Functional nucleic acids for detecting bacteria
Bibliographic record
Abstract
Abstract Bacterial infection represents one of the leading causes of disease and death, and as such, bacterial detection is an important step in managing infectious diseases. The current protocol requires growing cell cultures, which can take several days. Fast detection of low copies of bacterial cells has thus posed an analytical challenge. Among the new strategies developed to achieve this goal, functional nucleic acids (FNAs) have emerged to be a promising platform. FNAs include DNAzymes, aptamers, and aptazymes, all of which can recognize analytes other than complementary nucleic acids. FNAs are obtained using a combinatorial biology technique called systematic evolution of ligands by exponential enrichment (SELEX). FNAs have been isolated against not only purified proteins and surface markers from bacterial cells but also whole cells. A diverse range of signaling mechanisms including fluorescence, color, and electrochemistry-based detection has been reported. Although the majority of current sensors cannot achieve single-cell sensitivity, with improved combinatorial selection techniques and the incorporation of nanomaterials to realize multivalent binding and signal amplification, FNAs represent a feasible solution for bacterial detection.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".