested via flow cytometry, microscopy, and fluorescence measurements. Out of 27 aptamers that were cloned and sequenced, one sequence, hemag1P, was found to bind to L. acidophilus much more strongly and specifically than other cells tested. This aptamer was predicted to have a tight hairpin secondary structure. On average, an estimated 164 +/- 47 aptamer molecules were bound to a target cell with an apparent K d of 13 +/- 3 nM. A likely putative molecular target of hemag1P is the S-layer protein on the cell surface.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".